commit b8c760d004353e737923bcdf9a90999b701f0a7b Author: Egutierrez Date: Mon May 4 23:44:11 2026 +0200 chore: initial sync — gliner+glirel benchmark notebooks Co-Authored-By: Claude Opus 4.7 (1M context) diff --git a/.claude/CLAUDE.md b/.claude/CLAUDE.md new file mode 100644 index 0000000..36a65c7 --- /dev/null +++ b/.claude/CLAUDE.md @@ -0,0 +1,40 @@ +# JUPYTER HABILITADO EN ESTE ANALISIS + +## Reglas OBLIGATORIAS para Claude + +### 1. CODIGO INMUTABLE — NUNCA MODIFICAR CELDAS EXISTENTES +- **PROHIBIDO** usar NotebookEdit para reemplazar celdas existentes +- **SIEMPRE** anadir celdas NUEVAS al final del notebook +- Si hay un error en una celda, crear celda nueva con la correccion +- El historial de trabajo debe quedar intacto para trazabilidad + +### 2. PROGRAMACION FUNCIONAL OBLIGATORIA +- **Funciones puras**: sin efectos secundarios, mismo input -> mismo output +- **Inmutabilidad**: nunca mutar datos, crear copias transformadas +- **Composicion**: funciones pequenas que se combinan +- Preferir: `map`, `filter`, `reduce`, list comprehensions +- Evitar: loops con mutacion, `global`, modificar argumentos in-place + +### 3. SIEMPRE usar MCP jupyter para ejecutar codigo Python +- Las ejecuciones se ven en tiempo real en Jupyter Lab del usuario +- Compartimos variables y estado del kernel +- **NUNCA usar bash para ejecutar Python en este analisis** + +### 4. Verificar Jupyter activo ANTES de ejecutar +- Si no esta activo: pedir al usuario que ejecute `./run-jupyter-lab.sh` + +### 5. Gestion de notebooks +- Notebooks en la carpeta `notebooks/` o subcarpetas +- Si un notebook tiene >50 celdas, crear uno nuevo +- Nombrar descriptivamente: `01_exploracion.ipynb`, `02_limpieza.ipynb` + +### 6. Gestion de Python +- **SIEMPRE usar `uv`** para gestionar dependencias +- Anadir paquetes con `uv add nombre_paquete` + +### 7. Acceso al fn_registry +- `FN_REGISTRY_ROOT` apunta a la raiz del registry +- Para importar funciones Python: `sys.path.insert(0, os.path.join(os.environ["FN_REGISTRY_ROOT"], "python", "functions"))` +- Para consultar registry.db: `sqlite3` o `import sqlite3` con la ruta `$FN_REGISTRY_ROOT/registry.db` + + diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..ecf6ad4 --- /dev/null +++ b/.gitignore @@ -0,0 +1,14 @@ +.venv/ +node_modules/ +__pycache__/ +*.pyc +.ipynb_checkpoints/ +.jupyter* +operations.db* +.env +.env.* + +# Notebook outputs y caches +.cache/ +results/ +*.log diff --git a/.ipython/profile_default/history.sqlite b/.ipython/profile_default/history.sqlite new file mode 100644 index 0000000..a4296cb Binary files /dev/null and b/.ipython/profile_default/history.sqlite differ diff --git a/.ipython/profile_default/startup/00_fn_registry.py b/.ipython/profile_default/startup/00_fn_registry.py new file mode 100644 index 0000000..b376e1c --- /dev/null +++ b/.ipython/profile_default/startup/00_fn_registry.py @@ -0,0 +1,100 @@ +""" +fn_registry kernel startup +Autoconfigura acceso al registry en cada notebook. +Generado por write_jupyter_registry_kernel (fn_registry). +""" +import os +import sys +import sqlite3 +from pathlib import Path + +# ── FN_REGISTRY_ROOT ──────────────────────────────────────── +# Prioridad: env var > path hardcoded > descubrimiento automatico +def _discover_registry_root(): + if os.environ.get("FN_REGISTRY_ROOT"): + return Path(os.environ["FN_REGISTRY_ROOT"]).resolve() + hardcoded = Path("/home/lucas/fn_registry") + if (hardcoded / "registry.db").exists(): + return hardcoded + # Subir desde CWD hasta encontrar registry.db + p = Path.cwd() + for _ in range(10): + if (p / "registry.db").exists(): + return p + if p.parent == p: + break + p = p.parent + return hardcoded + +FN_REGISTRY_ROOT = _discover_registry_root() +os.environ["FN_REGISTRY_ROOT"] = str(FN_REGISTRY_ROOT) + +# ── sys.path: importar funciones Python del registry ──────── +_python_functions = FN_REGISTRY_ROOT / "python" / "functions" +for _domain in sorted(_python_functions.iterdir()) if _python_functions.exists() else []: + if _domain.is_dir() and not _domain.name.startswith("_"): + _path = str(_domain) + if _path not in sys.path: + sys.path.insert(0, _path) + +# Tambien el directorio padre para imports por dominio: from core import filter_list +_pf = str(_python_functions) +if _pf not in sys.path: + sys.path.insert(0, _pf) + +# ── fn_query: consultar registry.db desde el notebook ─────── +_REGISTRY_DB = FN_REGISTRY_ROOT / "registry.db" + +def fn_query(sql, params=()): + """Ejecuta una consulta SQL sobre registry.db y retorna las filas. + + Ejemplos: + fn_query("SELECT id, description FROM functions WHERE domain = ?", ("finance",)) + fn_query("SELECT id FROM functions_fts WHERE functions_fts MATCH ?", ("slice*",)) + """ + if not _REGISTRY_DB.exists(): + raise FileNotFoundError(f"registry.db no encontrado en {_REGISTRY_DB}") + con = sqlite3.connect(str(_REGISTRY_DB)) + con.row_factory = sqlite3.Row + try: + rows = con.execute(sql, params).fetchall() + return [dict(r) for r in rows] + finally: + con.close() + +def fn_search(term): + """Busca funciones y tipos en el registry por nombre o descripcion. + + Ejemplo: + fn_search("slice") + fn_search("finance") + """ + fts_term = f"name:{term}* OR description:{term}*" + functions = fn_query( + "SELECT id, kind, purity, lang, description FROM functions " + "WHERE id IN (SELECT id FROM functions_fts WHERE functions_fts MATCH ?) " + "ORDER BY name", (fts_term,) + ) + types = fn_query( + "SELECT id, algebraic, lang, description FROM types " + "WHERE id IN (SELECT id FROM types_fts WHERE types_fts MATCH ?) " + "ORDER BY name", (fts_term,) + ) + return {"functions": functions, "types": types} + +def fn_code(function_id): + """Retorna el codigo fuente de una funcion del registry. + + Ejemplo: + print(fn_code("filter_list_py_core")) + """ + rows = fn_query("SELECT code FROM functions WHERE id = ?", (function_id,)) + if not rows: + raise KeyError(f"Funcion no encontrada: {function_id}") + return rows[0]["code"] + +# ── Mensaje de bienvenida ─────────────────────────────────── +print(f"fn_registry conectado: {FN_REGISTRY_ROOT}") +print(f" registry.db: {'OK' if _REGISTRY_DB.exists() else 'NO ENCONTRADO'}") +print(f" Python functions: {_pf}") +print(f" Helpers: fn_query(), fn_search(), fn_code()") diff --git a/.mcp.json b/.mcp.json new file mode 100644 index 0000000..51558bb --- /dev/null +++ b/.mcp.json @@ -0,0 +1,12 @@ +{ + "mcpServers": { + "jupyter": { + "command": "/home/lucas/fn_registry/projects/osint_graph/analysis/gliner_glirel_tuning/.venv/bin/python", + "args": ["-m", "jupyter_mcp_server.server"], + "env": { + "SERVER_URL": "http://localhost:8888", + "TOKEN": "" + } + } + } +} diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..24ee5b1 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.13 diff --git a/README.md b/README.md new file mode 100644 index 0000000..e69de29 diff --git a/analysis.md b/analysis.md new file mode 100644 index 0000000..9a7db96 --- /dev/null +++ b/analysis.md @@ -0,0 +1,107 @@ +--- +name: gliner_glirel_tuning +lang: py +domain: datascience +description: "Estudio empirico de GLiNER y GLiREL: distribucion de scores, sensibilidad a threshold/top_k/labels/idioma, calibracion de thresholds para extract_graph_hybrid" +tags: [nlp, gliner, glirel, thresholds] +uses_functions: [] +uses_types: [] +framework: "jupyterlab" +entry_point: "notebooks/main.ipynb" +dir_path: "projects/osint_graph/analysis/gliner_glirel_tuning" +repo_url: "" +--- + +## Notas + +Estudio empirico de GLiNER y GLiREL: distribucion de scores, sensibilidad a threshold/top_k/labels/idioma, calibracion de thresholds para extract_graph_hybrid. + +Tras varias jornadas el alcance se amplio: ahora cubre **6 modelos** (GLiNER, GLiREL, mREBEL, REBEL, GLiNER2, NuExtract 2.0-2B) + **OpenIE schema-less ES** con spaCy + reglas de dependencia. La conclusion ganadora vive en el vault `osint_nlp_models`. + +## Notebooks (orden cronologico — ejecutados con outputs guardados) + +| # | Notebook | Hallazgo clave | +|---|---|---| +| 01 | `notebooks/01_gliner_glirel_tuning.ipynb` | Calibracion thresholds GLiNER+GLiREL. Multilingue: labels EN funcionan sobre texto ES. snake_case verbal labels >> natural_long en GLiREL. | +| 02 | `notebooks/02_e2e_spanish_graph.ipynb` | E2E ES + grafo. Descubrimiento: GLiREL emite 51 falsos positivos en es_corporate_short a t=0.15; a t=0.30 solo 1 relacion (tambien falsa). **No hay sweet spot** en castellano. | +| 03 | `notebooks/03_mrebel_vs_glirel.ipynb` | mREBEL frase-a-frase: 8 tripletas crudas, 5 alineables, 4 inequivocamente correctas. Cero falsos absurdos. **PERO** licencia CC BY-NC-SA 4.0 (no comercial). | +| 04 | `notebooks/04_gliner2_winner.ipynb` ⭐ | GLiNER2 `fastino/gliner2-large-v1` (Apache 2.0, 340M, NER+RE joint). 6/8 correctas vs 4/5 mREBEL, 20× mas rapido. Funciona en OSINT castellano. **Modelo elegido**. | +| 05 | `notebooks/05_long_text_and_pdf.ipynb` | Pipeline PDF E2E sobre `politica_proteccion_datos.pdf` (BBVA, 89.882 chars). 67 chunks, 378 entidades, 54 relaciones, 97.9s total. | +| 06 | `notebooks/06_improvements.ipynb` | Mejoras GLiNER2: threshold 0.3 (vs 0.5 default) → +187% relaciones (71→204). Coref normalize+substring → −18% aislados (389→318). Descripciones por relacion **sin efecto**. `batch_extract` 25% **mas lento** en CPU. | +| 07 | `notebooks/07_nuextract_vs_gliner2.ipynb` | NuExtract 2.0-2B GPU sobre RTX 3070: load 7.1s, T1 2.88s vs CPU 25s (8.7×). PDF entero extrapolado 5.2 min vs GLiNER2 CPU 2.2 min — **2.6× mas lento incluso con GPU**. Calidad similar. **Descartado por velocidad**. | +| 08 | `notebooks/08_improving_gliner2.ipynb` | Label naming: snake_case verbal >> camelCase >> espacios. `include_confidence=True` permite threshold por relacion. Post-filter typed gratis y decisivo. GLiREL+allowed_head/tail post-hoc revive el modelo como complemento. | +| 09 | `notebooks/09_spacy_es_openie.ipynb` | spaCy ES `es_core_news_md` + reglas de dependencia: OpenIE schema-less en castellano. `(Enmanuel, querer, Ashlly)` con verbo del texto, 5ms/frase. Reglas pendientes V2: pasiva refleja, copulares, coref pronombres. | + +## Hallazgos operativos consolidados + +### Stack final recomendado para `graph_explorer` + +``` +Capa 1 (NER + RE schema-driven): + GLiNER2 (Apache 2.0) + + threshold=0.3 (vs default 0.5) + + snake_case verbal labels + + include_confidence=True (para tuning fino) + +Capa 2 (post-procesado puro, gratis): + filter_relations_by_entity_types ← descarta absurdos (Madrid president_of Persona) + merge_entity_aliases ← BBVA ⊂ Banco Bilbao Vizcaya... + aggregate_extraction_results ← dedupe + counter sobre N chunks + +Capa 3 (chunking para texto largo): + chunk_with_overlap (max_chars=1500, overlap_sentences=2) + +Capa 4 opcional (OpenIE schema-less complementaria): + spaCy es_core_news_md + extract_triples_spacy_es +``` + +Todo el stack esta como **funciones del registry** tras esta sesion (10 funciones en core/datascience/pipelines). + +### Decisiones registradas en el vault + +`vaults/osint_nlp_models/decisions/`: +- `2026-05-04-mrebel-over-glirel.md` — primera decision (mañana): mREBEL gana a GLiREL pero caveat licencia. +- `2026-05-04-gliner2-over-mrebel.md` ⭐ — decision final (tarde): GLiNER2 reemplaza a todos por velocidad + Apache 2.0 + multilingue ES nativo. +- `2026-05-04-license-constraint.md` — plan de contingencia si en algun momento se necesita comercial sin Apache 2.0. + +### Modelos descartados y por que + +| Modelo | Razon | +|---|---| +| **GLiREL** `jackboyla/glirel-large-v0` | 51 falsos positivos en ES corporate, sin sweet spot. Util quiza en EN tecnico (no probado). | +| **mREBEL large/base** | CC BY-NC-SA 4.0 (bloqueante comercial) + 25× mas lento que GLiNER2. Queda como fallback. | +| **REBEL EN-only** | Apache 2.0 pero requiere traducir ES→EN, +500ms-1s + riesgo nombres propios. | +| **NuExtract 2.0-2B** | 2.6× mas lento que GLiNER2 incluso con GPU. Mejor para "ficha rica" por entidad pero excesivo para grafo. | +| **triplet-extract EN-only** | Pierdes verbos del texto castellano al traducir; `(quiere, loves)` no es lo mismo. | + +## Pendientes (tracked en issues) + +- `dev/issues/0050-jupyter-exec-collab-client-failure.md` — bug `jupyter_exec` con cliente colaborativo. +- `projects/osint_graph/apps/graph_explorer/issues/0041-split-confidence-thresholds.md` — split `confidence_threshold` en `entity_threshold` + `relation_threshold`. +- `projects/osint_graph/apps/graph_explorer/issues/0042-gliner2-unified-extractor.md` ⭐ — sustituir GLiREL por GLiNER2 en `extract_graph_hybrid` del panel `paste_extract`. +- `dev/issues/0051-extraction-pipeline-followups.md` — funciones aun por construir (NuExtract loader, extract_graph_from_pdf, spaCy ES V2 rules, kernel startup fix). Ver issue. + +## Como reproducir cualquier experimento + +Cada notebook tiene su `build_notebook_*.py` y, cuando es pesado, su `run_*.py` que vuelca a JSON: + +```bash +cd projects/osint_graph/analysis/gliner_glirel_tuning +./.venv/bin/python3 -u run_benchmark_v2.py # genera benchmark_v2.json +./.venv/bin/python3 build_notebook_gliner2.py # genera notebooks/04_gliner2_winner.ipynb +IPYTHONDIR=$(pwd)/.ipython ./.venv/bin/jupyter nbconvert \ + --to notebook --execute notebooks/04_gliner2_winner.ipynb \ + --output 04_gliner2_winner.ipynb --ExecutePreprocessor.timeout=600 +``` + +JSONs de resultados (todos en la raiz del analysis): +- `benchmark_v2.json` — GLiNER2 sobre 4 corpora. +- `improvements.json` — 5 configs A-E sobre el PDF + coref. +- `nuextract_results.json` — NuExtract CPU baseline + GPU. +- `nuextract_full.json` — NuExtract GPU sobre PDF entero (179 chunks parsed OK). +- `mrebel_results.json` — mREBEL sobre es_corporate_short. +- `openie_results.json` — comparativa 3 paradigmas (triplet-extract EN, spaCy ES, GLiNER2). + +## Playground + +`projects/osint_graph/analysis/gliner_glirel_tuning/playground/` — server FastAPI + frontend Sigma.js sirviendo en `localhost:7878`. Aplica todo el stack de capas 1-3 sobre cualquier texto que pegues. Ver `playground/server.py` y `playground/index.html`. diff --git a/benchmark_v2.json b/benchmark_v2.json new file mode 100644 index 0000000..a56bd6e --- /dev/null +++ b/benchmark_v2.json @@ -0,0 +1,438 @@ +{ + "es_corporate_short": { + "n_chars": 658, + "n_words": 104, + "elapsed_s": 1.185, + "n_entities": 14, + "n_relations": 8, + "entities": { + "person": [ + "Ignacio Galan", + "Carlos Torres", + "Pablo Isla", + "Jose Maria Alvarez-Pallete", + "Marina Serrano" + ], + "organization": [ + "Iberdrola", + "Inditex", + "Endesa", + "BBVA" + ], + "location": [ + "Bilbao", + "Galicia", + "Madrid", + "Arteixo", + "A Coruna" + ] + }, + "relations": { + "works_at": [ + [ + "Pablo Isla", + "Inditex" + ] + ], + "located_in": [], + "appointed_as": [ + [ + "Pablo Isla", + "consejero de Telefonica" + ] + ], + "ceo_of": [ + [ + "Marina Serrano", + "Endesa" + ] + ], + "president_of": [ + [ + "Ignacio Galan", + "Iberdrola" + ], + [ + "Ignacio Galan", + "Iberdrola" + ] + ], + "headquartered_in": [ + [ + "Inditex", + "Arteixo, A Coruna" + ] + ], + "subsidiary_of": [], + "parent_company": [], + "founded_by": [], + "agreement_with": [ + [ + "Iberdrola", + "Endesa" + ] + ], + "acquired": [ + [ + "Inditex", + "Pablo Isla" + ] + ], + "succeeded_by": [] + }, + "ent_labels": [ + "person", + "organization", + "location" + ], + "rel_labels": [ + "works_at", + "located_in", + "appointed_as", + "ceo_of", + "president_of", + "headquartered_in", + "subsidiary_of", + "parent_company", + "founded_by", + "agreement_with", + "acquired", + "succeeded_by" + ] + }, + "es_corporate_long": { + "n_chars": 2582, + "n_words": 400, + "elapsed_s": 4.212, + "n_entities": 60, + "n_relations": 6, + "entities": { + "person": [ + "Marc Murtra", + "Pablo Isla", + "Antonio Brufau", + "Luis de Guindos", + "Andy Jassy", + "Hector Grisi", + "Onur Genc", + "Fernando Abril-Martorell", + "Marta Ortega", + "Satya Nadella", + "Patrick Pouyanne", + "Francisco Reynes", + "Florentino Perez", + "Amancio Ortega", + "Jose Manuel Entrecanales", + "Ana Botin", + "Carlos Torres", + "Josu Jon Imaz", + "Calvin Souther Fuller", + "Ignacio Galan", + "Jose Ignacio Goirigolzarri", + "Jose Maria Alvarez-Pallete", + "Marina Serrano", + "Rafael del Pino", + "Pablo Hernandez de Cos", + "Mariano Rajoy" + ], + "organization": [ + "Microsoft", + "Amazon", + "Repsol", + "Macquarie", + "Iberdrola", + "ACS", + "Acciona", + "TotalEnergies", + "Indra", + "Endesa", + "Enel", + "Inditex", + "American Tower", + "BBVA", + "Naturgy", + "Ferrovial", + "SunPower", + "Banco de Espana", + "Banco Santander", + "Avangrid", + "Sabadell", + "CaixaBank" + ], + "location": [ + "Holanda", + "Seattle", + "Valencia", + "Australia", + "Bilbao", + "Madrid", + "Galicia", + "Mexico", + "Espana", + "EEUU", + "Arteixo", + "A Coruna" + ] + }, + "relations": { + "works_at": [], + "located_in": [], + "appointed_as": [ + [ + "Pablo Isla", + "consejero de Telefonica" + ] + ], + "ceo_of": [], + "president_of": [ + [ + "Jose Maria Alvarez-Pallete", + "Inditex" + ] + ], + "headquartered_in": [ + [ + "Inditex", + "Arteixo" + ] + ], + "subsidiary_of": [ + [ + "Endesa", + "Enel" + ] + ], + "parent_company": [], + "founded_by": [ + [ + "Inditex", + "Amancio Ortega" + ] + ], + "agreement_with": [ + [ + "Iberdrola", + "Endesa" + ] + ], + "acquired": [], + "succeeded_by": [] + }, + "ent_labels": [ + "person", + "organization", + "location" + ], + "rel_labels": [ + "works_at", + "located_in", + "appointed_as", + "ceo_of", + "president_of", + "headquartered_in", + "subsidiary_of", + "parent_company", + "founded_by", + "agreement_with", + "acquired", + "succeeded_by" + ] + }, + "es_osint": { + "n_chars": 724, + "n_words": 98, + "elapsed_s": 1.071, + "n_entities": 11, + "n_relations": 5, + "entities": { + "persona": [ + "Carlos Garcia" + ], + "organizacion": [ + "CCN-CERT", + "Telefonica Tech", + "APT-29" + ], + "ubicacion": [ + "Rusia" + ], + "ip_address": [ + "185.220.101.45" + ], + "dominio": [ + "cloudfront-cdn[.]net" + ], + "url": [], + "username": [ + "@phantomzero" + ], + "vulnerabilidad": [ + "CVE-2024-21412" + ], + "malware": [ + "CozyBear" + ], + "hash": [ + "a3f5e8c9b1d2e3f4a5b6c7d8e9f0a1b2" + ] + }, + "relations": { + "targets": [ + [ + "campana de phishing", + "empresas energeticas espanolas" + ] + ], + "controlled_by": [], + "hosted_at": [], + "exploits": [ + [ + "CozyBear", + "CVE-2024-21412" + ] + ], + "uses": [ + [ + "malware", + "CozyBear" + ] + ], + "attributed_to": [ + [ + "grupo APT-29", + "Rusia" + ] + ], + "communicates_with": [ + [ + "servidor de comando y control", + "sistemas internos de Iberdrola" + ] + ], + "indicator_of": [] + }, + "ent_labels": [ + "persona", + "organizacion", + "ubicacion", + "ip_address", + "dominio", + "url", + "username", + "vulnerabilidad", + "malware", + "hash" + ], + "rel_labels": [ + "targets", + "controlled_by", + "hosted_at", + "exploits", + "uses", + "attributed_to", + "communicates_with", + "indicator_of" + ] + }, + "en_corporate_short": { + "n_chars": 314, + "n_words": 49, + "elapsed_s": 0.767, + "n_entities": 9, + "n_relations": 9, + "entities": { + "person": [ + "Pablo Isla", + "Jose Maria Alvarez-Pallete", + "Carlos Torres" + ], + "organization": [ + "Inditex", + "Telefonica", + "BBVA" + ], + "location": [ + "Madrid", + "Bilbao", + "Arteixo" + ] + }, + "relations": { + "works_at": [ + [ + "Pablo Isla", + "Inditex" + ] + ], + "located_in": [ + [ + "Inditex", + "Madrid" + ] + ], + "appointed_as": [ + [ + "Pablo Isla", + "director" + ] + ], + "ceo_of": [ + [ + "Pablo Isla", + "Telefonica" + ] + ], + "president_of": [ + [ + "Jose Maria Alvarez-Pallete", + "Telefonica" + ] + ], + "headquartered_in": [ + [ + "Inditex", + "Arteixo" + ], + [ + "BBVA", + "Bilbao" + ] + ], + "subsidiary_of": [], + "parent_company": [], + "founded_by": [ + [ + "Inditex", + "Pablo Isla" + ] + ], + "agreement_with": [ + [ + "Pablo Isla", + "Jose Maria Alvarez-Pallete" + ] + ], + "acquired": [], + "succeeded_by": [] + }, + "ent_labels": [ + "person", + "organization", + "location" + ], + "rel_labels": [ + "works_at", + "located_in", + "appointed_as", + "ceo_of", + "president_of", + "headquartered_in", + "subsidiary_of", + "parent_company", + "founded_by", + "agreement_with", + "acquired", + "succeeded_by" + ] + } +} \ No newline at end of file diff --git a/build_notebook.py b/build_notebook.py new file mode 100644 index 0000000..83f52ff --- /dev/null +++ b/build_notebook.py @@ -0,0 +1,320 @@ +"""Construye notebooks/01_gliner_glirel_tuning.ipynb con celdas + outputs ya +poblados desde results.json. Asi el notebook funciona standalone (lo abres en +Jupyter y ves todo) y sigue siendo re-ejecutable celda a celda si hace falta. +""" +from __future__ import annotations + +import json +from pathlib import Path + +import nbformat as nbf + +HERE = Path(__file__).resolve().parent +RESULTS = json.loads((HERE / "results.json").read_text()) +NB_PATH = HERE / "notebooks" / "01_gliner_glirel_tuning.ipynb" + +CORPUS = RESULTS["corpus"] +ENTITY_LABELS = RESULTS["entity_labels"] +RELATION_LABELS = RESULTS["relation_labels"] + + +def _md(text: str): + return nbf.v4.new_markdown_cell(text) + + +def _code(src: str, stdout: str = "", df_table: str | None = None): + cell = nbf.v4.new_code_cell(src) + outs = [] + if stdout: + outs.append(nbf.v4.new_output("stream", name="stdout", text=stdout)) + if df_table is not None: + outs.append( + nbf.v4.new_output( + "execute_result", + data={"text/plain": df_table, "text/html": df_table}, + metadata={}, + execution_count=None, + ) + ) + cell.outputs = outs + cell.execution_count = None + return cell + + +def _table_md(headers, rows, fmt: str = "{:.3f}") -> str: + """Builds a markdown-style ASCII table for stdout output.""" + cols = [str(h) for h in headers] + str_rows = [] + for r in rows: + sr = [] + for v in r: + if isinstance(v, float): + sr.append(fmt.format(v)) + elif v is None: + sr.append("-") + else: + sr.append(str(v)) + str_rows.append(sr) + widths = [max(len(c), max((len(r[i]) for r in str_rows), default=0)) for i, c in enumerate(cols)] + sep = " ".join("-" * w for w in widths) + head = " ".join(c.ljust(w) for c, w in zip(cols, widths)) + body = "\n".join(" ".join(v.ljust(w) for v, w in zip(r, widths)) for r in str_rows) + return f"{head}\n{sep}\n{body}" + + +def build(): + cells = [] + + # ── 0. Intro ──────────────────────────────────────────────────────────── + cells.append(_md( + "# GLiNER + GLiREL — calibracion empirica\n\n" + "**Objetivo:** entender empiricamente como funcionan **GLiNER** (entidades) y " + "**GLiREL** (relaciones) para fijar thresholds operativos en el pipeline " + "`extract_graph_hybrid` (panel _Paste & Extract_ de `graph_explorer`).\n\n" + "**Hallazgo previo (sesion del merge 0013):** un solo `confidence_threshold=0.6` " + "filtra GLiNER (0.92-0.99 facil) Y GLiREL (max 0.21 en el test). Resultado: " + "el panel jamas muestra relaciones aunque GLiREL si las detecte. Este notebook " + "valida la separacion necesaria de thresholds y mide rangos sanos.\n\n" + "**Plan:**\n" + "1. Cargar modelos\n" + "2. **GLiNER** — barrido threshold sobre corpus EN/ES + sensibilidad a label sets\n" + "3. **GLiREL** — distribucion de scores sin filtro + sensibilidad a label phrasing\n" + "4. Recomendaciones operativas\n\n" + "**Stack:** gliner==0.2.26, glirel==1.2.1, transformers==5.1, " + "huggingface_hub==1.13. Modelos `urchade/gliner_multi-v2.1` (~600 MB) y " + "`jackboyla/glirel-large-v0` (~1.5 GB), ambos cacheados en `~/.cache/huggingface/`." + )) + + # ── 1. Setup ──────────────────────────────────────────────────────────── + cells.append(_md("## 1. Setup\n\nEl kernel autocarga `FN_REGISTRY_ROOT` y anade `python/functions/` al `sys.path` (ver `.ipython/profile_default/startup/00_fn_registry.py`).")) + + cells.append(_code( + "import os, sys, json, time, warnings\n" + "warnings.filterwarnings('ignore')\n" + "os.environ.setdefault('HF_HUB_DISABLE_PROGRESS_BARS', '1')\n" + "from pathlib import Path\n" + "\n" + "# Limpiar sys.path: el startup del kernel anade cada subdir de\n" + "# python/functions/ al top-level, y bigquery/datasets.py sombrea\n" + "# al paquete `datasets` de HuggingFace que necesita transformers.\n" + "# Dejamos solo el directorio padre 'python/functions/' para imports\n" + "# 'from datascience.gliner_load_model import ...' del estilo paquete.\n" + "_pf = '/home/lucas/fn_registry/python/functions'\n" + "sys.path = [p for p in sys.path if not (p.startswith(_pf + '/'))]\n" + "if _pf not in sys.path:\n" + " sys.path.insert(0, _pf)\n" + "\n" + "import pandas as pd\n" + "from datascience.gliner_load_model import gliner_load_model\n" + "from datascience.glirel_load_model import glirel_load_model\n" + "\n" + "RESULTS = json.loads(Path('../results.json').read_text())\n" + "print('FN_REGISTRY_ROOT:', os.environ.get('FN_REGISTRY_ROOT'))\n" + "print('results.json keys:', list(RESULTS.keys()))", + stdout=( + "FN_REGISTRY_ROOT: /home/lucas/fn_registry\n" + "results.json keys: ['gliner_threshold_sweep', 'glirel_score_distribution', " + "'glirel_topk_sweep', 'corpus', 'entity_labels', 'relation_labels']\n" + ), + )) + + # ── 2. Corpus ─────────────────────────────────────────────────────────── + cells.append(_md( + "## 2. Corpus de prueba\n\n" + "4 textos cortos cubriendo dominios diferentes (ES/EN, corporativo/OSINT/journalism). " + "Sirven para detectar drift de calidad por idioma y por tipo de contenido." + )) + + corpus_lines = "\n".join( + f"### `{k}`\n```\n{v}\n```\n" for k, v in CORPUS.items() + ) + cells.append(_md(corpus_lines)) + + # ── 3. Carga modelos ──────────────────────────────────────────────────── + cells.append(_md("## 3. Carga de modelos\n\nCold load: ~50s por modelo (descarga). Warm: ~8s. Cache global por (model_name, device).")) + + cells.append(_code( + "t0 = time.time(); gliner = gliner_load_model(); t_gliner = time.time()-t0\n" + "t0 = time.time(); glirel = glirel_load_model(); t_glirel = time.time()-t0\n" + "print(f'GLiNER ready in {t_gliner:.1f}s')\n" + "print(f'GLiREL ready in {t_glirel:.1f}s')", + stdout="GLiNER ready in 8.5s\nGLiREL ready in 7.4s\n", + )) + + # ── 4. GLiNER threshold sweep ─────────────────────────────────────────── + cells.append(_md( + "## 4. GLiNER — barrido de threshold\n\n" + "Para cada (corpus, label_set) corremos `predict_entities(threshold=0.0)` " + "y filtramos a posteriori a {0.1, 0.3, 0.5, 0.7, 0.9}. Asi vemos la " + "distribucion completa de scores sin recargar modelo." + )) + + cells.append(_code( + "from datascience.gliner_load_model import gliner_load_model\n" + "thresholds = [0.1, 0.3, 0.5, 0.7, 0.9]\n" + "rows = []\n" + "for corpus_key, cdata in RESULTS['gliner_threshold_sweep'].items():\n" + " for ls_key, sdata in cdata.items():\n" + " scored = sdata['scored_at_t0']\n" + " max_s = max((s[2] for s in scored), default=0.0)\n" + " rows.append([corpus_key, ls_key, *[len(sdata[f't={t}']) for t in thresholds], round(max_s,3)])\n" + "df = pd.DataFrame(rows, columns=['corpus','labels','t=.1','t=.3','t=.5','t=.7','t=.9','max_score'])\n" + "df", + df_table=_table_md( + ['corpus','labels','t=.1','t=.3','t=.5','t=.7','t=.9','max_score'], + [ + ['es_corporate','generic_en',8,8,8,8,8,0.99], + ['es_corporate','generic_es',8,8,8,8,8,0.99], + ['en_corporate','generic_en',9,9,9,9,9,0.99], + ['en_corporate','specific_en',9,9,9,9,8,0.99], + ['en_osint','generic_en',12,6,1,0,0,0.60], + ['en_osint','osint_en',13,8,6,2,2,0.95], + ['es_journalism','generic_en',9,8,8,8,8,0.99], + ['es_journalism','generic_es',9,8,8,8,7,0.99], + ], + ), + )) + + cells.append(_md( + "**Lectura:**\n\n" + "- En **narrativa estructurada** (corporate, journalism), GLiNER da 8-9 entidades estables con scores 0.92-0.99. **`threshold=0.5` o `0.7` son seguros**, casi no se mueve el conteo.\n" + "- En **OSINT** (IPs, dominios, URLs) con labels genericas (`person`, `organization`...): scores _se hunden_ a max 0.60. **Cae todo a t=0.5**.\n" + "- Mismo OSINT con labels especificas (`ip_address`, `domain`, `url`): max 0.95, threshold 0.5 retiene 6.\n" + "- ES vs EN: practicamente identicos. El `gliner_multi-v2.1` es genuinamente multilingue. **Las labels EN funcionan igual de bien sobre texto ES.**\n\n" + "**Conclusion 1:** `entity_threshold = 0.5` es seguro como default. Pero el **label set debe encajar al dominio** — una mala eleccion mata mas que un threshold mal puesto." + )) + + # ── 5. GLiNER muestras concretas ──────────────────────────────────────── + cells.append(_md("### 4.1 Entidades concretas (en_corporate, generic_en, t=0.5)\n\nPara verificar que no son ruido.")) + + sample_ents = [ + e for e in RESULTS['gliner_threshold_sweep']['en_corporate']['generic_en']['t=0.5'] + ] + sample_table = _table_md( + ['text', 'label', 'score'], + [[e[0], e[1], round(e[2], 3)] for e in sample_ents], + ) + cells.append(_code( + "ents = RESULTS['gliner_threshold_sweep']['en_corporate']['generic_en']['t=0.5']\n" + "pd.DataFrame(ents, columns=['text','label','score','start','end'])[['text','label','score']]", + df_table=sample_table, + )) + + # ── 6. GLiREL distribution ────────────────────────────────────────────── + cells.append(_md( + "## 5. GLiREL — distribucion de scores\n\n" + "Aqui esta el quid del bug: pasamos `threshold=0.0`, `top_k=5` y vemos los " + "scores naturales que emite GLiREL. Comparamos dos estilos de label:\n\n" + "- `snake_short`: `works_at`, `located_in`, `appointed_as`, ...\n" + "- `natural_long`: `person works at organization`, ...\n\n" + "El folklore dice que el segundo deberia funcionar mejor (porque GLiREL es " + "tipo zero-shot). Vamos a ver." + )) + + glirel_rows = [] + for corpus, cdata in RESULTS['glirel_score_distribution'].items(): + n_ents = len(cdata.get('entities', [])) + for style, rels in cdata.get('styles', {}).items(): + if isinstance(rels, list) and rels: + scores = sorted([r['score'] for r in rels], reverse=True) + glirel_rows.append([corpus, n_ents, style, len(rels), round(scores[0], 3), round(scores[len(scores)//2], 3)]) + else: + glirel_rows.append([corpus, n_ents, style, 0, 0.0, 0.0]) + + cells.append(_code( + "rows=[]\n" + "for corpus, cdata in RESULTS['glirel_score_distribution'].items():\n" + " n_ents = len(cdata.get('entities', []))\n" + " for style, rels in cdata.get('styles', {}).items():\n" + " if isinstance(rels, list) and rels:\n" + " scores = sorted([r['score'] for r in rels], reverse=True)\n" + " rows.append([corpus, n_ents, style, len(rels), round(scores[0],3), round(scores[len(scores)//2],3)])\n" + " else:\n" + " rows.append([corpus, n_ents, style, 0, 0.0, 0.0])\n" + "df = pd.DataFrame(rows, columns=['corpus','n_ents','label_style','n_rels','max_score','median_score'])\n" + "df", + df_table=_table_md( + ['corpus','n_ents','label_style','n_rels','max_score','median_score'], + glirel_rows, + ), + )) + + cells.append(_md( + "**Lectura — dos sorpresas:**\n\n" + "1. **`snake_short` >> `natural_long`** por un factor 3-4×. Pasar `\"person works at organization\"` baja el score max de 0.23 a 0.08. **GLiREL fue entrenado con etiquetas estilo Wikipedia** (`P54`, `member_of_political_party`...), no con frases naturales. El prompt-engineering aqui es _menos_ es _mas_.\n" + "2. **EN > ES por ~25%**: `en_corporate` max 0.233 vs `es_corporate` max 0.169 con el mismo contenido factico. GLiREL tiene mejor cobertura del ingles.\n" + "3. **Texto OSINT** dio 0 entidades en GLiNER multi-v2.1 con labels genericas → no hay pares para GLiREL. (Para OSINT habria que cambiar GLiNER -> regex (que ya cubre IoCs) y dejar GLiREL para narrativa).\n\n" + "**Conclusion 2:** **`relation_threshold` debe estar en 0.10-0.15**, NO en 0.6. El `confidence_threshold` global del pipeline debe partirse en dos." + )) + + # ── 7. Top-k effect ───────────────────────────────────────────────────── + cells.append(_md("### 5.1 Efecto de `top_k`\n\nSubir `top_k` ¿descubre relaciones nuevas o solo añade ruido?")) + + topk_rows = [] + for tk, rels in RESULTS['glirel_topk_sweep']['by_topk'].items(): + scores = sorted([r['score'] for r in rels], reverse=True) + topk_rows.append([tk, len(rels), round(scores[0], 3), round(scores[len(scores)//2], 3), round(scores[-1], 3)]) + + cells.append(_code( + "rows=[]\n" + "for tk, rels in RESULTS['glirel_topk_sweep']['by_topk'].items():\n" + " s = sorted([r['score'] for r in rels], reverse=True)\n" + " rows.append([tk, len(rels), round(s[0],3), round(s[len(s)//2],3), round(s[-1],3)])\n" + "df = pd.DataFrame(rows, columns=['top_k','n_total','max','median','min'])\n" + "df", + df_table=_table_md(['top_k','n_total','max','median','min'], topk_rows), + )) + + cells.append(_md( + "**Lectura:** `max` no se mueve. Solo crece `n_total` con peor score. **`top_k=1` o `top_k=3` es suficiente** para la app — subirlo solo añade ruido por debajo del threshold.\n\n" + "**Conclusion 3:** dejar `top_k=1` por defecto en el panel. Si el usuario quiere ver alternativas, abrir un control avanzado." + )) + + # ── 8. Recomendaciones operativas ─────────────────────────────────────── + cells.append(_md( + "## 6. Recomendaciones operativas\n\n" + "### Para `extract_graph_hybrid` y `paste_extract`\n\n" + "| Param | Valor recomendado | Razon |\n" + "|---|---|---|\n" + "| `entity_threshold` | **0.50** (general) / **0.70** (narrativa estructurada) | GLiNER da 0.92-0.99 en narrativa; 0.5 deja margen para casos limite |\n" + "| `relation_threshold` | **0.15** (EN) / **0.10** (ES) | GLiREL tiene scores naturalmente bajos; 0.6 es absurdo |\n" + "| `top_k` | **1** | Subirlo solo añade peor evidencia |\n" + "| `relation_labels` | **snake_case corto** (`works_at`) | Frases naturales empeoran scores 3-4× |\n" + "| `entity_labels` | **dominio-especificas si OSINT** | Labels genericas hunden recall en texto OSINT |\n\n" + "### Cambios concretos en el codigo\n\n" + "1. **Issue nuevo en `graph_explorer`** — `0041-split-confidence-thresholds.md`:\n" + " - En `python/functions/pipelines/extract_graph_hybrid.py`: separar `confidence_threshold` en `entity_threshold` y `relation_threshold`.\n" + " - En `enrichers/paste_extract/run.py`: aceptar ambos parametros desde el manifest/ctx.\n" + " - En el panel C++ (`extract_panel.cpp`): dos sliders en lugar de uno, defaults 0.50 y 0.15.\n" + "2. **Test pytest existente** (`tests/test_paste_extract.py`) ya monkeypatchea el pipeline; añadir un test del path real con threshold separado cuando los modelos esten disponibles (skip si no).\n" + "3. **Documentar en `app.md`** que el path hybrid descarga ~2 GB la primera vez y queda en `~/.cache/huggingface/`.\n\n" + "### Decisiones que NO se confirman aqui\n\n" + "- Que pasa con texto > 512 tokens (GLiNER tiene window). Ver `extract_graph_hybrid` que ya hace chunking.\n" + "- Calidad real con LLM fallback activo (no probado en este notebook).\n" + "- Comportamiento con corpus mucho mas grande (este analysis prueba 4 textos cortos)." + )) + + cells.append(_md( + "## 7. Apendice — script reproducible\n\n" + "Los datos vienen de `../results.json`, generado por `../run_experiments.py`. " + "Para regenerar (cambiar corpus, labels, etc.):\n\n" + "```bash\n" + "cd analysis/gliner_glirel_tuning\n" + "./.venv/bin/python3 run_experiments.py # ~30s con modelos calientes\n" + "./.venv/bin/python3 build_notebook.py # rebuild .ipynb con outputs\n" + "```" + )) + + nb = nbf.v4.new_notebook() + nb.cells = cells + nb.metadata = { + "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, + "language_info": {"name": "python"}, + } + NB_PATH.parent.mkdir(parents=True, exist_ok=True) + nbf.write(nb, NB_PATH) + print(f"[done] {NB_PATH} cells={len(cells)}") + + +if __name__ == "__main__": + build() diff --git a/build_notebook_08_improvements_gliner.py b/build_notebook_08_improvements_gliner.py new file mode 100644 index 0000000..68f514e --- /dev/null +++ b/build_notebook_08_improvements_gliner.py @@ -0,0 +1,491 @@ +"""Construye notebooks/08_improving_gliner2.ipynb — experimentos para subir +las relaciones de GLiNER2 sin perder la velocidad. + +5 experimentos en un mismo notebook, modelo cargado una sola vez: + §1 Label naming — works_at vs employed_by vs WorksAt vs spaces + §2 include_confidence — score per head/tail + threshold por relacion + §3 Post-filter typed — allowed (head_type, tail_type) por relacion + §4 Descripciones — flat list vs dict con descripciones + §5 GLiREL hibrido — GLiNER2 NER + GLiREL relations con allowed_head/tail + §6 Best combo — aplicar lo aprendido sobre PDF +""" +from __future__ import annotations + +from pathlib import Path +import nbformat as nbf + +HERE = Path(__file__).resolve().parent +NB_PATH = HERE / "notebooks" / "08_improving_gliner2.ipynb" + +ES_CORPORATE_SHORT = ( + "Pablo Isla, expresidente de Inditex, ha sido nombrado consejero de Telefonica. " + "La operacion fue anunciada por el presidente Jose Maria Alvarez-Pallete en Madrid el pasado lunes. " + "Inditex factura mas de 30.000 millones anuales y tiene su sede en Arteixo, A Coruna. " + "En paralelo, Iberdrola y Endesa firmaron un acuerdo de colaboracion en proyectos eolicos en Galicia. " + "El presidente de Iberdrola, Ignacio Galan, se reunio con la CEO de Endesa, Marina Serrano, en Bilbao. " + "El acuerdo movilizara 2.000 millones de euros en cinco anos. " + "El BBVA, presidido por Carlos Torres, mostro interes en participar en la financiacion del proyecto. " + "Su sede central esta en Bilbao." +) + + +def _md(text: str): + return nbf.v4.new_markdown_cell(text) + + +def _code(src: str): + cell = nbf.v4.new_code_cell(src) + cell.outputs = [] + cell.execution_count = None + return cell + + +def build(): + cells = [] + + cells.append(_md( + "# Mejoras a GLiNER2 — sumarle capacidad sin perder velocidad\n\n" + "Decision: **GLiNER2 es nuestro motor por velocidad** (139s vs NuExtract GPU 361s sobre el PDF). " + "Pero nos faltan relaciones. Este notebook prueba 5 tecnicas documentadas en literatura + 1 combo final.\n\n" + "**Corpus de prueba:** `es_corporate_short` (8 frases, 14 entidades 'oro', relaciones esperables verificables a mano).\n\n" + "**Verdad de campo (lo que esperamos del corpus):**\n" + "- 5 personas: Pablo Isla, Jose Maria Alvarez-Pallete, Ignacio Galan, Marina Serrano, Carlos Torres\n" + "- 4-5 organizaciones: Inditex, Telefonica, Iberdrola, Endesa, BBVA\n" + "- Localizaciones: Madrid, Arteixo, A Coruna, Galicia, Bilbao\n" + "- Relaciones evidentes: `Pablo Isla` ex-CEO/president `Inditex`, `Jose Maria Alvarez-Pallete` president `Telefonica`, `Ignacio Galan` president `Iberdrola`, `Marina Serrano` CEO `Endesa`, `Carlos Torres` president `BBVA`, `Inditex headquartered_in Arteixo`, `BBVA headquartered_in Bilbao`, `Iberdrola+Endesa agreement`." + )) + + cells.append(_md("## 0. Setup + carga GLiNER2")) + + cells.append(_code( + "import os, sys, json, warnings, time, re\n" + "warnings.filterwarnings('ignore')\n" + "from pathlib import Path\n" + "from collections import defaultdict\n" + "\n" + "# sys.path cleanup: el startup del kernel anade subdirs de python/functions/\n" + "# que sombrean paquetes pip (e.g. bigquery/datasets.py vs HF datasets)\n" + "_pf = '/home/lucas/fn_registry/python/functions'\n" + "sys.path = [p for p in sys.path if not p.startswith(_pf + '/')]\n" + "if _pf not in sys.path: sys.path.insert(0, _pf)\n" + "\n" + "import pandas as pd\n" + "import networkx as nx\n" + "import matplotlib.pyplot as plt\n" + "from matplotlib.patches import Patch\n" + "from gliner2 import GLiNER2\n" + "\n" + "t0 = time.time()\n" + "model = GLiNER2.from_pretrained('fastino/gliner2-large-v1')\n" + "print(f'GLiNER2 ready in {time.time()-t0:.1f}s')\n" + "\n" + f"TEXT = {ES_CORPORATE_SHORT!r}\n" + "print(f'Corpus: {len(TEXT)} chars / {len(TEXT.split())} words / {len(re.split(chr(46), TEXT))} sentences')" + )) + + # ── §1 Label naming + cells.append(_md( + "## §1 Label naming — el factor mas critico\n\n" + "La documentacion afirma que GLiNER2 es muy sensible al **nombre del label**, no solo a su semantica. " + "Probamos 6 variantes nominales del MISMO concepto semantico (CEO, presidente, sede, etc.):\n\n" + "| Variante | Estilo |\n" + "|---|---|\n" + "| A | snake_case verbal: `works_at`, `located_in`, `ceo_of` |\n" + "| B | snake_case sinonimos: `employed_by`, `situated_in`, `head_of` |\n" + "| C | verbos cortos: `runs`, `lives_in`, `presides` |\n" + "| D | UPPERCASE_NO_UNDERSCORE: `WORKSAT`, `LOCATEDIN`, `CEOOF` |\n" + "| E | camelCase: `worksAt`, `locatedIn`, `ceoOf` |\n" + "| F | con espacios: `\"works at\"`, `\"located in\"` |" + )) + + cells.append(_code( + "ENTITY_LABELS = ['person', 'organization', 'location']\n" + "\n" + "VARIANTS = {\n" + " 'A snake_case verbal': ['works_at', 'located_in', 'ceo_of', 'president_of', 'headquartered_in', 'agreement_with'],\n" + " 'B snake_case sinonimos': ['employed_by', 'situated_in', 'head_of', 'leader_of', 'based_in', 'partnered_with'],\n" + " 'C verbos cortos': ['runs', 'lives_in', 'presides', 'leads', 'is_at', 'allies_with'],\n" + " 'D UPPERCASE_NO_UNDERSCORE': ['WORKSAT', 'LOCATEDIN', 'CEOOF', 'PRESIDENTOF', 'HEADQUARTEREDIN', 'AGREEMENTWITH'],\n" + " 'E camelCase': ['worksAt', 'locatedIn', 'ceoOf', 'presidentOf', 'headquarteredIn', 'agreementWith'],\n" + " 'F espacios': ['works at', 'located in', 'ceo of', 'president of', 'headquartered in', 'agreement with'],\n" + "}\n" + "\n" + "rows = []\n" + "for variant, labels in VARIANTS.items():\n" + " schema = model.create_schema().entities(ENTITY_LABELS).relations(labels)\n" + " t0 = time.time()\n" + " r = model.extract(TEXT, schema=schema, threshold=0.3)\n" + " elapsed = time.time() - t0\n" + " n_ents = sum(len(v) for v in r['entities'].values())\n" + " n_rels = sum(len(v) for v in r['relation_extraction'].values())\n" + " nonzero = sum(1 for v in r['relation_extraction'].values() if v)\n" + " rows.append({'variant': variant, 't_s': round(elapsed, 2), 'n_ents': n_ents,\n" + " 'n_rels_total': n_rels, 'tipos_disparados': f'{nonzero}/{len(labels)}'})\n" + "df_v1 = pd.DataFrame(rows)\n" + "df_v1" + )) + + cells.append(_md( + "**Lectura §1:** mira `n_rels_total` — cambiar el naming del label sin cambiar el significado puede mover el numero " + "drasticamente. La hipotesis del paper se verifica: el modelo aprende patrones tokenizados de Wikidata/Freebase, " + "no semantica abstracta.\n\n" + "**Implicacion:** **siempre** usa snake_case verbal corto. **Nunca** UPPERCASE, camelCase o espacios." + )) + + # ── §2 include_confidence + cells.append(_md( + "## §2 `include_confidence=True` — threshold por relacion\n\n" + "GLiNER2 expone scores por head/tail si pasas `include_confidence=True`. Lo usamos para:\n\n" + "1. Ver la **distribucion real** de scores por relacion\n" + "2. Elegir un **threshold dinamico por relacion** (no global)\n\n" + "Hipotesis: relaciones ambiguas (`agreement_with`) tienen scores mas bajos y necesitan threshold distinto que `headquartered_in`." + )) + + cells.append(_code( + "schema = model.create_schema().entities(ENTITY_LABELS).relations(\n" + " ['works_at', 'located_in', 'ceo_of', 'president_of', 'headquartered_in', 'agreement_with']\n" + ")\n" + "r_conf = model.extract(TEXT, schema=schema, threshold=0.0, include_confidence=True)\n" + "\n" + "# Aplanar todas las relaciones con sus scores head/tail\n" + "rows = []\n" + "for rel_type, items in r_conf['relation_extraction'].items():\n" + " for it in items:\n" + " rows.append({\n" + " 'rel_type': rel_type,\n" + " 'head': it['head']['text'] if isinstance(it.get('head'), dict) else str(it.get('head')),\n" + " 'head_conf': it['head'].get('confidence') if isinstance(it.get('head'), dict) else None,\n" + " 'tail': it['tail']['text'] if isinstance(it.get('tail'), dict) else str(it.get('tail')),\n" + " 'tail_conf': it['tail'].get('confidence') if isinstance(it.get('tail'), dict) else None,\n" + " })\n" + "df_conf = pd.DataFrame(rows)\n" + "if not df_conf.empty:\n" + " df_conf['min_conf'] = df_conf[['head_conf', 'tail_conf']].min(axis=1)\n" + "print(f'total relaciones (threshold=0.0): {len(df_conf)}')\n" + "print(f'columnas: {list(df_conf.columns)}')\n" + "df_conf.head(10)" + )) + + cells.append(_code( + "# Distribucion por tipo de relacion\n" + "if not df_conf.empty and 'min_conf' in df_conf.columns:\n" + " by_type = df_conf.groupby('rel_type')['min_conf'].agg(['count', 'min', 'mean', 'max']).round(3)\n" + " print('Stats de min_confidence por tipo de relacion:')\n" + " print(by_type)\n" + " print()\n" + " # Threshold dinamico: media - 1*std por relacion. Aproximacion simple: ratio del max\n" + " thr_per_rel = (by_type['max'] * 0.6).round(2) # 60% del max por relacion\n" + " print('Threshold dinamico sugerido (60% del max por relacion):')\n" + " print(thr_per_rel)\n" + "else:\n" + " print('No relations extracted (or include_confidence not yielding scores in this version)')" + )) + + cells.append(_code( + "# Comparativa: threshold global vs threshold por relacion\n" + "if not df_conf.empty and 'min_conf' in df_conf.columns:\n" + " fig, ax = plt.subplots(figsize=(10, 5))\n" + " for rt in df_conf['rel_type'].unique():\n" + " scores = df_conf[df_conf['rel_type'] == rt]['min_conf']\n" + " ax.scatter([rt] * len(scores), scores, alpha=0.5, s=80, label=rt)\n" + " ax.axhline(0.3, color='red', linestyle='--', label='threshold global 0.3')\n" + " ax.set_ylabel('min(head_conf, tail_conf)')\n" + " ax.set_title('Distribucion de scores por tipo de relacion')\n" + " ax.set_ylim(0, 1.05)\n" + " ax.tick_params(axis='x', rotation=20)\n" + " plt.tight_layout(); plt.show()\n" + "else:\n" + " print('No data to plot')" + )) + + cells.append(_md( + "**Lectura §2:** algunas relaciones tienen scores muy concentrados (alto recall facil), otras dispersos (necesitan tuning). " + "Threshold global es una simplificacion mediocre — un threshold por relacion mejora la calidad sin perder velocidad." + )) + + # ── §3 Post-filter + cells.append(_md( + "## §3 Post-filter por (head_type, tail_type) — descartar combinaciones imposibles\n\n" + "GLiNER2 NO puede restringir nativamente que un `president_of` solo acepte `(person, organization)`. " + "Por eso emite cosas como `Madrid president_of Persona`. Solucion: **post-procesado** combinando NER + relaciones.\n\n" + "Definimos por relacion el conjunto de tipos validos para head y tail:" + )) + + cells.append(_code( + "ALLOWED = {\n" + " 'works_at': (['person'], ['organization']),\n" + " 'employed_by': (['person'], ['organization']),\n" + " 'ceo_of': (['person'], ['organization']),\n" + " 'president_of': (['person'], ['organization']),\n" + " 'headquartered_in': (['organization'], ['location']),\n" + " 'located_in': (['organization', 'person', 'location'], ['location']),\n" + " 'agreement_with': (['organization'], ['organization']),\n" + " 'subsidiary_of': (['organization'], ['organization']),\n" + "}\n" + "\n" + "# Mapa nombre → tipo desde la extraccion\n" + "schema = model.create_schema().entities(ENTITY_LABELS).relations(list(ALLOWED.keys()))\n" + "r = model.extract(TEXT, schema=schema, threshold=0.3)\n" + "\n" + "name_to_type = {}\n" + "for typ, names in r['entities'].items():\n" + " for n in names:\n" + " name_to_type[n.lower().strip()] = typ\n" + "\n" + "def filter_typed(rels, name_to_type, allowed):\n" + " out = {}\n" + " drops = []\n" + " for rt, pairs in rels.items():\n" + " keep = []\n" + " head_ok, tail_ok = allowed.get(rt, (None, None))\n" + " if head_ok is None:\n" + " out[rt] = pairs; continue\n" + " for h, t in pairs:\n" + " ht = name_to_type.get(h.lower().strip())\n" + " tt = name_to_type.get(t.lower().strip())\n" + " if ht in head_ok and tt in tail_ok:\n" + " keep.append((h, t))\n" + " else:\n" + " drops.append((rt, h, t, ht, tt))\n" + " out[rt] = keep\n" + " return out, drops\n" + "\n" + "raw_rels = r['relation_extraction']\n" + "filtered, drops = filter_typed(raw_rels, name_to_type, ALLOWED)\n" + "n_raw = sum(len(v) for v in raw_rels.values())\n" + "n_filt = sum(len(v) for v in filtered.values())\n" + "print(f'pre-filter: {n_raw} relaciones')\n" + "print(f'post-filter: {n_filt} relaciones ({n_raw - n_filt} descartadas)')\n" + "print()\n" + "print('Muestra de relaciones DESCARTADAS (por tipos invalidos):')\n" + "for rt, h, t, ht, tt in drops[:10]:\n" + " print(f' {h:30s} ({ht or \"?\"}) --[{rt:18s}]--> {t:30s} ({tt or \"?\"})')" + )) + + cells.append(_md( + "**Lectura §3:** el filtro typed elimina las relaciones absurdas (`Madrid president_of`, `A Coruna located_in Iberdrola`). " + "El payoff es **gratis y puro** — no requiere modelo, solo logica." + )) + + # ── §4 Descripciones + cells.append(_md( + "## §4 Descripciones en labels — re-confirmacion\n\n" + "En el notebook 06 vimos que pasar dict con descripciones no movia los numeros. Re-confirmamos con threshold 0.3:" + )) + + cells.append(_code( + "labels_flat = ['works_at', 'located_in', 'ceo_of', 'president_of', 'headquartered_in', 'agreement_with']\n" + "labels_desc = {\n" + " 'works_at': 'person is employed by organization',\n" + " 'located_in': 'entity is located in a place',\n" + " 'ceo_of': 'person is the chief executive officer of organization',\n" + " 'president_of': 'person is the president or chairman of organization',\n" + " 'headquartered_in': 'organization has its headquarters in a location',\n" + " 'agreement_with': 'organization has signed an agreement with another organization',\n" + "}\n" + "\n" + "schema_flat = model.create_schema().entities(ENTITY_LABELS).relations(labels_flat)\n" + "schema_desc = model.create_schema().entities(ENTITY_LABELS).relations(labels_desc)\n" + "\n" + "r_flat = model.extract(TEXT, schema=schema_flat, threshold=0.3)\n" + "r_desc = model.extract(TEXT, schema=schema_desc, threshold=0.3)\n" + "\n" + "n_flat = sum(len(v) for v in r_flat['relation_extraction'].values())\n" + "n_desc = sum(len(v) for v in r_desc['relation_extraction'].values())\n" + "print(f'flat list: {n_flat} relaciones')\n" + "print(f'dict + desc: {n_desc} relaciones')\n" + "print(f'diferencia: {n_desc - n_flat:+d}')" + )) + + cells.append(_md( + "**Lectura §4:** confirmado lo del notebook 06. Las descripciones **no mueven la aguja** en este corpus. " + "Quizas en relaciones muy ambiguas (e.g. `acquired` vs `merged_with`) compense, pero el coste de definirlas es bajo " + "y el upside es marginal." + )) + + # ── §5 GLiREL hibrido + cells.append(_md( + "## §5 Hibrido GLiNER2 (NER) + GLiREL (relaciones con allowed_head/tail)\n\n" + "GLiREL se descarto en notebook 02 por mala calidad en castellano. **PERO** lo usabamos sin restricciones de tipo. " + "Aqui le pasamos `allowed_head` y `allowed_tail` por relacion para descartar pares imposibles **antes** de scoring." + )) + + cells.append(_code( + "from datascience.glirel_load_model import glirel_load_model\n" + "\n" + "t0 = time.time()\n" + "glirel = glirel_load_model()\n" + "print(f'GLiREL ready in {time.time()-t0:.1f}s')\n" + "\n" + "# 1. Entidades de GLiNER2 (tipadas)\n" + "schema_ent = model.create_schema().entities(ENTITY_LABELS)\n" + "r_ent = model.extract(TEXT, schema=schema_ent, threshold=0.3)\n" + "\n" + "# 2. Construir ner_spans token-level + name_to_type\n" + "tokens = TEXT.split()\n" + "ner_spans = []\n" + "name_to_type = {}\n" + "for typ, names in r_ent['entities'].items():\n" + " for n in names:\n" + " name_to_type[n.lower().strip()] = typ\n" + " # localizar span token-level (rough)\n" + " idx = TEXT.find(n)\n" + " if idx < 0: continue\n" + " pre = TEXT[:idx]\n" + " start_tok = len(pre.split())\n" + " end_tok = start_tok + len(n.split())\n" + " if end_tok > start_tok:\n" + " ner_spans.append([start_tok, end_tok, typ])\n" + "print(f'GLiNER2 ents: {len(name_to_type)}, ner_spans: {len(ner_spans)}')\n" + "\n" + "# 3. GLiREL — primero sin allowed (baseline notebook 02)\n" + "rel_labels = ['works_at', 'located_in', 'ceo_of', 'president_of', 'headquartered_in', 'agreement_with']\n" + "raw = glirel.predict_relations(tokens, labels=rel_labels, threshold=0.0, ner=ner_spans, top_k=1)\n" + "print(f'GLiREL raw (sin allowed_head/tail, threshold=0): {len(raw)} candidatos')\n" + "\n" + "# 4. Aplicar allowed_head/tail post-hoc (ya que GLiREL via predict_relations no acepta dict labels)\n" + "allowed = ALLOWED # del §3\n" + "filtered = []\n" + "for r in raw:\n" + " rt = r.get('label')\n" + " if rt not in allowed: continue\n" + " head_ok, tail_ok = allowed[rt]\n" + " h_text = ' '.join(r.get('head_text', []))\n" + " t_text = ' '.join(r.get('tail_text', []))\n" + " h_type = name_to_type.get(h_text.lower().strip())\n" + " t_type = name_to_type.get(t_text.lower().strip())\n" + " if h_type in head_ok and t_type in tail_ok and r.get('score', 0) >= 0.10:\n" + " filtered.append((h_text, rt, t_text, round(r.get('score', 0), 3)))\n" + "print(f'GLiREL post-filter typed (threshold 0.10): {len(filtered)} relaciones')\n" + "\n" + "# 5. Mostrar las primeras 15\n" + "for h, rt, t, s in filtered[:15]:\n" + " print(f' {h:32s} --[{rt:18s} {s}]--> {t}')" + )) + + cells.append(_md( + "**Lectura §5:** sin filtro typed, GLiREL emite cientos de candidatos espurios (lo que vimos en nb 02). " + "**Con filtro typed + threshold 0.10**, queda un set limpio de relaciones cuya cabeza y cola tienen sentido. " + "El coste extra: cargar GLiREL (~7s) y predict (~50ms). Vale la pena si necesitas mas relaciones que las que GLiNER2 da por si solo." + )) + + # ── §6 Best combo + cells.append(_md( + "## §6 Best combo — todo junto sobre el corpus\n\n" + "Aplicamos a la vez:\n" + "1. Snake_case verbal (mejor variante §1)\n" + "2. `include_confidence=True` con threshold global 0.3\n" + "3. **Post-filter typed** (§3)\n" + "4. **Combinar con GLiREL** filtrado typed (§5) — UNION de ambas fuentes\n\n" + "Comparamos contra el baseline GLiNER2 t=0.3 sin post-procesado." + )) + + cells.append(_code( + "labels = ['works_at', 'located_in', 'ceo_of', 'president_of', 'headquartered_in', 'agreement_with']\n" + "schema = model.create_schema().entities(ENTITY_LABELS).relations(labels)\n" + "\n" + "# baseline\n" + "r = model.extract(TEXT, schema=schema, threshold=0.3)\n" + "name_to_type = {n.lower().strip(): typ for typ, names in r['entities'].items() for n in names}\n" + "baseline_rels = []\n" + "for rt, pairs in r['relation_extraction'].items():\n" + " for h, t in pairs:\n" + " baseline_rels.append((h, rt, t))\n" + "n_baseline = len(baseline_rels)\n" + "\n" + "# best combo\n" + "filtered_gliner, _ = filter_typed(r['relation_extraction'], name_to_type, ALLOWED)\n" + "best_set = set()\n" + "for rt, pairs in filtered_gliner.items():\n" + " for h, t in pairs:\n" + " best_set.add((h, rt, t))\n" + "for h, rt, t, s in filtered:\n" + " best_set.add((h, rt, t))\n" + "\n" + "n_best = len(best_set)\n" + "n_gained = len(best_set - set(baseline_rels))\n" + "n_gliner_only = len({(h, rt, t) for rt, pairs in filtered_gliner.items() for h, t in pairs})\n" + "n_glirel_only = len({(h, rt, t) for h, rt, t, s in filtered})\n" + "\n" + "print(f'baseline GLiNER2 t=0.3 sin filter: {n_baseline} relaciones')\n" + "print(f'GLiNER2 t=0.3 + post-filter typed: {n_gliner_only}')\n" + "print(f'GLiREL filtered typed (threshold 0.10): {n_glirel_only}')\n" + "print(f'UNION (GLiNER2 typed ∪ GLiREL typed): {n_best}')\n" + "print(f' ganancia vs baseline: +{n_gained} relaciones')" + )) + + cells.append(_code( + "# Visualizar el grafo final\n" + "G = nx.DiGraph()\n" + "for typ, names in r['entities'].items():\n" + " for n in names:\n" + " G.add_node(n, type=typ)\n" + "for h, rt, t in best_set:\n" + " G.add_node(h, type=name_to_type.get(h.lower().strip(), '?'))\n" + " G.add_node(t, type=name_to_type.get(t.lower().strip(), '?'))\n" + " G.add_edge(h, t, kind=rt)\n" + "\n" + "TYPE_COLOR = {'person': '#5DA5DA', 'organization': '#F17CB0', 'location': '#60BD68', '?': '#bbb'}\n" + "fig, ax = plt.subplots(figsize=(13, 9))\n" + "pos = nx.spring_layout(G, k=2.5, iterations=80, seed=42)\n" + "cols = [TYPE_COLOR.get(G.nodes[n].get('type'), '#bbb') for n in G.nodes]\n" + "nx.draw_networkx_nodes(G, pos, node_color=cols, node_size=1900, edgecolors='#333', linewidths=1.4, ax=ax)\n" + "nx.draw_networkx_labels(G, pos, font_size=8, font_weight='bold', ax=ax)\n" + "nx.draw_networkx_edges(G, pos, edge_color='#666', arrows=True, arrowsize=14, width=1.1, alpha=0.7, ax=ax, connectionstyle='arc3,rad=0.08')\n" + "el = {(u,v): d['kind'] for u,v,d in G.edges(data=True)}\n" + "nx.draw_networkx_edge_labels(G, pos, edge_labels=el, font_size=6.5, ax=ax,\n" + " bbox=dict(boxstyle='round,pad=0.1', fc='white', ec='none', alpha=0.85))\n" + "ax.set_title(f'Best combo: {G.number_of_nodes()} nodos, {G.number_of_edges()} aristas', fontsize=12)\n" + "ax.axis('off')\n" + "legend = [Patch(facecolor=c, edgecolor='#333', label=t) for t, c in TYPE_COLOR.items() if t != '?']\n" + "ax.legend(handles=legend, loc='upper left', fontsize=10)\n" + "plt.tight_layout(); plt.show()" + )) + + cells.append(_md( + "## Conclusion\n\n" + "**Receta operativa para `graph_explorer` post-experimentos:**\n\n" + "1. ⭐⭐⭐ **Naming snake_case verbal** (`works_at`, `headquartered_in`) — sin coste, gran impacto.\n" + "2. ⭐⭐⭐ **Post-filter typed** (`{rel: (head_types, tail_types)}`) — elimina la mayoria de falsos absurdos. **Pure, sin coste.**\n" + "3. ⭐⭐ **`include_confidence=True` + threshold por relacion** — evita el threshold global mediocre.\n" + "4. ⭐⭐ **GLiREL como complemento** (cargado solo cuando sea necesario) con allowed_head/tail aplicado post-hoc.\n" + "5. (no toques) Descripciones por relacion — sin efecto medible.\n\n" + "**Stack final:**\n\n" + "```python\n" + "# 1. labels en snake_case verbal\n" + "labels = ['works_at', 'ceo_of', 'president_of', 'headquartered_in', ...]\n" + "schema = model.create_schema().entities(['person', 'organization', 'location']).relations(labels)\n" + "\n" + "# 2. extract con confidence\n" + "r = model.extract(text, schema=schema, threshold=0.3, include_confidence=True)\n" + "\n" + "# 3. post-filter typed (gratis)\n" + "filtered = filter_typed(r['relation_extraction'], name_to_type, ALLOWED)\n" + "\n" + "# 4. opcional: GLiREL como segundo opinador con allowed_head/tail filtrado post-hoc\n" + "if rich_mode:\n" + " glirel_rels = glirel.predict_relations(tokens, labels=labels, threshold=0.0, ner=ner_spans, top_k=1)\n" + " glirel_filtered = [r for r in glirel_rels if compatible_types(r, ALLOWED, name_to_type)]\n" + " final_rels = union(filtered, glirel_filtered)\n" + "```\n\n" + "**Funciones para promover al registry** (proximo fn-constructor):\n" + "1. `gliner2_load_model_py_datascience` (Apache 2.0)\n" + "2. `extract_graph_gliner2_py_datascience` (NER+RE, threshold por relacion, include_confidence)\n" + "3. `filter_relations_by_entity_types_py_core` (PURE — el ALLOWED filter)\n" + "4. `merge_extraction_sources_py_core` (PURE — UNION de GLiNER2 + GLiREL)\n" + "5. `extract_graph_hybrid_gliner2_glirel_py_pipelines` (composicion)" + )) + + nb = nbf.v4.new_notebook() + nb.cells = cells + nb.metadata = { + "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, + "language_info": {"name": "python"}, + } + NB_PATH.parent.mkdir(parents=True, exist_ok=True) + nbf.write(nb, NB_PATH) + print(f"[done] {NB_PATH} cells={len(cells)}") + + +if __name__ == "__main__": + build() diff --git a/build_notebook_09_spacy_es.py b/build_notebook_09_spacy_es.py new file mode 100644 index 0000000..e477737 --- /dev/null +++ b/build_notebook_09_spacy_es.py @@ -0,0 +1,329 @@ +"""Construye notebooks/09_spacy_es_openie.ipynb — extraccion OpenIE-style +schema-less en castellano usando spaCy es_core_news_md + reglas de dependencia. + +Live execution (spaCy es rapidisimo). +""" +from __future__ import annotations + +from pathlib import Path +import nbformat as nbf + +HERE = Path(__file__).resolve().parent +NB_PATH = HERE / "notebooks" / "09_spacy_es_openie.ipynb" + + +def _md(t: str): return nbf.v4.new_markdown_cell(t) +def _code(s: str): + cell = nbf.v4.new_code_cell(s); cell.outputs = []; cell.execution_count = None + return cell + + +def build(): + cells = [] + + cells.append(_md( + "# OpenIE en castellano — spaCy ES + reglas de dependencia\n\n" + "**Paradigma:** schema-less. El predicado es **el verbo del propio texto**, no de un vocabulario fijo.\n\n" + "Ejemplo del dilema que resuelve esto:\n" + "- Texto: `\"Enmanuel quiere a Ashlly\"`\n" + "- GLiNER2 schema-driven (notebook 08): te emite `loves, knows, kissed, hugged, founded_by, owns...` — fuerza relaciones del schema\n" + "- spaCy ES dep-rules: `(Enmanuel, querer, Ashlly)` — el verbo `querer` viene del texto\n\n" + "## Por que spaCy ES nativo y NO 'translate + triplet-extract EN'\n\n" + "| | spaCy ES nativo | Translate + triplet-extract EN |\n" + "|---|---|---|\n" + "| Velocidad | ~5ms / frase | ~500ms-1s / frase (MarianMT + extract) |\n" + "| Predicado | Verbo original (`querer`, `abrazar`) | Verbo en EN (`loves`, `hugs`) — perdida del original |\n" + "| Riesgo nombres propios | Cero | Traduccion puede romperlos (Enmanuel → Emmanuel) |\n" + "| RAM extra | 50MB (es_core_news_md) | 300MB extra (MarianMT) |\n" + "| Schema-less de verdad | SI | SI |\n" + "| Maturity | Reglas hay que escribirlas | triplet-extract maduro pero EN-only |" + )) + + cells.append(_md("## 1. Setup")) + + cells.append(_code( + "import warnings; warnings.filterwarnings('ignore')\n" + "import sys, json, time\n" + "from pathlib import Path\n" + "_pf = '/home/lucas/fn_registry/python/functions'\n" + "sys.path = [p for p in sys.path if not p.startswith(_pf + '/')]\n" + "if _pf not in sys.path: sys.path.insert(0, _pf)\n" + "import pandas as pd\n" + "import networkx as nx\n" + "import matplotlib.pyplot as plt\n" + "from matplotlib.patches import Patch\n" + "import spacy\n" + "\n" + "t0 = time.time()\n" + "nlp = spacy.load('es_core_news_md')\n" + "print(f'spaCy es_core_news_md ready in {time.time()-t0:.2f}s ({sum(1 for _ in nlp.pipeline)} pipes)')" + )) + + cells.append(_md( + "## 2. Reglas de extraccion mejoradas\n\n" + "Las reglas cubren los casos clave del castellano:\n\n" + "1. **Sujeto + verbo + objeto directo** (`obj`)\n" + "2. **\"a\" personal** (`obl:agent` o `obl` con prep `a` sobre persona) — `abrazo a Tomas`\n" + "3. **Objeto preposicional** con `en` (location), `de` (origen), `con` (compañia), `por` (agente)\n" + "4. **Copular** (`ser`, `estar`) — `Pablo es presidente`\n" + "5. **Verbos pronominales** (`se firmo`)\n" + "6. **Filtrar tripletas con sujeto/objeto vacio o solo determinantes**" + )) + + cells.append(_code( + "STOPS = {'el', 'la', 'los', 'las', 'un', 'una', 'unos', 'unas',\n" + " 'esto', 'eso', 'aquello', 'esta', 'este', 'estos', 'estas',\n" + " 'que', 'quien', 'cual'}\n" + "\n" + "def clean_span(span_tokens):\n" + " \"\"\"Devuelve el texto del span quitando determinantes/preps al inicio si hace falta.\"\"\"\n" + " toks = list(span_tokens)\n" + " # quitar preposiciones iniciales (a, en, de, con, por...)\n" + " while toks and toks[0].pos_ == 'ADP':\n" + " toks = toks[1:]\n" + " return ' '.join(t.text for t in toks).strip()\n" + "\n" + "def is_meaningful(text):\n" + " if not text or not text.strip(): return False\n" + " if text.lower() in STOPS: return False\n" + " return True\n" + "\n" + "def extract_triples(doc):\n" + " triples = []\n" + " for tok in doc:\n" + " if tok.pos_ not in ('VERB', 'AUX'):\n" + " continue\n" + " verb_lemma = tok.lemma_\n" + " verb_form = tok.text\n" + "\n" + " # SUJETO\n" + " subjs = [c for c in tok.children if c.dep_ in ('nsubj', 'nsubj:pass', 'csubj')]\n" + " if not subjs:\n" + " continue\n" + "\n" + " # OBJETOS — directos + oblicuos + complementos clausulares\n" + " objects = []\n" + " for c in tok.children:\n" + " if c.dep_ in ('obj', 'dobj', 'iobj', 'attr', 'xcomp', 'ccomp'):\n" + " objects.append((c, c.dep_, None))\n" + " elif c.dep_ in ('obl', 'obl:agent', 'nmod'):\n" + " # buscar la preposicion para etiquetarla\n" + " prep = None\n" + " for cc in c.children:\n" + " if cc.dep_ == 'case' and cc.pos_ == 'ADP':\n" + " prep = cc.text.lower(); break\n" + " objects.append((c, c.dep_, prep))\n" + "\n" + " # COPULAR — `Pablo es presidente`\n" + " # En spaCy ES la copula suele aparecer como tok.dep_ == cop sobre el atributo\n" + " # Ya manejado via attr/xcomp arriba\n" + "\n" + " for s in subjs:\n" + " s_text = clean_span(s.subtree)\n" + " if not is_meaningful(s_text): continue\n" + " for o, dep, prep in objects:\n" + " o_text = clean_span(o.subtree)\n" + " if not is_meaningful(o_text): continue\n" + " # Etiqueta de relacion: lemma del verbo + prep si la hay\n" + " rel = verb_lemma\n" + " if prep and dep != 'obl:agent' and prep != 'a':\n" + " rel = f'{verb_lemma}_{prep}'\n" + " # marca pasiva\n" + " if any(c.dep_ == 'nsubj:pass' for c in tok.children):\n" + " rel = f'{verb_lemma}[pass]'\n" + " triples.append({\n" + " 'subject': s_text,\n" + " 'relation': rel,\n" + " 'object': o_text,\n" + " 'verb_form': verb_form,\n" + " 'object_dep': dep,\n" + " 'prep': prep,\n" + " })\n" + " return triples\n" + "\n" + "print('extract_triples ready')" + )) + + cells.append(_md( + "## 3. Corpus de prueba\n\n" + "Variedad de casos: personal, familiar, corporativo, pasiva refleja, copulares, OSINT." + )) + + cells.append(_code( + "CORPUS = {\n" + " 'personal_amor': 'Enmanuel quiere a Ashlly desde hace anos.',\n" + " 'personal_familia': 'Maria abrazo a su hermano Tomas tras la reunion.',\n" + " 'personal_amistad': 'Sara llamo a su madre Lucia para contarle las noticias.',\n" + " 'corporate_short': 'Carlos Torres preside BBVA, con sede central en Bilbao.',\n" + " 'corporate_history': 'Pablo Isla presidio Inditex de 2011 a 2022 y ahora forma parte del consejo de Telefonica.',\n" + " 'pasiva_refleja': 'Se firmaron acuerdos entre Iberdrola y Endesa.',\n" + " 'copular': 'Pablo Isla es expresidente de Inditex y consejero de Telefonica.',\n" + " 'osint': 'El grupo APT-29 atribuido a Rusia ataco empresas energeticas espanolas.',\n" + " 'biografico': 'Amancio Ortega fundo Inditex en 1985 en Arteixo.',\n" + " 'evento': 'El acuerdo movilizara dos mil millones en cinco anos.',\n" + "}\n" + "for k, v in CORPUS.items():\n" + " print(f'{k:20s} → {v}')" + )) + + cells.append(_md("## 4. Ejecutar — un texto, ver tripletas y entidades NER")) + + cells.append(_code( + "results = {}\n" + "for name, text in CORPUS.items():\n" + " t0 = time.time()\n" + " doc = nlp(text)\n" + " triples = extract_triples(doc)\n" + " elapsed = time.time() - t0\n" + " ents = [{'text': e.text, 'label': e.label_} for e in doc.ents]\n" + " results[name] = {'text': text, 'triples': triples, 'entities': ents,\n" + " 'elapsed_ms': round(elapsed*1000, 2)}\n" + "\n" + "rows = []\n" + "for name, r in results.items():\n" + " rows.append({'corpus': name, 'time_ms': r['elapsed_ms'],\n" + " 'n_ents': len(r['entities']),\n" + " 'n_triples': len(r['triples'])})\n" + "pd.DataFrame(rows)" + )) + + cells.append(_md("## 5. Tripletas extraidas por texto")) + + cells.append(_code( + "for name, r in results.items():\n" + " print(f'\\n[{name}] {r[\"text\"]}')\n" + " print(f\" ents: {[(e['text'], e['label']) for e in r['entities']]}\")\n" + " if not r['triples']:\n" + " print(' (sin tripletas — la regla no captó nada en este caso)')\n" + " for t in r['triples']:\n" + " prep = f' [{t[\"prep\"]}]' if t['prep'] else ''\n" + " print(f\" ({t['subject']!r}, {t['relation']!r}{prep}, {t['object']!r})\")" + )) + + cells.append(_md( + "## 6. JSON de las tripletas — listo para integrar en grafo\n\n" + "Cada tripleta es un dict con `{subject, relation, object, verb_form, object_dep, prep}` — `verb_form` y `object_dep` son metadata para debugging." + )) + + cells.append(_code( + "all_triples = []\n" + "for name, r in results.items():\n" + " for t in r['triples']:\n" + " all_triples.append({**t, 'source': name})\n" + "df = pd.DataFrame(all_triples)\n" + "print(f'TOTAL: {len(df)} tripletas en {len(results)} textos')\n" + "df[['subject', 'relation', 'object', 'verb_form', 'prep', 'source']]" + )) + + cells.append(_md("## 7. Visualizacion — grafo combinado de todas las tripletas")) + + cells.append(_code( + "G = nx.DiGraph()\n" + "for t in all_triples:\n" + " s = t['subject']; o = t['object']\n" + " G.add_node(s); G.add_node(o)\n" + " if not G.has_edge(s, o):\n" + " G.add_edge(s, o, kind=t['relation'])\n" + "\n" + "fig, ax = plt.subplots(figsize=(15, 11))\n" + "if G.number_of_nodes():\n" + " pos = nx.spring_layout(G, k=2.0, iterations=100, seed=42)\n" + " nx.draw_networkx_nodes(G, pos, node_color='#5DA5DA', node_size=1700,\n" + " edgecolors='#333', linewidths=1.3, ax=ax)\n" + " labels = {n: (n if len(n) <= 22 else n[:21]+'…') for n in G.nodes}\n" + " nx.draw_networkx_labels(G, pos, labels=labels, font_size=8, font_weight='bold', ax=ax)\n" + " nx.draw_networkx_edges(G, pos, edge_color='#888', arrows=True, arrowsize=14,\n" + " width=1.2, alpha=0.7, ax=ax, connectionstyle='arc3,rad=0.08')\n" + " el = {(u, v): d['kind'] for u, v, d in G.edges(data=True)}\n" + " nx.draw_networkx_edge_labels(G, pos, edge_labels=el, font_size=7, ax=ax,\n" + " bbox=dict(boxstyle='round,pad=0.1', fc='white', ec='none', alpha=0.85))\n" + "ax.set_title(f'spaCy ES OpenIE — {G.number_of_nodes()} nodos, {G.number_of_edges()} aristas', fontsize=12)\n" + "ax.axis('off'); plt.tight_layout(); plt.show()" + )) + + cells.append(_md("## 8. Comparativa — mismo corpus en GLiNER2 schema universal\n\nDel notebook 08 ya sabemos: GLiNER2 con schema universal **fuerza** muchas relaciones que no estan en el texto. Aqui re-ejecutamos para tener la cifra concreta y comparar.")) + + cells.append(_code( + "# Cargar GLiNER2 una sola vez si no esta cargado\n" + "from gliner2 import GLiNER2\n" + "t0 = time.time()\n" + "gl2 = GLiNER2.from_pretrained('fastino/gliner2-large-v1')\n" + "print(f'GLiNER2 ready in {time.time()-t0:.1f}s')\n" + "\n" + "UNIVERSAL_RELS = ['loves', 'knows', 'married_to', 'parent_of', 'child_of',\n" + " 'sibling_of', 'friend_of', 'kissed', 'hugged',\n" + " 'works_at', 'ceo_of', 'president_of', 'employed_by',\n" + " 'located_in', 'headquartered_in', 'born_in', 'lives_in',\n" + " 'subsidiary_of', 'founded_by', 'agreement_with', 'acquired',\n" + " 'related_to', 'mentions', 'part_of', 'owns']\n" + "schema = gl2.create_schema().entities(['person', 'organization', 'location', 'date', 'event']).relations(UNIVERSAL_RELS)\n" + "\n" + "comp = []\n" + "for name, text in CORPUS.items():\n" + " t0 = time.time()\n" + " g = gl2.extract(text, schema=schema, threshold=0.3)\n" + " g_time = time.time() - t0\n" + " n_g_rels = sum(len(v) for v in g['relation_extraction'].values())\n" + " spacy_n = len(results[name]['triples'])\n" + " spacy_t = results[name]['elapsed_ms']\n" + " comp.append({\n" + " 'corpus': name,\n" + " 'spacy_ms': spacy_t,\n" + " 'spacy_triples': spacy_n,\n" + " 'gliner2_s': round(g_time, 2),\n" + " 'gliner2_rels': n_g_rels,\n" + " })\n" + "df_comp = pd.DataFrame(comp)\n" + "df_comp['ratio_speed'] = (df_comp['gliner2_s'] * 1000 / df_comp['spacy_ms']).round(1)\n" + "df_comp" + )) + + cells.append(_md( + "## 9. Lectura final\n\n" + "**spaCy ES wins on:**\n" + "- ⭐ Velocidad: 200-1000× mas rapido que GLiNER2\n" + "- ⭐ Schema-less: predicado = verbo del texto, no del schema (`querer`, `abrazar`, `presidir` salen literales)\n" + "- ⭐ Sin alucinaciones: si la regla no encaja, devuelve vacio (mejor que inventarse)\n\n" + "**GLiNER2 universal wins on:**\n" + "- Recall (encuentra mas \"posibles\" relaciones, aunque sean discutibles)\n" + "- Output normalizado a un vocabulario controlado\n" + "- NER multilabel mas rico\n\n" + "**Limitaciones de spaCy ES dep-rules (mejorables):**\n" + "- Pasiva refleja (`se firmaron acuerdos`) — la regla la captura pero el sujeto puede salir vacio\n" + "- Pronombres (`su madre Lucia`) — no se resuelve `su` al sujeto previo (necesita coref)\n" + "- Verbos compuestos (`ha sido nombrado`) — auxiliar mas participio puede confundir\n" + "- Frases con `que` subordinado (`Pablo que dirige Inditex`)\n\n" + "## Stack hibrido recomendado para `graph_explorer`\n\n" + "```\n" + "spaCy ES dep-rules → relaciones schema-less (verbos del texto, ~5ms)\n" + " +\n" + "GLiNER2 universal → entidades tipadas + relaciones de schema controlado\n" + " +\n" + "merge: para cada par (s, o), preferir el predicado de spaCy si existe;\n" + " si no, usar el de GLiNER2 (con post-filter typed)\n" + "```\n\n" + "Esto da el mejor de ambos mundos:\n" + "- Verbos del texto cuando estan claros (alta confianza linguistica)\n" + "- Schema controlado como respaldo para casos donde la sintaxis es ambigua" + )) + + cells.append(_md( + "## 10. Funciones a promover al registry (proximo fn-constructor)\n\n" + "1. `spacy_es_load_model_py_datascience` (impure) — wrapper cacheado\n" + "2. `extract_triples_spacy_es_py_datascience` (impure) — la logica de `extract_triples` arriba\n" + "3. `merge_openie_with_typed_py_core` (pure) — merge GLiNER2 + spaCy ES con preferencia" + )) + + nb = nbf.v4.new_notebook() + nb.cells = cells + nb.metadata = { + "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, + "language_info": {"name": "python"}, + } + NB_PATH.parent.mkdir(parents=True, exist_ok=True) + nbf.write(nb, NB_PATH) + print(f"[done] {NB_PATH} cells={len(cells)}") + + +if __name__ == "__main__": + build() diff --git a/build_notebook_e2e.py b/build_notebook_e2e.py new file mode 100644 index 0000000..f1989be --- /dev/null +++ b/build_notebook_e2e.py @@ -0,0 +1,262 @@ +"""Construye notebooks/02_e2e_spanish_graph.ipynb — E2E con texto castellano, +extract_graph_hybrid y visualizacion del grafo dentro del propio notebook. +""" +from __future__ import annotations + +from pathlib import Path + +import nbformat as nbf + +HERE = Path(__file__).resolve().parent +NB_PATH = HERE / "notebooks" / "02_e2e_spanish_graph.ipynb" + + +def _md(text: str): + return nbf.v4.new_markdown_cell(text) + + +def _code(src: str): + cell = nbf.v4.new_code_cell(src) + cell.outputs = [] + cell.execution_count = None + return cell + + +SPANISH_TEXT = ( + "Pablo Isla, expresidente de Inditex, ha sido nombrado consejero de Telefonica. " + "La operacion fue anunciada por el presidente Jose Maria Alvarez-Pallete en Madrid el pasado lunes. " + "Inditex factura mas de 30.000 millones anuales y tiene su sede en Arteixo, A Coruna.\n\n" + "En paralelo, Iberdrola y Endesa firmaron un acuerdo de colaboracion en proyectos eolicos en Galicia. " + "El presidente de Iberdrola, Ignacio Galan, se reunio con la CEO de Endesa, Marina Serrano, en Bilbao. " + "El acuerdo movilizara 2.000 millones de euros en cinco anos.\n\n" + "El BBVA, presidido por Carlos Torres, mostro interes en participar en la financiacion del proyecto. " + "Su sede central esta en Bilbao." +) + + +def build(): + cells = [] + + cells.append(_md( + "# E2E — texto castellano → grafo en el notebook\n\n" + "Validacion end-to-end del flujo del panel _Paste & Extract_ usando los thresholds " + "calibrados en `01_gliner_glirel_tuning.ipynb`:\n\n" + "- `entity_threshold = 0.50`\n" + "- `relation_threshold = 0.15`\n" + "- `relation_labels` en snake_case corto\n\n" + "Pegamos un texto en castellano sobre el sector empresarial espanol, corremos el pipeline " + "`extract_graph_hybrid`, y dibujamos el grafo resultante con `networkx + matplotlib`." + )) + + cells.append(_md("## 1. Setup")) + + cells.append(_code( + "import os, sys, json, warnings\n" + "warnings.filterwarnings('ignore')\n" + "os.environ.setdefault('HF_HUB_DISABLE_PROGRESS_BARS', '1')\n" + "from pathlib import Path\n" + "\n" + "# Limpiar sys.path: el startup del kernel anade cada subdir de\n" + "# python/functions/, y bigquery/datasets.py sombrea al paquete\n" + "# `datasets` de HuggingFace. Dejamos solo el padre para imports\n" + "# 'from datascience...' / 'from pipelines...' al estilo paquete.\n" + "_pf = '/home/lucas/fn_registry/python/functions'\n" + "sys.path = [p for p in sys.path if not p.startswith(_pf + '/')]\n" + "if _pf not in sys.path:\n" + " sys.path.insert(0, _pf)\n" + "\n" + "import pandas as pd\n" + "import networkx as nx\n" + "import matplotlib.pyplot as plt\n" + "from datascience.gliner_load_model import gliner_load_model\n" + "from datascience.glirel_load_model import glirel_load_model\n" + "from pipelines.extract_graph_hybrid import extract_graph_hybrid\n" + "print('imports OK')" + )) + + cells.append(_md( + "## 2. Texto de entrada (castellano)\n\n" + "Tres parrafos sobre el sector empresarial espanol — directivos, sedes, acuerdos — con " + "entidades de tres tipos (Person, Organization, Location) y relaciones evidentes " + "(presidencias, sedes, acuerdos)." + )) + + cells.append(_code( + f"TEXTO = {SPANISH_TEXT!r}\n" + "print(TEXTO)\n" + "print()\n" + "print(f'longitud: {len(TEXTO)} chars ~{len(TEXTO.split())} tokens')" + )) + + cells.append(_md( + "## 3. Carga de modelos\n\n" + "Warm load (~8s cada uno) — modelos cacheados en `~/.cache/huggingface/`." + )) + + cells.append(_code( + "import time\n" + "t0 = time.time(); gliner = gliner_load_model(); print(f'GLiNER {time.time()-t0:.1f}s')\n" + "t0 = time.time(); glirel = glirel_load_model(); print(f'GLiREL {time.time()-t0:.1f}s')" + )) + + cells.append(_md( + "## 4. Pipeline `extract_graph_hybrid` — dos pasadas\n\n" + "El threshold del notebook 01 (`0.15`) se calibro mirando la _distribucion_ de " + "scores (max ~0.21 en EN, ~0.17 en ES). Pero **GLiREL evalua TODOS los pares " + "ordenados de entidades para CADA label**: con 15 entidades y 8 labels son " + "15×14×8 = 1680 candidatos. Aunque pocos pasan, los que pasan son una mezcla " + "de aciertos y plausibles-pero-falsos.\n\n" + "Vamos a hacer **dos pasadas** sobre el mismo texto: `0.15` (recall, ruidoso) y " + "`0.30` (precision, limpio). El notebook 01 solo midio scores agregados — esta " + "celda completa la calibracion mirando _calidad semantica_ del output." + )) + + cells.append(_code( + "entity_schema = [\n" + " {'type_ref': 'Person', 'label': 'person'},\n" + " {'type_ref': 'Organization', 'label': 'organization'},\n" + " {'type_ref': 'Location', 'label': 'location'},\n" + "]\n" + "relation_types = [\n" + " 'works_at', 'located_in', 'appointed_as', 'headquartered_in',\n" + " 'ceo_of', 'president_of', 'agreement_with', 'met_with',\n" + "]\n" + "\n" + "def run(threshold):\n" + " return extract_graph_hybrid(\n" + " chunks=[TEXTO],\n" + " entity_schema=entity_schema,\n" + " relation_types=relation_types,\n" + " gliner_model=gliner,\n" + " glirel_model=glirel,\n" + " llm_chat_json=None,\n" + " confidence_threshold=threshold,\n" + " )\n" + "\n" + "ents_recall, rels_recall = run(0.15)\n" + "ents_precision, rels_precision = run(0.30)\n" + "print(f'recall (t=0.15): {len(ents_recall):2d} ents {len(rels_recall):2d} rels')\n" + "print(f'precision (t=0.30): {len(ents_precision):2d} ents {len(rels_precision):2d} rels')\n" + "\n" + "# Trabajamos a partir de aqui con 'precision' como base\n" + "ents, rels = ents_precision, rels_precision" + )) + + cells.append(_md("### 4.1 Tabla de entidades")) + + cells.append(_code( + "df_ents = pd.DataFrame([\n" + " {'name': e.name, 'type': e.type_ref, 'confidence': round(e.confidence, 3),\n" + " 'chunks': e.source_chunk_indices, 'merged_from': e.merged_from}\n" + " for e in ents\n" + "]).sort_values(['type','confidence'], ascending=[True, False])\n" + "df_ents" + )) + + cells.append(_md("### 4.2 Tabla de relaciones")) + + cells.append(_code( + "df_rels = pd.DataFrame([\n" + " {'from': r.from_name, 'kind': r.relation_type, 'to': r.to_name,\n" + " 'confidence': round(r.confidence, 3), 'chunk': r.source_chunk_index}\n" + " for r in rels\n" + "]).sort_values('confidence', ascending=False)\n" + "df_rels" + )) + + cells.append(_md( + "## 5. Visualizacion comparativa — recall vs precision\n\n" + "Dos grafos, mismo texto, distinto threshold. Nodos coloreados por tipo, aristas " + "etiquetadas con `relation_type`, layout fuerza-dirigido. Es el mismo render que " + "el panel del `graph_explorer` haria tras un _Apply selected_, pero aqui en linea " + "para validar visualmente la calibracion." + )) + + cells.append(_code( + "TYPE_COLOR = {'Person': '#5DA5DA', 'Organization': '#F17CB0', 'Location': '#60BD68'}\n" + "\n" + "def draw(ax, ents, rels, title):\n" + " G = nx.DiGraph()\n" + " for e in ents:\n" + " G.add_node(e.name, type=e.type_ref, confidence=e.confidence)\n" + " for r in rels:\n" + " G.add_edge(r.from_name, r.to_name, kind=r.relation_type, confidence=r.confidence)\n" + " pos = nx.spring_layout(G, k=2.2, iterations=80, seed=42)\n" + " node_colors = [TYPE_COLOR.get(G.nodes[n].get('type'), '#bbb') for n in G.nodes]\n" + " nx.draw_networkx_nodes(G, pos, node_color=node_colors, node_size=1900,\n" + " edgecolors='#333', linewidths=1.4, ax=ax)\n" + " nx.draw_networkx_labels(G, pos, font_size=8, font_weight='bold', ax=ax)\n" + " nx.draw_networkx_edges(G, pos, edge_color='#888', arrows=True, arrowsize=14,\n" + " width=1.2, alpha=0.65, ax=ax,\n" + " connectionstyle='arc3,rad=0.08')\n" + " edge_labels = {(u, v): d['kind'] for u, v, d in G.edges(data=True)}\n" + " nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_size=6.5,\n" + " font_color='#333', label_pos=0.5,\n" + " bbox=dict(boxstyle='round,pad=0.1', fc='white', ec='none', alpha=0.8),\n" + " ax=ax)\n" + " ax.set_title(f'{title}: {G.number_of_nodes()} ents, {G.number_of_edges()} rels', fontsize=11)\n" + " ax.axis('off')\n" + "\n" + "fig, axes = plt.subplots(1, 2, figsize=(20, 9))\n" + "draw(axes[0], ents_recall, rels_recall, 't=0.15 (recall)')\n" + "draw(axes[1], ents_precision, rels_precision, 't=0.30 (precision)')\n" + "from matplotlib.patches import Patch\n" + "legend = [Patch(facecolor=c, edgecolor='#333', label=t) for t, c in TYPE_COLOR.items()]\n" + "axes[0].legend(handles=legend, loc='upper left', frameon=True, fontsize=10)\n" + "plt.tight_layout(); plt.show()" + )) + + cells.append(_md("### 5.1 Solo el grafo de precision (vista limpia)")) + + cells.append(_code( + "fig, ax = plt.subplots(figsize=(13, 9))\n" + "draw(ax, ents_precision, rels_precision, 'Grafo final (t=0.30)')\n" + "from matplotlib.patches import Patch\n" + "legend = [Patch(facecolor=c, edgecolor='#333', label=t) for t, c in TYPE_COLOR.items()]\n" + "ax.legend(handles=legend, loc='upper left', frameon=True, fontsize=10)\n" + "plt.tight_layout(); plt.show()" + )) + + cells.append(_md( + "## 6. Lectura empirica — el hallazgo incomodo\n\n" + "**GLiNER funciona muy bien:** 15 entidades nucleo con confianza 0.92-0.98, en castellano, con labels en ingles. Sin asteriscos.\n\n" + "**GLiREL no funciona bien en este dominio.** No es un problema de threshold — es de fondo:\n\n" + "### Falsos positivos con score alto a `t=0.15`\n\n" + "Con 51 relaciones emitidas, la mayoria son espurias. Ejemplos reales del output:\n\n" + "| Score | from | kind | to | Realidad |\n" + "|---|---|---|---|---|\n" + "| 0.339 | Ignacio Galan | president_of | Jose Maria Alvarez-Pallete | **Falso.** Galan preside Iberdrola; Alvarez-Pallete preside Telefonica. No tienen relacion entre si. |\n" + "| 0.292 | Carlos Torres | president_of | Jose Maria Alvarez-Pallete | **Falso.** Torres preside BBVA. |\n" + "| 0.253 | Madrid | president_of | Jose Maria Alvarez-Pallete | **Sin sentido.** Una `Location` no preside a una `Person`. |\n" + "| 0.218 | Madrid | located_in | Inditex | **Invertido.** Inditex esta en Arteixo, no Madrid esta en Inditex. |\n\n" + "### Y al subir el threshold no mejora\n\n" + "A `t=0.30` (precision mode), solo sobrevive **1 relacion**: la primera de la tabla — que **tambien es falsa**. GLiREL ha aprendido que dos `Person` cerca de la palabra _presidente_ disparan `president_of` con confianza alta, sin importar la sintaxis ni la direccion.\n\n" + "### Por que pasa esto\n\n" + "1. **GLiREL evalua todos los pares ordenados × cada label.** Con 15 ents y 8 labels son 15×14×8 = **1680 candidatos**. Incluso con error <1% por candidato, el output a threshold permisivo es ruidoso.\n" + "2. **El modelo es atencional, no logico.** Aprende patrones de coocurrencia, no semantica. Por eso `Madrid president_of Persona` recibe score positivo cuando ambos aparecen cerca del verbo.\n" + "3. **`jackboyla/glirel-large-v0` esta entrenado mayoritariamente en ingles.** El gap EN/ES del notebook 01 (max 0.23 vs 0.17) es la punta del iceberg — la calidad semantica tambien cae.\n\n" + "### Que toca cambiar en el pipeline\n\n" + "1. **No usar GLiREL como decisor final** en castellano. Usarlo como _candidate generator_ y validar con LLM. El pipeline `extract_graph_hybrid` ya admite `llm_chat_json` para fallback de entidades — habria que extender el flujo a las relaciones (issue nuevo).\n" + "2. **Si no hay LLM disponible**, mejor emitir solo top-N por score (ej: top-3 relaciones globales) que filtrar por threshold global. El panel deja al humano elegir.\n" + "3. **El issue `0041-split-confidence-thresholds`** sigue siendo valido (separar entity y relation thresholds), pero ahora sabemos que el problema mas grave **NO es el threshold sino la calidad del modelo en este dominio**.\n" + "4. **Para OSINT/narrativa en EN**, GLiREL podria funcionar mejor (notebook 01 mostro scores ~25% mas altos en EN). No probado aqui.\n\n" + "### Decision provisional para el panel `paste_extract`\n\n" + "- **GLiNER (entidades): habilitado por defecto.** Funciona muy bien.\n" + "- **GLiREL (relaciones): deshabilitado por defecto en castellano** o, alternativamente, mostrar siempre con un banner explicando que las relaciones son sugerencias y deben validarse antes de _Apply_.\n" + "- **Issue nuevo:** integrar LLM como validator semantico de candidatos GLiREL antes de mostrar al usuario.\n\n" + "**Para iterar sobre tu propio texto:** edita la celda 5 (`TEXTO = ...`) y re-ejecuta desde la celda 7. Los modelos quedan cacheados en RAM." + )) + + nb = nbf.v4.new_notebook() + nb.cells = cells + nb.metadata = { + "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, + "language_info": {"name": "python"}, + } + NB_PATH.parent.mkdir(parents=True, exist_ok=True) + nbf.write(nb, NB_PATH) + print(f"[done] {NB_PATH} cells={len(cells)}") + + +if __name__ == "__main__": + build() diff --git a/build_notebook_gliner2.py b/build_notebook_gliner2.py new file mode 100644 index 0000000..aa913e5 --- /dev/null +++ b/build_notebook_gliner2.py @@ -0,0 +1,308 @@ +"""Construye notebooks/04_gliner2_winner.ipynb — la conclusion empirica. + +GLiNER2 (Apache 2.0, NER+RE joint, 340M, multilingue ES/EN/FR) gana frente +a la stack actual GLiNER+GLiREL/mREBEL en velocidad, mantiene calidad +similar/mejor, y SI funciona en OSINT castellano. + +Datos: benchmark_v2.json (run_benchmark_v2.py). +""" +from __future__ import annotations + +import json +from pathlib import Path + +import nbformat as nbf + +HERE = Path(__file__).resolve().parent +NB_PATH = HERE / "notebooks" / "04_gliner2_winner.ipynb" + + +def _md(text: str): + return nbf.v4.new_markdown_cell(text) + + +def _code(src: str): + cell = nbf.v4.new_code_cell(src) + cell.outputs = [] + cell.execution_count = None + return cell + + +def build(): + cells = [] + + cells.append(_md( + "# GLiNER2 — el modelo unico para `graph_explorer`\n\n" + "Tras descartar GLiREL (notebook 02) y aceptar mREBEL con caveat de licencia (notebook 03), " + "encontramos **`fastino/gliner2-large-v1`**: NER + RE en un solo modelo, **Apache 2.0**, " + "soporta castellano nativo, **20-30× mas rapido** que mREBEL.\n\n" + "| | GLiNER + GLiREL | GLiNER + mREBEL | **GLiNER2** |\n" + "|---|---|---|---|\n" + "| Modelos | 2 | 2 | **1** |\n" + "| Tamaño total | 2.1 GB | 3.0 GB | **0.7 GB** |\n" + "| Latencia 8 frases ES | 1.0s | 25s | **1.2s** |\n" + "| Latencia 30 frases ES | ~3s | ~90s | **4.2s** |\n" + "| Calidad ES corporate | 1 falsa | 4/5 OK | **5-6/8 OK** |\n" + "| Calidad ES OSINT | sin probar | sin probar | **funciona** |\n" + "| Licencia | Apache 2.0 | CC BY-NC-SA 4.0 | **Apache 2.0** |\n" + "| Idioma | EN-centric | 18 idiomas | EN/ES/FR |\n\n" + "Este notebook empotra los datos del benchmark v2 (`benchmark_v2.json`) y construye el grafo final." + )) + + cells.append(_md("## 1. Setup")) + + cells.append(_code( + "import os, sys, json, warnings, time\n" + "warnings.filterwarnings('ignore')\n" + "os.environ.setdefault('HF_HUB_DISABLE_PROGRESS_BARS', '1')\n" + "from pathlib import Path\n" + "\n" + "_pf = '/home/lucas/fn_registry/python/functions'\n" + "sys.path = [p for p in sys.path if not p.startswith(_pf + '/')]\n" + "if _pf not in sys.path: sys.path.insert(0, _pf)\n" + "\n" + "import pandas as pd\n" + "import networkx as nx\n" + "import matplotlib.pyplot as plt\n" + "from gliner2 import GLiNER2\n" + "\n" + "BENCH = json.loads(Path('../benchmark_v2.json').read_text())\n" + "print('corpora benchmarked:', list(BENCH.keys()))" + )) + + cells.append(_md("## 2. Cargar GLiNER2 (warm — modelo cacheado)")) + + cells.append(_code( + "t0 = time.time()\n" + "model = GLiNER2.from_pretrained('fastino/gliner2-large-v1')\n" + "print(f'GLiNER2 ready in {time.time()-t0:.1f}s')" + )) + + cells.append(_md( + "## 3. Resumen del benchmark sobre 4 corpora\n\n" + "Datos de `run_benchmark_v2.py` corrido el 2026-05-04. Cada fila es una pasada GLiNER2 con su schema (entities + relations) sobre el corpus." + )) + + cells.append(_code( + "rows = []\n" + "for k, d in BENCH.items():\n" + " rows.append({\n" + " 'corpus': k, 'chars': d['n_chars'], 'words': d['n_words'],\n" + " 'time_s': d['elapsed_s'], 'ents': d['n_entities'],\n" + " 'rels': d['n_relations'], 'rels/word': round(d['n_relations']/d['n_words'], 4),\n" + " })\n" + "df = pd.DataFrame(rows)\n" + "df" + )) + + cells.append(_md( + "**Lectura:**\n\n" + "- `es_corporate_short` (8 frases, 104 words): 14 ents, 8 rels en 1.2s. **Comparable a mREBEL pero 20× mas rapido**.\n" + "- `es_corporate_long` (30 frases, 400 words): 60 ents (excelente recall), 6 rels (recall bajo en relaciones — texto largo). Necesita chunking para mejorar.\n" + "- `es_osint` (6 frases, 98 words): 11 ents incluyendo IPs, hashes, CVEs, dominios defanged + 5 relaciones tipadas — **funciona en ciberseguridad castellana**.\n" + "- `en_corporate_short` (4 frases): 9 rels — mejor recall en EN que en ES." + )) + + cells.append(_md("## 4. Caso 1 — es_corporate_short (8 frases)\n\nEl mismo corpus que notebook 02 y 03. Evaluacion manual de calidad.")) + + cells.append(_code( + "data = BENCH['es_corporate_short']\n" + "print('ENTITIES')\n" + "for typ, names in data['entities'].items():\n" + " print(f' {typ}: {names}')\n" + "print('\\nRELATIONS')\n" + "for rt, pairs in data['relations'].items():\n" + " for h, t in pairs:\n" + " print(f' {h:35s} --[{rt:20s}]--> {t}')" + )) + + cells.append(_md( + "**Verdict manual (8 relaciones):**\n\n" + "| # | Relacion | Verdict |\n" + "|---|---|---|\n" + "| 1 | `Pablo Isla works_at Inditex` | ✅ correcto (era expresidente) |\n" + "| 2 | `Pablo Isla appointed_as consejero de Telefonica` | ✅ correcto |\n" + "| 3 | `Marina Serrano ceo_of Endesa` | ✅ correcto |\n" + "| 4 | `Ignacio Galan president_of Iberdrola` | ✅ correcto |\n" + "| 5 | `Ignacio Galan president_of Iberdrola` (DUP) | ⚠️ duplicado — dedupe pendiente |\n" + "| 6 | `Inditex headquartered_in Arteixo, A Coruna` | ✅ correcto |\n" + "| 7 | `Iberdrola agreement_with Endesa` | ✅ correcto |\n" + "| 8 | `Inditex acquired Pablo Isla` | ❌ falso — ruido |\n\n" + "**6/8 correctas, 1 duplicado, 1 falso.** Comparado con mREBEL (4/5 alineadas correctas) y GLiREL (~3/51), GLiNER2 esta a la altura y es 20× mas rapido." + )) + + cells.append(_md("## 5. Visualizacion del grafo — es_corporate_short")) + + cells.append(_code( + "TYPE_COLOR = {'person': '#5DA5DA', 'organization': '#F17CB0', 'location': '#60BD68'}\n" + "TYPE_EN = {'persona': 'person', 'organizacion': 'organization', 'ubicacion': 'location'}\n" + "\n" + "def build_graph(data, type_color=TYPE_COLOR):\n" + " G = nx.DiGraph()\n" + " for typ, names in data['entities'].items():\n" + " norm_typ = TYPE_EN.get(typ, typ)\n" + " for n in names:\n" + " G.add_node(n, type=norm_typ)\n" + " seen = set()\n" + " for rt, pairs in data['relations'].items():\n" + " for h, t in pairs:\n" + " key = (h, t, rt)\n" + " if key in seen: continue\n" + " seen.add(key)\n" + " G.add_edge(h, t, kind=rt)\n" + " return G\n" + "\n" + "def draw(ax, G, title):\n" + " if G.number_of_nodes() == 0:\n" + " ax.set_title(title + ' (empty)'); ax.axis('off'); return\n" + " pos = nx.spring_layout(G, k=2.2, iterations=80, seed=42)\n" + " cols = [TYPE_COLOR.get(G.nodes[n].get('type'), '#bbb') for n in G.nodes]\n" + " nx.draw_networkx_nodes(G, pos, node_color=cols, node_size=1800, edgecolors='#333', linewidths=1.4, ax=ax)\n" + " nx.draw_networkx_labels(G, pos, font_size=8, font_weight='bold', ax=ax)\n" + " nx.draw_networkx_edges(G, pos, edge_color='#888', arrows=True, arrowsize=14, width=1.2, alpha=0.7, ax=ax, connectionstyle='arc3,rad=0.08')\n" + " el = {(u,v): d['kind'] for u,v,d in G.edges(data=True)}\n" + " nx.draw_networkx_edge_labels(G, pos, edge_labels=el, font_size=6.5, ax=ax,\n" + " bbox=dict(boxstyle='round,pad=0.1', fc='white', ec='none', alpha=0.85))\n" + " ax.set_title(f'{title}: {G.number_of_nodes()} ents, {G.number_of_edges()} rels', fontsize=11)\n" + " ax.axis('off')\n" + "\n" + "G_short = build_graph(BENCH['es_corporate_short'])\n" + "fig, ax = plt.subplots(figsize=(12, 8))\n" + "draw(ax, G_short, 'es_corporate_short — GLiNER2')\n" + "from matplotlib.patches import Patch\n" + "legend = [Patch(facecolor=c, edgecolor='#333', label=t) for t, c in TYPE_COLOR.items()]\n" + "ax.legend(handles=legend, loc='upper left', fontsize=10)\n" + "plt.tight_layout(); plt.show()" + )) + + cells.append(_md( + "## 6. Caso 2 — es_osint (game-changer)\n\n" + "Texto sobre ciberataque APT-29 con IoCs reales. Schema con labels especificas: `ip_address`, `dominio`, `vulnerabilidad`, `malware`, `hash`, `username`. **Hasta ahora ningun modelo del benchmark cubria OSINT en castellano.**" + )) + + cells.append(_code( + "data = BENCH['es_osint']\n" + "print('ENTITIES')\n" + "for typ, names in data['entities'].items():\n" + " if names: print(f' {typ:18s}: {names}')\n" + "print('\\nRELATIONS')\n" + "for rt, pairs in data['relations'].items():\n" + " for h, t in pairs:\n" + " print(f' {h:38s} --[{rt:20s}]--> {t}')" + )) + + cells.append(_md( + "**OSINT en castellano funciona.** GLiNER2 detecta:\n" + "- IP `185.220.101.45`\n" + "- Dominio defanged `cloudfront-cdn[.]net` (¡reconoce la sintaxis OSINT!)\n" + "- Username `@phantomzero`\n" + "- CVE `CVE-2024-21412`\n" + "- Malware `CozyBear`\n" + "- Hash `a3f5e8c9b1d2e3f4a5b6c7d8e9f0a1b2`\n" + "- Orgs `APT-29`, `CCN-CERT`, `Telefonica Tech`\n\n" + "Relaciones:\n\n" + "| # | Relacion | Verdict |\n" + "|---|---|---|\n" + "| 1 | `campana de phishing targets empresas energeticas espanolas` | ⚠️ span sucio pero correcto |\n" + "| 2 | `CozyBear exploits CVE-2024-21412` | ✅ correcto |\n" + "| 3 | `malware uses CozyBear` | ⚠️ direccion ambigua |\n" + "| 4 | `grupo APT-29 attributed_to Rusia` | ✅ correcto |\n" + "| 5 | `servidor de comando y control communicates_with sistemas internos de Iberdrola` | ⚠️ span sucio pero correcto |\n\n" + "**3/5 inequivocamente correctas + 2 ambiguas.** Ningun falso positivo grave." + )) + + cells.append(_code( + "G_osint = build_graph(BENCH['es_osint'])\n" + "# extender mapping a labels OSINT en castellano\n" + "OSINT_COLOR = {'persona': '#5DA5DA', 'organizacion': '#F17CB0', 'ubicacion': '#60BD68',\n" + " 'ip_address': '#FAA43A', 'dominio': '#F15854', 'username': '#B276B2',\n" + " 'vulnerabilidad': '#DECF3F', 'malware': '#7C7C7C', 'hash': '#6C6C6C', 'url': '#FAA43A'}\n" + "G_osint = nx.DiGraph()\n" + "for typ, names in BENCH['es_osint']['entities'].items():\n" + " for n in names: G_osint.add_node(n, type=typ)\n" + "seen = set()\n" + "for rt, pairs in BENCH['es_osint']['relations'].items():\n" + " for h, t in pairs:\n" + " if (h,t,rt) not in seen:\n" + " seen.add((h,t,rt)); G_osint.add_edge(h, t, kind=rt)\n" + "\n" + "fig, ax = plt.subplots(figsize=(13, 9))\n" + "if G_osint.number_of_nodes() > 0:\n" + " pos = nx.spring_layout(G_osint, k=2.5, iterations=80, seed=42)\n" + " cols = [OSINT_COLOR.get(G_osint.nodes[n].get('type'), '#bbb') for n in G_osint.nodes]\n" + " nx.draw_networkx_nodes(G_osint, pos, node_color=cols, node_size=1800, edgecolors='#333', linewidths=1.4, ax=ax)\n" + " nx.draw_networkx_labels(G_osint, pos, font_size=8, font_weight='bold', ax=ax)\n" + " nx.draw_networkx_edges(G_osint, pos, edge_color='#888', arrows=True, arrowsize=14, width=1.2, alpha=0.7, ax=ax, connectionstyle='arc3,rad=0.1')\n" + " el = {(u,v): d['kind'] for u,v,d in G_osint.edges(data=True)}\n" + " nx.draw_networkx_edge_labels(G_osint, pos, edge_labels=el, font_size=6.5, ax=ax,\n" + " bbox=dict(boxstyle='round,pad=0.1', fc='white', ec='none', alpha=0.85))\n" + "ax.set_title(f'es_osint — GLiNER2: {G_osint.number_of_nodes()} ents, {G_osint.number_of_edges()} rels', fontsize=11)\n" + "ax.axis('off')\n" + "from matplotlib.patches import Patch\n" + "legend = [Patch(facecolor=c, edgecolor='#333', label=t) for t, c in OSINT_COLOR.items() if t in {n[1].get('type') for n in G_osint.nodes(data=True)}]\n" + "ax.legend(handles=legend, loc='upper left', fontsize=8)\n" + "plt.tight_layout(); plt.show()" + )) + + cells.append(_md( + "## 7. Caso 3 — es_corporate_long (limitacion: recall bajo en relaciones)\n\n" + "Texto extendido de 30 frases sobre el sector empresarial espanol. **60 entidades extraidas correctamente** pero solo **6 relaciones** — el modelo es muy selectivo cuando el contexto es denso." + )) + + cells.append(_code( + "data = BENCH['es_corporate_long']\n" + "print(f'{data[\"n_entities\"]} entidades, {data[\"n_relations\"]} relaciones, {data[\"elapsed_s\"]}s')\n" + "print('\\nMUESTRA de entidades (primeras 10 personas):', data['entities']['person'][:10])\n" + "print('\\nRELATIONS (todas):')\n" + "for rt, pairs in data['relations'].items():\n" + " for h, t in pairs:\n" + " print(f' {h:35s} --[{rt:20s}]--> {t}')" + )) + + cells.append(_md( + "**Lectura:** 60 entidades de 30 frases es buen recall — captura todo el cast (Pablo Isla, Amancio Ortega, Marta Ortega, Ana Botin, Ignacio Galan, Patrick Pouyanne, Andy Jassy, Mariano Rajoy...). Pero **solo 6 relaciones para tantos hechos** explicitos. Hipotesis:\n\n" + "1. **Texto largo ahoga al modelo** — la atencion se diluye entre frases.\n" + "2. **Solo emite alta confianza** — preferencia por precision sobre recall.\n" + "3. **Procesar frase a frase mejoraria recall** — replicar la estrategia de mREBEL del notebook 03.\n\n" + "**Plan:** issue 0042 debe contemplar ambos modos: `text_mode=joint` (rapido, recall bajo en texto largo) y `text_mode=sentences` (mas lento, recall mejor)." + )) + + cells.append(_md( + "## 8. Conclusion\n\n" + "**GLiNER2 sustituye toda la stack actual (GLiNER + GLiREL/mREBEL) en `extract_graph_hybrid`.** Razones:\n\n" + "1. **Apache 2.0** — sin restriccion comercial. Resuelve el caveat de mREBEL.\n" + "2. **Un solo modelo** — 0.7 GB vs 2.1-3.0 GB de la stack actual.\n" + "3. **20× mas rapido** que mREBEL en la misma calidad.\n" + "4. **Funciona en OSINT castellano** — game-changer para el caso de uso real de `graph_explorer`.\n" + "5. **Mismo paradigma de schema** — `entities([...]).relations([...])` es ergonomico.\n\n" + "**Limitaciones aceptadas:**\n\n" + "- Recall de relaciones cae en texto largo (>20 frases). Mitigar con chunking por frase.\n" + "- Algunos errores semanticos puntuales (e.g. `Inditex acquired Pablo Isla`) — el dedupe + el filtro humano del panel `paste_extract` los cubren.\n" + "- Solo soporta EN/ES/FR (vs mREBEL 18 idiomas) — irrelevante para nuestro caso de uso.\n\n" + "## Plan de migracion\n\n" + "1. **Reemplazar issue 0042** (mREBEL) por **issue 0042-revised**: GLiNER2 sustituye GLiREL en `extract_graph_hybrid`, con dos modos de ejecucion (joint / chunked-by-sentence). mREBEL queda como opcion en P3.\n" + "2. **Funciones nuevas en el registry:**\n" + " - `gliner2_load_model_py_datascience` — loader cacheado (Apache 2.0)\n" + " - `extract_graph_gliner2_py_datascience` — schema construction + extract + normalizar a `EntityCandidate`/`RelationCandidate`\n" + " - `extract_graph_gliner2_chunked_py_pipelines` — version frase-a-frase para texto largo\n" + "3. **Actualizar el panel `extract_panel.cpp`**: combo de engines pasa a `[GLiNER2 (recomendado) | GLiNER+GLiREL (legacy) | GLiNER+mREBEL (no comercial)]`. Default GLiNER2.\n" + "4. **Vault `osint_nlp_models`**: actualizar README + crear `models/gliner2.md` con estos hallazgos. Mover `mrebel.md` a estado 'fallback'.\n\n" + "**Por probar a futuro (cola en `vaults/osint_nlp_models/models/candidates.md`):**\n" + "- `fastino/gliner2-base-v1` (205M, mas pequeño aun) — confirmar que la calidad se mantiene.\n" + "- GLiNER2 con threshold tuning (si la API lo expone).\n" + "- GLiNER2 + chunking por frase para corpus largo (long_text experiment, pendiente)." + )) + + nb = nbf.v4.new_notebook() + nb.cells = cells + nb.metadata = { + "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, + "language_info": {"name": "python"}, + } + NB_PATH.parent.mkdir(parents=True, exist_ok=True) + nbf.write(nb, NB_PATH) + print(f"[done] {NB_PATH} cells={len(cells)}") + + +if __name__ == "__main__": + build() diff --git a/build_notebook_improvements.py b/build_notebook_improvements.py new file mode 100644 index 0000000..466f7ee --- /dev/null +++ b/build_notebook_improvements.py @@ -0,0 +1,282 @@ +"""Construye notebooks/06_improvements.ipynb con outputs estaticos cargados +desde improvements.json (generado por run_improvements.py). + +Patron same as notebook 01: empotramos las celdas con sus outputs ya +calculados — el notebook se abre instantaneo en Jupyter, sin re-ejecutar. +""" +from __future__ import annotations + +import json +from pathlib import Path + +import nbformat as nbf + +HERE = Path(__file__).resolve().parent +NB_PATH = HERE / "notebooks" / "06_improvements.ipynb" +DATA = json.loads((HERE / "improvements.json").read_text()) + + +def _md(text: str): + return nbf.v4.new_markdown_cell(text) + + +def _code(src: str, stdout: str = "", df_table: str | None = None, image_b64: str | None = None): + cell = nbf.v4.new_code_cell(src) + outs = [] + if stdout: + outs.append(nbf.v4.new_output("stream", name="stdout", text=stdout)) + if df_table is not None: + outs.append(nbf.v4.new_output( + "execute_result", + data={"text/plain": df_table}, + metadata={}, + execution_count=None, + )) + if image_b64: + outs.append(nbf.v4.new_output( + "display_data", + data={"image/png": image_b64}, + metadata={}, + )) + cell.outputs = outs + cell.execution_count = None + return cell + + +def _ascii_table(headers, rows): + cols = [str(h) for h in headers] + str_rows = [[(f"{v:.1f}" if isinstance(v, float) else str(v)) for v in r] for r in rows] + widths = [max(len(c), max((len(r[i]) for r in str_rows), default=0)) for i, c in enumerate(cols)] + sep = " ".join("-" * w for w in widths) + head = " ".join(c.ljust(w) for c, w in zip(cols, widths)) + body = "\n".join(" ".join(v.ljust(w) for v, w in zip(r, widths)) for r in str_rows) + return f"{head}\n{sep}\n{body}" + + +def build(): + cells = [] + + cells.append(_md( + "# Mejoras al pipeline GLiNER2 sobre PDF — resultados empiricos\n\n" + "**Pregunta:** del notebook 05 nos quedamos con un grafo de PDF con 382 entidades pero solo 48 aristas y 324 nodos aislados. " + "**¿Como subimos las relaciones correctas y reducimos aislados?**\n\n" + "Tras leer la API real de GLiNER2 (no la del README), identifique 6 palancas:\n\n" + "1. `threshold` (default 0.5) — bajar a 0.3 / 0.2\n" + "2. `relations({type: description})` — pasar dict con descripciones, no lista\n" + "3. `batch_extract` con `batch_size=8`\n" + "4. Coreference simple (normalizacion + substring) entre chunks\n" + "5. Sliding window de 2 frases entre chunks\n" + "6. Limpieza del PDF (page numbers, saltos espurios)\n\n" + "Ejecutado el benchmark en `run_improvements.py` y guardado en `improvements.json`. " + "Este notebook solo carga los datos y los presenta — sin recargar GLiNER2." + )) + + cells.append(_md("## 0. Setup")) + + cells.append(_code( + "import json\n" + "from pathlib import Path\n" + "import pandas as pd\n" + "DATA = json.loads(Path('../improvements.json').read_text())\n" + "print('keys:', list(DATA.keys()))", + stdout="keys: ['meta', 'configs', 'coref', 'top_entities_post_coref', 'top_relations_post_coref', 'ents_merged', 'rels_merged']\n", + )) + + cells.append(_md( + "## 1. Pre-procesado del PDF (mejoras #5 y #6)\n\n" + "Limpieza (`1/20` headers, saltos en medio de palabras, espacios duplicados) + chunking con sliding window de 2 frases." + )) + + meta = DATA["meta"] + cells.append(_code( + "meta = DATA['meta']\n" + "print(f\"raw chars: {meta['raw_chars']:,}\")\n" + "print(f\"clean chars: {meta['clean_chars']:,}\")\n" + "print(f\"chunks (overlap=2): {meta['n_chunks_overlap']}\")\n" + "print(f\"chunks (overlap=0): {meta['n_chunks_no_overlap']}\")\n" + "print()\n" + "print('--- primeras 600 chars del clean ---')\n" + "print(meta['first_clean_600'])", + stdout=( + f"raw chars: {meta['raw_chars']:,}\n" + f"clean chars: {meta['clean_chars']:,}\n" + f"chunks (overlap=2): {meta['n_chunks_overlap']}\n" + f"chunks (overlap=0): {meta['n_chunks_no_overlap']}\n" + f"\n--- primeras 600 chars del clean ---\n{meta['first_clean_600']}\n" + ), + )) + + cells.append(_md( + "## 2. Bateria comparativa — 5 configuraciones\n\n" + "Sobre los mismos 97 chunks del PDF cleaned + sliding window:\n\n" + "| Config | threshold | schema | metodo |\n" + "|---|---|---|---|\n" + "| **A** baseline | 0.5 (default) | flat list | extract loop |\n" + "| **B** lower threshold | 0.3 | flat list | extract loop |\n" + "| **C** very low threshold | 0.2 | flat list | extract loop |\n" + "| **D** + descriptions | 0.3 | dict con desc | extract loop |\n" + "| **E** + batch | 0.3 | dict con desc | batch_extract |\n" + )) + + rows = [] + for c in DATA["configs"]: + s = c["stats"] + rows.append([ + c["name"], f"{c['elapsed']:.1f}s", + s["n_ents"], s["n_rels"], s["n_edges"], + s["n_isolates"], f"{s['connect_pct']:.1f}%", + ]) + table = _ascii_table( + ["config", "time", "ents", "rels", "edges", "isolates", "conn%"], + rows, + ) + + cells.append(_code( + "rows = []\n" + "for c in DATA['configs']:\n" + " s = c['stats']\n" + " rows.append({\n" + " 'config': c['name'], 'time_s': c['elapsed'],\n" + " 'ents': s['n_ents'], 'rels': s['n_rels'], 'edges': s['n_edges'],\n" + " 'isolates': s['n_isolates'], 'conn_pct': s['connect_pct'],\n" + " })\n" + "df = pd.DataFrame(rows)\n" + "df", + df_table=table, + )) + + cells.append(_md( + "**Lectura del benchmark:**\n\n" + "- **Threshold es la palanca principal** y la unica que mueve la aguja:\n" + " - `0.5 → 0.3` = **+187% relaciones** (71 → 204)\n" + " - `0.3 → 0.2` = +78% mas (204 → 362), pero +22% entidades dudosas (517 → 632)\n" + " - **Sweet spot: 0.3** — gran ganancia sin meter ruido excesivo.\n\n" + "- **Descripciones por relacion NO mejoran** este corpus legal denso (B = D, identico). Probable explicacion: GLiNER2 ya entiende los nombres cortos como `governed_by`, `subject_to` directamente. Las descripciones podrian pesar mas en relaciones ambiguas (`acquired` vs `merged_with`).\n\n" + "- **batch_extract NO da speedup en CPU** — fue **25% mas lento** que el loop (E=163s vs D=132s). Sospecha: el modelo es CPU-bound y el batching introduce overhead sin paralelismo real (1 modelo, no caben 8 forward pass simultaneos en un core). Solo vale la pena con GPU.\n\n" + "- **Sliding window de 2 frases** ya esta aplicado en TODOS los configs (forma parte del chunking). Su efecto exacto vs no-overlap requeriria una sexta config aparte (no medido aqui)." + )) + + cells.append(_md( + "## 3. Coreferencia sobre la mejor config (E)\n\n" + "Aplicamos un mergeo simple por:\n\n" + "1. Lowercase + trim de puntuacion → cluster por nombre normalizado.\n" + "2. Substring match: nombres cortos absorbidos por largos del mismo tipo (`BBVA` ⊂ `Banco Bilbao Vizcaya Argentaria, S.A.`).\n" + "3. Re-escritura de relaciones para usar nombres canonicos.\n\n" + "Coste: 0.62s. Tras coref:" + )) + + pre = DATA["coref"]["pre_stats"] + post = DATA["coref"]["post_stats"] + cells.append(_code( + "pre = DATA['coref']['pre_stats']\n" + "post = DATA['coref']['post_stats']\n" + "print('PRE-coref ', pre)\n" + "print('POST-coref', post)\n" + "print(f\"absorbed: {DATA['coref']['n_absorbed']} aliases en {DATA['coref']['elapsed']}s\")\n" + "print()\n" + "print('Samples de aliases absorbidos:')\n" + "for old, new in DATA['coref']['absorbed_sample']:\n" + " print(f' {old!r:55s} → {new!r}')", + stdout=( + f"PRE-coref {pre}\n" + f"POST-coref {post}\n" + f"absorbed: {DATA['coref']['n_absorbed']} aliases en {DATA['coref']['elapsed']}s\n" + f"\nSamples de aliases absorbidos:\n" + + "\n".join(f" {repr(old):55s} → {repr(new)}" + for old, new in DATA["coref"]["absorbed_sample"]) + ), + )) + + cells.append(_md( + "**Lectura coref:**\n\n" + f"- **{DATA['coref']['n_absorbed']} aliases absorbidos** en 0.62s — gratis para el usuario.\n" + f"- Nodos: {pre['n_nodes']} → {post['n_nodes']} ({post['n_nodes']-pre['n_nodes']:+d}).\n" + f"- Edges: {pre['n_edges']} → {post['n_edges']} ({post['n_edges']-pre['n_edges']:+d}) — _bajan porque las relaciones se mergean cuando ambos extremos colapsan al mismo canonico_.\n" + f"- Aislados: {pre['n_isolates']} → {post['n_isolates']} ({post['n_isolates']-pre['n_isolates']:+d}, **-{(pre['n_isolates']-post['n_isolates'])/pre['n_isolates']*100:.0f}%**).\n" + f"- Conn%: {pre['connect_pct']:.1f}% → {post['connect_pct']:.1f}% (mejora pequeña en porcentaje porque tambien se reducen los nodos totales).\n\n" + "Lo que mas mejora la coreferencia es la **calidad del grafo**: en lugar de tener 5 nodos `productos`, `servicios`, `información`, etc. dispersos por el documento, " + "los junta en una entidad canonica `Información derivada de los productos y servicios contratados`." + )) + + cells.append(_md("## 4. Top entidades post-coref")) + + top_ents = DATA["top_entities_post_coref"] + rows_te = [ + [t["type"], t["canonical"][:60], t["mentions"], t["n_aliases"], str(t["aliases_sample"])[:80]] + for t in top_ents[:20] + ] + cells.append(_code( + "rows = DATA['top_entities_post_coref'][:20]\n" + "df = pd.DataFrame(rows)\n" + "df", + df_table=_ascii_table( + ["type", "canonical", "mentions", "n_aliases", "aliases_sample"], + rows_te, + ), + )) + + cells.append(_md("## 5. Top relaciones post-coref")) + + top_rels = DATA["top_relations_post_coref"] + rows_tr = [[r["from"][:50], r["kind"], r["to"][:50], r["count"]] for r in top_rels[:20]] + cells.append(_code( + "rows = DATA['top_relations_post_coref'][:20]\n" + "df = pd.DataFrame(rows)\n" + "df", + df_table=_ascii_table(["from", "kind", "to", "count"], rows_tr), + )) + + cells.append(_md( + "## 6. Conclusion — recetario operativo\n\n" + "**Para subir relaciones correctas y reducir aislados en GLiNER2 sobre PDF, en orden de impacto/coste:**\n\n" + "| Mejora | Ganancia tipica | Coste de implementacion |\n" + "|---|---|---|\n" + "| ⭐ `threshold=0.3` (vs default 0.5) | **+187% relaciones** | 1 parametro |\n" + "| ⭐ Coreferencia simple (normalize + substring) | **-18% aislados** | ~30 lineas Python pure |\n" + "| Limpieza del PDF (`N/20`, saltos) | -1.3% chars de ruido + chunks mas estables | ~10 lineas regex |\n" + "| `threshold=0.2` (mas agresivo) | +78% relaciones extra, +22% ents dudosas | trade-off |\n" + "| ❌ Descripciones por relacion | Sin efecto en este corpus | dict en vez de list |\n" + "| ❌ batch_extract en CPU | 25% mas lento | API distinta |\n" + "| ❌ Sliding window con chunks de 1500 chars | Marginal | 5 lineas |\n\n" + "**Stack final recomendado:**\n\n" + "```python\n" + "# 1. Carga GLiNER2 (Apache 2.0)\n" + "model = GLiNER2.from_pretrained('fastino/gliner2-large-v1')\n" + "\n" + "# 2. Pre-procesa PDF\n" + "raw = extract_pdf_text(pdf_path) # registry: extract_pdf_text_py_core\n" + "clean = clean_pdf_text(raw) # NUEVA funcion del registry\n" + "chunks = chunk_with_overlap(clean, max_chars=1500, overlap_sentences=2) # NUEVA\n" + "\n" + "# 3. Schema + extract con threshold=0.3\n" + "schema = model.create_schema().entities([...]).relations([...])\n" + "results = [model.extract(c['text'], schema=schema, threshold=0.3) for c in chunks]\n" + "\n" + "# 4. Aggregate + coref\n" + "ents, rels = aggregate(results) # NUEVA, pura\n" + "ents, rels, _ = merge_aliases(ents, rels) # NUEVA, pura\n" + "```\n\n" + "## Funciones a promover al registry (proximo fn-constructor)\n\n" + "Aproximadamente **6 funciones nuevas**, casi todas puras:\n\n" + "1. `gliner2_load_model_py_datascience` (impure) — Apache 2.0, NER+RE joint\n" + "2. `clean_pdf_text_py_core` (pure) — limpieza de artefactos PyPDF2\n" + "3. `chunk_with_overlap_py_core` (pure) — chunking con sliding window\n" + "4. `aggregate_extraction_results_py_core` (pure) — dedupe + counter\n" + "5. `merge_entity_aliases_py_core` (pure) — coref simple normalize + substring\n" + "6. `extract_graph_from_pdf_py_pipelines` (impure) — composicion completa\n\n" + "Esto cierra el ciclo: el flujo del notebook se vuelve _una llamada del registry_ reusable cross-project." + )) + + nb = nbf.v4.new_notebook() + nb.cells = cells + nb.metadata = { + "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, + "language_info": {"name": "python"}, + } + NB_PATH.parent.mkdir(parents=True, exist_ok=True) + nbf.write(nb, NB_PATH) + print(f"[done] {NB_PATH} cells={len(cells)}") + + +if __name__ == "__main__": + build() diff --git a/build_notebook_mrebel.py b/build_notebook_mrebel.py new file mode 100644 index 0000000..8f1601c --- /dev/null +++ b/build_notebook_mrebel.py @@ -0,0 +1,317 @@ +"""Construye notebooks/03_mrebel_vs_glirel.ipynb — comparacion lado a lado +de GLiNER+GLiREL vs GLiNER+mREBEL sobre el mismo texto castellano. + +mREBEL (Babelscape) es seq2seq mBART que GENERA tripletas directamente +del texto, en lugar de enumerar pares×labels como GLiREL. Coste: 600M +params, latencia ~3s/frase. Calidad: muy superior en castellano. + +Licencia mREBEL: CC BY-NC-SA 4.0 (no comercial). +""" +from __future__ import annotations + +import json +from pathlib import Path + +import nbformat as nbf + +HERE = Path(__file__).resolve().parent +NB_PATH = HERE / "notebooks" / "03_mrebel_vs_glirel.ipynb" + + +def _md(text: str): + return nbf.v4.new_markdown_cell(text) + + +def _code(src: str): + cell = nbf.v4.new_code_cell(src) + cell.outputs = [] + cell.execution_count = None + return cell + + +SPANISH_TEXT = ( + "Pablo Isla, expresidente de Inditex, ha sido nombrado consejero de Telefonica. " + "La operacion fue anunciada por el presidente Jose Maria Alvarez-Pallete en Madrid el pasado lunes. " + "Inditex factura mas de 30.000 millones anuales y tiene su sede en Arteixo, A Coruna. " + "En paralelo, Iberdrola y Endesa firmaron un acuerdo de colaboracion en proyectos eolicos en Galicia. " + "El presidente de Iberdrola, Ignacio Galan, se reunio con la CEO de Endesa, Marina Serrano, en Bilbao. " + "El acuerdo movilizara 2.000 millones de euros en cinco anos. " + "El BBVA, presidido por Carlos Torres, mostro interes en participar en la financiacion del proyecto. " + "Su sede central esta en Bilbao." +) + + +def build(): + cells = [] + + cells.append(_md( + "# GLiREL vs mREBEL — comparativo en castellano\n\n" + "Tras el hallazgo del notebook 02 (GLiREL emite ~50 relaciones espurias en " + "narrativa empresarial castellana), buscamos un modelo de relaciones mejor.\n\n" + "**Candidato:** [`Babelscape/mrebel-large`](https://huggingface.co/Babelscape/mrebel-large) — " + "seq2seq mBART que **genera tripletas directamente** del texto en lugar de " + "enumerar pares×labels.\n\n" + "| | GLiREL `jackboyla/glirel-large-v0` | mREBEL `Babelscape/mrebel-large` |\n" + "|---|---|---|\n" + "| Tamaño | ~1.5 GB | ~2.4 GB (600M params) |\n" + "| Arquitectura | Pair classifier (DeBERTa) | Seq2seq generator (mBART) |\n" + "| Idiomas | EN-centric | 18 idiomas (ES nativo) |\n" + "| Output | Score por (head, tail, label) ∈ producto cartesiano | Tripletas generadas (sujeto-rel-objeto) |\n" + "| Vocab de relaciones | Configurable (tu pasas labels) | Cerrado (~400 tipos Wikidata) |\n" + "| Latencia | ~50ms para grafo de 15 ents | ~3s por frase |\n" + "| Licencia | Apache 2.0 | **CC BY-NC-SA 4.0 (no comercial)** |\n\n" + "Probamos los dos sobre el mismo texto castellano y comparamos los grafos." + )) + + cells.append(_md("## 1. Setup")) + + cells.append(_code( + "import os, sys, json, time, warnings, re\n" + "warnings.filterwarnings('ignore')\n" + "os.environ.setdefault('HF_HUB_DISABLE_PROGRESS_BARS', '1')\n" + "from pathlib import Path\n" + "\n" + "_pf = '/home/lucas/fn_registry/python/functions'\n" + "sys.path = [p for p in sys.path if not p.startswith(_pf + '/')]\n" + "if _pf not in sys.path:\n" + " sys.path.insert(0, _pf)\n" + "\n" + "import pandas as pd\n" + "import networkx as nx\n" + "import matplotlib.pyplot as plt\n" + "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n" + "from datascience.gliner_load_model import gliner_load_model\n" + "from datascience.glirel_load_model import glirel_load_model\n" + "from pipelines.extract_graph_hybrid import extract_graph_hybrid\n" + "print('imports OK')" + )) + + cells.append(_md("## 2. Texto de entrada (mismo que notebook 02)")) + + cells.append(_code( + f"TEXTO = {SPANISH_TEXT!r}\n" + "print(TEXTO)" + )) + + cells.append(_md("## 3. Carga modelos: GLiNER + GLiREL + mREBEL\n\nGLiNER y GLiREL warm. mREBEL cold ~60s la primera vez (descarga 2.4 GB).")) + + cells.append(_code( + "t0 = time.time(); gliner = gliner_load_model(); print(f'GLiNER {time.time()-t0:.1f}s')\n" + "t0 = time.time(); glirel = glirel_load_model(); print(f'GLiREL {time.time()-t0:.1f}s')\n" + "t0 = time.time()\n" + "mrebel_tok = AutoTokenizer.from_pretrained('Babelscape/mrebel-large', src_lang='es_XX', tgt_lang='tp_XX')\n" + "mrebel = AutoModelForSeq2SeqLM.from_pretrained('Babelscape/mrebel-large')\n" + "print(f'mREBEL {time.time()-t0:.1f}s')" + )) + + cells.append(_md("## 4. Pipeline A: GLiNER + GLiREL (notebook 02 baseline, t=0.30)")) + + cells.append(_code( + "entity_schema = [\n" + " {'type_ref': 'Person', 'label': 'person'},\n" + " {'type_ref': 'Organization', 'label': 'organization'},\n" + " {'type_ref': 'Location', 'label': 'location'},\n" + "]\n" + "relation_types = [\n" + " 'works_at', 'located_in', 'appointed_as', 'headquartered_in',\n" + " 'ceo_of', 'president_of', 'agreement_with', 'met_with',\n" + "]\n" + "ents_a, rels_a = extract_graph_hybrid(\n" + " chunks=[TEXTO], entity_schema=entity_schema, relation_types=relation_types,\n" + " gliner_model=gliner, glirel_model=glirel, llm_chat_json=None,\n" + " confidence_threshold=0.30,\n" + ")\n" + "print(f'GLiNER+GLiREL: {len(ents_a)} ents, {len(rels_a)} rels')" + )) + + cells.append(_md( + "## 5. Pipeline B: GLiNER + mREBEL\n\n" + "Estrategia hibrida:\n" + "1. **GLiNER** sigue extrayendo entidades tipadas (es excelente).\n" + "2. **mREBEL frase a frase** — el seq2seq termina pronto si le pasas el texto entero, asi que troceamos por sentence boundaries.\n" + "3. Para cada tripleta de mREBEL, hacemos **string-match difuso** entre head/tail y los nombres de entidades de GLiNER. Solo conservamos tripletas con ambos lados en el grafo.\n" + "4. Las tripletas que no enganchan con entidades GLiNER se ignoran (mREBEL a veces emite spans crudos como `\"esta en Bilbao\"` — esos caen)." + )) + + cells.append(_code( + "# 5.1 Entidades GLiNER (mismas que pipeline A)\n" + "ents_b = ents_a # GLiNER es identico\n" + "ent_names = sorted({e.name for e in ents_b}, key=len, reverse=True)\n" + "name_to_ent = {e.name: e for e in ents_b}\n" + "print(f'GLiNER ents: {len(ent_names)}')\n" + "\n" + "# 5.2 mREBEL frase por frase\n" + "def mrebel_extract_triplets(decoded_text):\n" + " \"\"\"Parser oficial del README adaptado.\"\"\"\n" + " triplets = []\n" + " text = decoded_text.replace('','').replace('','').replace('','').replace('tp_XX','').replace('__en__','').strip()\n" + " current = 'x'\n" + " subject, relation, object_, object_type, subject_type = '', '', '', '', ''\n" + " for token in text.split():\n" + " if token == '' or token == '':\n" + " current = 't'\n" + " if relation:\n" + " triplets.append({'head':subject.strip(),'head_type':subject_type,'type':relation.strip(),'tail':object_.strip(),'tail_type':object_type})\n" + " relation = ''\n" + " subject = ''\n" + " elif token.startswith('<') and token.endswith('>'):\n" + " if current in ('t','o'):\n" + " current = 's'\n" + " if relation:\n" + " triplets.append({'head':subject.strip(),'head_type':subject_type,'type':relation.strip(),'tail':object_.strip(),'tail_type':object_type})\n" + " object_ = ''\n" + " subject_type = token[1:-1]\n" + " else:\n" + " current = 'o'\n" + " object_type = token[1:-1]\n" + " relation = ''\n" + " else:\n" + " if current == 't': subject += ' ' + token\n" + " elif current == 's': object_ += ' ' + token\n" + " elif current == 'o': relation += ' ' + token\n" + " if subject and relation and object_ and object_type and subject_type:\n" + " triplets.append({'head':subject.strip(),'head_type':subject_type,'type':relation.strip(),'tail':object_.strip(),'tail_type':object_type})\n" + " return triplets\n" + "\n" + "sentences = [s.strip() for s in re.split(r'(?<=[\\.])\\s+', TEXTO) if len(s.strip()) > 20]\n" + "raw_triplets = []\n" + "t0 = time.time()\n" + "for s in sentences:\n" + " inputs = mrebel_tok(s, max_length=256, padding=True, truncation=True, return_tensors='pt')\n" + " out = mrebel.generate(\n" + " inputs['input_ids'], attention_mask=inputs['attention_mask'],\n" + " decoder_start_token_id=mrebel_tok.convert_tokens_to_ids('tp_XX'),\n" + " max_length=256, num_beams=4, length_penalty=1.0,\n" + " )\n" + " decoded = mrebel_tok.batch_decode(out, skip_special_tokens=False)[0]\n" + " raw_triplets.extend(mrebel_extract_triplets(decoded))\n" + "print(f'mREBEL: {len(raw_triplets)} tripletas en {time.time()-t0:.1f}s ({len(sentences)} frases)')" + )) + + cells.append(_md("### 5.3 Tripletas crudas de mREBEL (antes del match)")) + + cells.append(_code( + "df_raw = pd.DataFrame(raw_triplets)\n" + "df_raw" + )) + + cells.append(_md( + "### 5.4 Match con entidades GLiNER\n\n" + "Para cada tripleta de mREBEL, busco si head y tail aparecen como substring " + "(case-insensitive) en algun nombre de entidad GLiNER. Solo conservo tripletas " + "donde ambos enganchan." + )) + + cells.append(_code( + "def match_to_ent(span: str):\n" + " s = span.strip().lower()\n" + " if not s: return None\n" + " # exact match first\n" + " for n in ent_names:\n" + " if n.lower() == s:\n" + " return n\n" + " # substring (longest entity wins, ent_names ya esta sorted desc by len)\n" + " for n in ent_names:\n" + " if n.lower() in s or s in n.lower():\n" + " return n\n" + " return None\n" + "\n" + "rels_b_dicts = []\n" + "for t in raw_triplets:\n" + " h = match_to_ent(t['head'])\n" + " tail = match_to_ent(t['tail'])\n" + " if h and tail and h != tail:\n" + " rels_b_dicts.append({'from': h, 'kind': t['type'], 'to': tail,\n" + " 'head_type': t['head_type'], 'tail_type': t['tail_type']})\n" + "df_b = pd.DataFrame(rels_b_dicts)\n" + "print(f'tripletas alineadas con GLiNER: {len(rels_b_dicts)} de {len(raw_triplets)}')\n" + "df_b" + )) + + cells.append(_md("## 6. Visualizacion comparativa")) + + cells.append(_code( + "TYPE_COLOR = {'Person': '#5DA5DA', 'Organization': '#F17CB0', 'Location': '#60BD68'}\n" + "\n" + "def draw_a(ax, ents, rels, title):\n" + " G = nx.DiGraph()\n" + " for e in ents: G.add_node(e.name, type=e.type_ref)\n" + " for r in rels: G.add_edge(r.from_name, r.to_name, kind=r.relation_type)\n" + " pos = nx.spring_layout(G, k=2.2, iterations=80, seed=42)\n" + " cols = [TYPE_COLOR.get(G.nodes[n].get('type'), '#bbb') for n in G.nodes]\n" + " nx.draw_networkx_nodes(G, pos, node_color=cols, node_size=1900, edgecolors='#333', linewidths=1.4, ax=ax)\n" + " nx.draw_networkx_labels(G, pos, font_size=8, font_weight='bold', ax=ax)\n" + " nx.draw_networkx_edges(G, pos, edge_color='#888', arrows=True, arrowsize=14, width=1.2, alpha=0.65, ax=ax, connectionstyle='arc3,rad=0.08')\n" + " el = {(u,v): d['kind'] for u,v,d in G.edges(data=True)}\n" + " nx.draw_networkx_edge_labels(G, pos, edge_labels=el, font_size=6.5, ax=ax,\n" + " bbox=dict(boxstyle='round,pad=0.1', fc='white', ec='none', alpha=0.85))\n" + " ax.set_title(f'{title}: {G.number_of_nodes()} ents, {G.number_of_edges()} rels', fontsize=11)\n" + " ax.axis('off')\n" + "\n" + "def draw_b(ax, ents, rel_dicts, title):\n" + " G = nx.DiGraph()\n" + " for e in ents: G.add_node(e.name, type=e.type_ref)\n" + " for d in rel_dicts: G.add_edge(d['from'], d['to'], kind=d['kind'])\n" + " # quita nodos sin grado para que el grafo se vea\n" + " isolates = list(nx.isolates(G))\n" + " G.remove_nodes_from(isolates)\n" + " pos = nx.spring_layout(G, k=2.2, iterations=80, seed=42)\n" + " cols = [TYPE_COLOR.get(G.nodes[n].get('type'), '#bbb') for n in G.nodes]\n" + " nx.draw_networkx_nodes(G, pos, node_color=cols, node_size=1900, edgecolors='#333', linewidths=1.4, ax=ax)\n" + " nx.draw_networkx_labels(G, pos, font_size=8, font_weight='bold', ax=ax)\n" + " nx.draw_networkx_edges(G, pos, edge_color='#888', arrows=True, arrowsize=14, width=1.2, alpha=0.65, ax=ax, connectionstyle='arc3,rad=0.08')\n" + " el = {(u,v): d['kind'] for u,v,d in G.edges(data=True)}\n" + " nx.draw_networkx_edge_labels(G, pos, edge_labels=el, font_size=6.5, ax=ax,\n" + " bbox=dict(boxstyle='round,pad=0.1', fc='white', ec='none', alpha=0.85))\n" + " ax.set_title(f'{title}: {G.number_of_nodes()} ents, {G.number_of_edges()} rels', fontsize=11)\n" + " ax.axis('off')\n" + "\n" + "fig, axes = plt.subplots(1, 2, figsize=(20, 9))\n" + "draw_a(axes[0], ents_a, rels_a, 'A: GLiNER + GLiREL (t=0.30)')\n" + "draw_b(axes[1], ents_b, rels_b_dicts, 'B: GLiNER + mREBEL (alineado)')\n" + "from matplotlib.patches import Patch\n" + "legend = [Patch(facecolor=c, edgecolor='#333', label=t) for t, c in TYPE_COLOR.items()]\n" + "axes[0].legend(handles=legend, loc='upper left', frameon=True, fontsize=10)\n" + "plt.tight_layout(); plt.show()" + )) + + cells.append(_md( + "## 7. Lectura\n\n" + "**mREBEL gana en este texto.** Las tripletas que sobreviven al match son semanticamente correctas (presidencias reales, sedes reales, posiciones reales) y los tipos de relacion vienen del vocabulario Wikidata (`employer`, `chairperson`, `chief executive officer`, `headquarters location`...) — mas rico y mas semantico que las labels que pasamos a GLiREL.\n\n" + "GLiREL a `t=0.30` queda con 1 relacion (falsa). Subiendo a `t=0.15` produce 51 con mayoria espuria. **No hay sweet spot util.**\n\n" + "### Trade-offs operativos\n\n" + "| Aspecto | Verdict |\n" + "|---|---|\n" + "| Calidad semantica ES | mREBEL >> GLiREL (no comparable) |\n" + "| Latencia | mREBEL ~3s/frase, GLiREL ~50ms total. mREBEL es 50× mas lento, pero las relaciones son utiles. |\n" + "| Tamaño en disco | mREBEL 2.4 GB, GLiREL 1.5 GB |\n" + "| Vocabulario relaciones | mREBEL fijo (~400 Wikidata types). GLiREL libre. Para narrativa empresarial Wikidata cubre todo. |\n" + "| Licencia | mREBEL CC BY-NC-SA 4.0 (no comercial). GLiREL Apache 2.0. **Bloqueante si esto pasa a producto comercial.** |\n" + "| Mapeo a entidades | mREBEL emite spans crudos → necesita match con GLiNER (ya implementado en celda 5.4). GLiREL ya devuelve nombres. |\n\n" + "### Implicacion para el pipeline\n\n" + "1. **Para uso personal/investigacion** (caso actual): cambiar GLiREL por mREBEL en `extract_graph_hybrid` cuando el chunk sea castellano. Issue nuevo en `graph_explorer`: `0042-mrebel-relation-extractor.md`.\n" + "2. **El panel `paste_extract`** debe avisar de la latencia: con texto largo (10+ frases) son ~30s. UI: barra de progreso por frase.\n" + "3. **Para uso comercial** (futuro): no se puede usar mREBEL tal cual. Alternativas:\n" + " - LLM (issue ya contemplado, cualquier proveedor licencia comercial OK).\n" + " - Fine-tunear REBEL monolingue (Apache 2.0) en castellano si tienes datos.\n" + " - Buscar otro modelo abierto (REDFM tiene licencia distinta — comprobar).\n" + "4. **Capa pre-mREBEL recomendada:** dado que mREBEL emite mejores tipos de relacion (Wikidata) que las labels que paso a mano (`works_at`...), **conviene que el panel `paste_extract` no fuerce un vocabulario fijo y use lo que mREBEL devuelva**. La taxonomia del grafo se enriquece sola.\n\n" + "### Que falta probar\n\n" + "- Mismo benchmark con corpus mas grande (10+ articulos).\n" + "- Evaluacion con texto OSINT (IPs, dominios, indicadores) — donde el vocabulario Wikidata puede no encajar.\n" + "- Integracion con LLM como tercer nivel (la capa que ya admite el pipeline). Ahora pasa de GLiREL a LLM-fallback solo si GLiREL falla; con mREBEL podria tener mas sentido tener LLM como _refiner_ encima." + )) + + nb = nbf.v4.new_notebook() + nb.cells = cells + nb.metadata = { + "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, + "language_info": {"name": "python"}, + } + NB_PATH.parent.mkdir(parents=True, exist_ok=True) + nbf.write(nb, NB_PATH) + print(f"[done] {NB_PATH} cells={len(cells)}") + + +if __name__ == "__main__": + build() diff --git a/build_notebook_nuextract.py b/build_notebook_nuextract.py new file mode 100644 index 0000000..5722944 --- /dev/null +++ b/build_notebook_nuextract.py @@ -0,0 +1,488 @@ +"""Construye notebooks/07_nuextract_vs_gliner2.ipynb — comparativa completa. + +Carga datos de: + - nuextract_results.json (NuExtract 2.0-2B en GPU + baseline CPU) + - benchmark_v2.json (GLiNER2 sobre el mismo PDF) + +Construye grafos a partir del JSON anidado de NuExtract (nested → edges) y +compara con los grafos de GLiNER2 lado a lado: numero de nodos, aristas, +tiempo por extraccion, calidad cualitativa. +""" +from __future__ import annotations + +import json +from pathlib import Path + +import nbformat as nbf + +HERE = Path(__file__).resolve().parent +NB_PATH = HERE / "notebooks" / "07_nuextract_vs_gliner2.ipynb" + + +def _md(text: str): + return nbf.v4.new_markdown_cell(text) + + +def _code(src: str): + cell = nbf.v4.new_code_cell(src) + cell.outputs = [] + cell.execution_count = None + return cell + + +def build(): + cells = [] + + cells.append(_md( + "# NuExtract 2.0-2B (GPU) vs GLiNER2 — comparativa con visualizacion\n\n" + "**Pregunta:** ¿merece la pena un LLM con inferencia (NuExtract 2.0) en un proyecto donde " + "antes elegimos GLiNER2 por velocidad?\n\n" + "**Setup:**\n" + "- NuExtract 2.0-2B (Qwen2-VL-2B base, **MIT license**, 2B params, GPU BF16 sobre RTX 3070).\n" + "- GLiNER2-large-v1 (Apache 2.0, 340M params, CPU).\n" + "- Mismos corpora: `es_corporate_short` (8 frases), `LONG_TEXT_ES` (25 frases), 5 chunks del PDF de BBVA.\n\n" + "**Diferencia de paradigma:**\n" + "- **GLiNER2** = clasificador. Output: listas planas `{entities: {tipo: [names]}, relations: {tipo: [(h, t)]}}`.\n" + "- **NuExtract** = LLM generativo. Output: JSON arbitrario que tu defines en el `template`. Las relaciones se modelan como atributos de los objetos (`{org: {ceo: \"X\", headquartered_in: \"Y\"}}`).\n\n" + "**Hipotesis:** NuExtract gana en _riqueza estructural_ (atributos por entidad de un solo paso) pero pierde en velocidad — incluso con GPU." + )) + + cells.append(_md("## 1. Setup")) + + cells.append(_code( + "import os, sys, json, warnings\n" + "warnings.filterwarnings('ignore')\n" + "from pathlib import Path\n" + "from collections import defaultdict\n" + "\n" + "_pf = '/home/lucas/fn_registry/python/functions'\n" + "sys.path = [p for p in sys.path if not p.startswith(_pf + '/')]\n" + "if _pf not in sys.path: sys.path.insert(0, _pf)\n" + "\n" + "import pandas as pd\n" + "import networkx as nx\n" + "import matplotlib.pyplot as plt\n" + "from matplotlib.patches import Patch\n" + "\n" + "NUEX = json.loads(Path('../nuextract_results.json').read_text())\n" + "\n" + "# Re-parsear el raw_text de cada test con un parser corregido (el original\n" + "# del script usaba rfind y solo capturaba el ultimo objeto pequeño).\n" + "def reparse(text):\n" + " if not text: return None\n" + " s = text.find('{')\n" + " if s < 0: return None\n" + " for end in range(len(text), s, -1):\n" + " try: return json.loads(text[s:end])\n" + " except Exception: continue\n" + " return None\n" + "for key in ['T1_corp_short_flat', 'T2_corp_short_rich', 'T3_long_text_rich']:\n" + " if key in NUEX:\n" + " NUEX[key]['parsed'] = reparse(NUEX[key].get('raw_text', ''))\n" + "for cr in NUEX.get('T4_pdf_chunks', []):\n" + " cr['parsed'] = reparse(cr.get('raw_text', ''))\n" + "GLNR_CORPUS = json.loads(Path('../benchmark_v2.json').read_text()) # GLiNER2 sobre 4 corpora\n" + "GLNR = json.loads(Path('../improvements.json').read_text()) # GLiNER2 sobre PDF + improvements\n" + "print('NuExtract keys:', list(NUEX.keys()))\n" + "print('GLiNER2 keys: ', list(GLNR.keys()))\n" + "print()\n" + "print('NuExtract device:', NUEX['meta']['device'], NUEX['meta']['dtype'])" + )) + + cells.append(_md( + "## 2. Tabla de tiempos — CPU vs GPU vs GLiNER2\n\n" + "Comparamos las 4 pasadas (T1-T4) de NuExtract contra GLiNER2 sobre los mismos corpora." + )) + + cells.append(_code( + "# Construir tabla de tiempos\n" + "rows = []\n" + "\n" + "# CPU baseline (capturado del run anterior)\n" + "cpu = NUEX.get('cpu_baseline', {})\n" + "if 'T1_flat' in cpu:\n" + " rows.append({'test': 'T1 corp_short flat', 'engine': 'NuExtract CPU', 'time_s': cpu['T1_flat']['elapsed_s'],\n" + " 'in_tok': cpu['T1_flat']['in_tok'], 'out_tok': cpu['T1_flat']['out_tok']})\n" + "if 'T2_rich' in cpu:\n" + " rows.append({'test': 'T2 corp_short rich', 'engine': 'NuExtract CPU', 'time_s': cpu['T2_rich']['elapsed_s'],\n" + " 'in_tok': cpu['T2_rich']['in_tok'], 'out_tok': cpu['T2_rich']['out_tok']})\n" + "\n" + "# GPU (este run)\n" + "for key, label in [('T1_corp_short_flat', 'T1 corp_short flat'),\n" + " ('T2_corp_short_rich', 'T2 corp_short rich'),\n" + " ('T3_long_text_rich', 'T3 long_text rich')]:\n" + " if key in NUEX:\n" + " r = NUEX[key]\n" + " rows.append({'test': label, 'engine': 'NuExtract GPU', 'time_s': r['elapsed_s'],\n" + " 'in_tok': r['n_input_tokens'], 'out_tok': r['n_output_tokens']})\n" + "\n" + "# GLiNER2 baseline timings (de benchmark_v2.json — el config A es el equivalente)\n" + "# A es el flat schema sobre 97 chunks PDF — para comparar con T4 PDF\n" + "rows.append({'test': 'PDF (97 chunks)', 'engine': 'GLiNER2 CPU', 'time_s': GLNR['configs'][0]['elapsed'],\n" + " 'in_tok': '-', 'out_tok': '-'})\n" + "rows.append({'test': 'PDF (97 chunks)', 'engine': 'GLiNER2 CPU t=0.3', 'time_s': GLNR['configs'][1]['elapsed'],\n" + " 'in_tok': '-', 'out_tok': '-'})\n" + "\n" + "df_times = pd.DataFrame(rows)\n" + "df_times" + )) + + cells.append(_md( + "## 3. Tiempos sobre el PDF — extrapolacion\n\n" + "5 chunks de muestra → estimacion del PDF completo." + )) + + cells.append(_code( + "if 'T4_pdf_chunks' in NUEX:\n" + " chunk_rows = []\n" + " for cr in NUEX['T4_pdf_chunks']:\n" + " chunk_rows.append({\n" + " 'chunk_idx': cr['chunk_idx'],\n" + " 'input_chars': cr['input_chars'],\n" + " 'time_s': cr['elapsed_s'],\n" + " 'in_tok': cr['n_input_tokens'],\n" + " 'out_tok': cr['n_output_tokens'],\n" + " })\n" + " df_chunks = pd.DataFrame(chunk_rows)\n" + " print('NuExtract GPU sobre 5 chunks del PDF:')\n" + " print(df_chunks)\n" + " print()\n" + " if 'full_pdf_extrapolation' in NUEX:\n" + " e = NUEX['full_pdf_extrapolation']\n" + " print(f\"Extrapolacion PDF entero ({e['n_chunks']} chunks):\")\n" + " print(f\" NuExtract GPU: {e['estimated_total_s']:.0f}s = {e['estimated_total_min']:.1f} min\")\n" + " print(f\" GLiNER2 CPU baseline: {GLNR['configs'][0]['elapsed']:.0f}s = {GLNR['configs'][0]['elapsed']/60:.1f} min\")\n" + " ratio = e['estimated_total_s'] / GLNR['configs'][0]['elapsed']\n" + " print(f\" ratio NuExtract/GLiNER2: {ratio:.1f}x\")\n" + "else:\n" + " print('T4_pdf_chunks no presente todavia')" + )) + + cells.append(_md( + "## 4. Estructura del output — paradigmas distintos\n\n" + "**NuExtract** rellena el template JSON. Lo que pidas, sale (si existe en el texto)." + )) + + cells.append(_code( + "# Mostrar el JSON parseado de T2 (rich corporate sobre 8 frases ES)\n" + "print('=== NuExtract T2 — schema rich corporate sobre es_corporate_short ===')\n" + "if 'T2_corp_short_rich' in NUEX:\n" + " parsed = NUEX['T2_corp_short_rich'].get('parsed')\n" + " if parsed:\n" + " print(json.dumps(parsed, indent=2, ensure_ascii=False))\n" + " else:\n" + " print('parsed = None (raw text:)')\n" + " print(NUEX['T2_corp_short_rich']['raw_text'][:1500])" + )) + + cells.append(_md("## 5. Convertir el JSON anidado de NuExtract a un grafo")) + + cells.append(_code( + "def nuextract_corp_to_graph(parsed: dict) -> nx.DiGraph:\n" + " \"\"\"Convierte el output de schema_rich_corporate a un DiGraph.\n" + "\n" + " Mapeo:\n" + " org.name → nodo (type=organization)\n" + " org.ceo → nodo (type=person), arista person --ceo_of--> org\n" + " org.chairman_president → nodo, arista --president_of--> org\n" + " org.headquartered_in → nodo (type=location), arista org --headquartered_in--> loc\n" + " org.subsidiaries[] → cada sub: nodo + arista sub --subsidiary_of--> org\n" + " org.parent_company → nodo + arista org --subsidiary_of--> parent\n" + " person.name → nodo, person --role--> organization\n" + " agreement.between[] → entre cada par, arista A --agreement_with--> B\n" + " \"\"\"\n" + " G = nx.DiGraph()\n" + " if not parsed: return G\n" + " \n" + " def add_node(name, typ):\n" + " if name and isinstance(name, str) and name.strip():\n" + " G.add_node(name.strip(), type=typ)\n" + " \n" + " for org in parsed.get('organizations', []) or []:\n" + " if not isinstance(org, dict): continue\n" + " oname = (org.get('name') or '').strip()\n" + " if not oname: continue\n" + " add_node(oname, 'organization')\n" + " if org.get('ceo'):\n" + " add_node(org['ceo'], 'person')\n" + " G.add_edge(org['ceo'].strip(), oname, kind='ceo_of')\n" + " if org.get('chairman_president'):\n" + " add_node(org['chairman_president'], 'person')\n" + " G.add_edge(org['chairman_president'].strip(), oname, kind='president_of')\n" + " if org.get('headquartered_in'):\n" + " add_node(org['headquartered_in'], 'location')\n" + " G.add_edge(oname, org['headquartered_in'].strip(), kind='headquartered_in')\n" + " if org.get('parent_company'):\n" + " add_node(org['parent_company'], 'organization')\n" + " G.add_edge(oname, org['parent_company'].strip(), kind='subsidiary_of')\n" + " for sub in org.get('subsidiaries', []) or []:\n" + " if isinstance(sub, str) and sub.strip():\n" + " add_node(sub, 'organization')\n" + " G.add_edge(sub.strip(), oname, kind='subsidiary_of')\n" + " \n" + " for p in parsed.get('people', []) or []:\n" + " if not isinstance(p, dict): continue\n" + " pname = (p.get('name') or '').strip()\n" + " if not pname: continue\n" + " add_node(pname, 'person')\n" + " org = (p.get('organization') or '').strip()\n" + " role = (p.get('role') or 'works_at').strip()\n" + " if org:\n" + " add_node(org, 'organization')\n" + " # role es texto libre, lo metemos como kind\n" + " kind = role.lower().replace(' ', '_')[:30] if role else 'works_at'\n" + " G.add_edge(pname, org, kind=kind)\n" + " \n" + " for ag in parsed.get('agreements', []) or []:\n" + " if not isinstance(ag, dict): continue\n" + " parties = [p for p in (ag.get('between') or []) if isinstance(p, str) and p.strip()]\n" + " if len(parties) < 2: continue\n" + " for i, a in enumerate(parties):\n" + " for b in parties[i+1:]:\n" + " G.add_edge(a.strip(), b.strip(), kind='agreement_with')\n" + " \n" + " return G\n" + "\n" + "G_nuext_t2 = nuextract_corp_to_graph(NUEX['T2_corp_short_rich'].get('parsed'))\n" + "print(f'NuExtract T2 grafo: {G_nuext_t2.number_of_nodes()} nodos, {G_nuext_t2.number_of_edges()} aristas')" + )) + + cells.append(_md("## 6. Visualizacion lado a lado — 8 frases ES corporate")) + + cells.append(_code( + "TYPE_COLOR = {'person': '#5DA5DA', 'organization': '#F17CB0', 'location': '#60BD68', '?': '#bbb'}\n" + "\n" + "def draw(ax, G, title, max_label=20):\n" + " if G.number_of_nodes() == 0:\n" + " ax.set_title(f'{title} (empty)'); ax.axis('off'); return\n" + " pos = nx.spring_layout(G, k=2.5, iterations=80, seed=42)\n" + " cols = [TYPE_COLOR.get(G.nodes[n].get('type'), '#bbb') for n in G.nodes]\n" + " nx.draw_networkx_nodes(G, pos, node_color=cols, node_size=1700, edgecolors='#333', linewidths=1.3, ax=ax)\n" + " labels = {n: (n if len(n) <= max_label else n[:max_label-1]+'…') for n in G.nodes}\n" + " nx.draw_networkx_labels(G, pos, labels=labels, font_size=7.5, font_weight='bold', ax=ax)\n" + " nx.draw_networkx_edges(G, pos, edge_color='#888', arrows=True, arrowsize=12, width=1.0, alpha=0.65, ax=ax, connectionstyle='arc3,rad=0.08')\n" + " el = {(u,v): d['kind'] for u,v,d in G.edges(data=True)}\n" + " nx.draw_networkx_edge_labels(G, pos, edge_labels=el, font_size=6, ax=ax,\n" + " bbox=dict(boxstyle='round,pad=0.1', fc='white', ec='none', alpha=0.85))\n" + " ax.set_title(f'{title}: {G.number_of_nodes()} nodos, {G.number_of_edges()} aristas', fontsize=11)\n" + " ax.axis('off')\n" + "\n" + "fig, axes = plt.subplots(1, 2, figsize=(20, 9))\n" + "draw(axes[0], G_nuext_t2, 'NuExtract 2.0-2B GPU\\n(8 frases, schema rich)')\n" + "\n" + "# Para GLiNER2 sobre el mismo texto, no tenemos benchmark v2 sobre es_corporate_short directamente.\n" + "# Notebook 04 dejo es_corporate_short con 14 ents + 8 rels via gliner2. Hardcodeamos del notebook 04 para comparar.\n" + "G_gliner2_t2 = nx.DiGraph()\n" + "_gliner2_short = { # del notebook 04 (es_corporate_short)\n" + " 'entities': {'person': ['Ignacio Galan','Carlos Torres','Pablo Isla','Jose Maria Alvarez-Pallete','Marina Serrano'],\n" + " 'organization': ['Iberdrola','Inditex','Endesa','BBVA'],\n" + " 'location': ['Bilbao','Galicia','Madrid','Arteixo','A Coruna']},\n" + " 'relations': [('Pablo Isla','works_at','Inditex'),\n" + " ('Pablo Isla','appointed_as','consejero de Telefonica'),\n" + " ('Marina Serrano','ceo_of','Endesa'),\n" + " ('Ignacio Galan','president_of','Iberdrola'),\n" + " ('Inditex','headquartered_in','Arteixo, A Coruna'),\n" + " ('Iberdrola','agreement_with','Endesa'),\n" + " ('Inditex','acquired','Pablo Isla')],\n" + "}\n" + "for typ, names in _gliner2_short['entities'].items():\n" + " for n in names: G_gliner2_t2.add_node(n, type=typ)\n" + "for h, k, t in _gliner2_short['relations']:\n" + " if h not in G_gliner2_t2: G_gliner2_t2.add_node(h, type='?')\n" + " if t not in G_gliner2_t2: G_gliner2_t2.add_node(t, type='?')\n" + " G_gliner2_t2.add_edge(h, t, kind=k)\n" + "draw(axes[1], G_gliner2_t2, 'GLiNER2 CPU\\n(8 frases, baseline notebook 04)')\n" + "\n" + "legend = [Patch(facecolor=c, edgecolor='#333', label=t) for t, c in TYPE_COLOR.items() if t != '?']\n" + "axes[0].legend(handles=legend, loc='upper left', fontsize=10)\n" + "plt.tight_layout(); plt.show()" + )) + + cells.append(_md( + "**Lectura del lado a lado:**\n\n" + "- **NuExtract** captura **atributos por entidad** (cada org tiene su `ceo`, `headquartered_in`, etc) en una sola pasada — el grafo se construye 'gratis' a partir del JSON anidado.\n" + "- **GLiNER2** extrae listas planas — el grafo emerge de las relaciones tipadas, pero a veces faltan atributos (no captura `parent_company`, `subsidiaries` directamente sin esos labels en el schema).\n" + "- Ambos tienen calidad alta en este corpus pequeño. Diferencia mas notable: NuExtract tiene mas dificultad con relaciones cruzadas (Iberdrola-Endesa) que GLiNER2 capta como `agreement_with`." + )) + + cells.append(_md( + "## 7. Long text (25 frases sector bancario) — NuExtract\n\n" + "**⚠️ Hallazgo importante:** En este test (T3), NuExtract **degenero en bucle de repeticion** y " + "agoto los 2048 max_new_tokens emitiendo `{\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\"...}` " + "repetido decenas de veces. El JSON resultante esta corrupto y `parsed = None`.\n\n" + "**Causa probable:** texto demasiado largo (400 words / ~952 tokens input + schema rico) sin `repetition_penalty`.\n" + "Mitigacion: anadir `repetition_penalty=1.1`, `do_sample=True, temperature=0.1`, o **trocear** el texto en chunks de ~150 words y agregar (mismo patron que GLiNER2).\n\n" + "**Implicacion operativa:** NuExtract requiere chunking SIEMPRE para texto medio-largo. GLiNER2 _tambien_ chunkea pero al menos no degenera — sigue extrayendo entidades correctas aunque baje recall." + )) + + cells.append(_code( + "G_nuext_long = nuextract_corp_to_graph(NUEX['T3_long_text_rich'].get('parsed'))\n" + "print(f'NuExtract T3 long_text: {G_nuext_long.number_of_nodes()} nodos, {G_nuext_long.number_of_edges()} aristas')\n" + "print()\n" + "print('Top entidades del JSON parseado:')\n" + "parsed = NUEX['T3_long_text_rich'].get('parsed') or {}\n" + "if parsed.get('organizations'):\n" + " print(f\" Organizations: {len(parsed['organizations'])}\")\n" + " for o in parsed['organizations'][:8]:\n" + " print(f\" {o.get('name'):30s} ceo={o.get('ceo')} pres={o.get('chairman_president')} hq={o.get('headquartered_in')}\")\n" + "if parsed.get('people'):\n" + " print(f\" People: {len(parsed['people'])}\")\n" + "if parsed.get('agreements'):\n" + " print(f\" Agreements: {len(parsed['agreements'])}\")" + )) + + cells.append(_code( + "fig, ax = plt.subplots(figsize=(15, 11))\n" + "draw(ax, G_nuext_long, 'NuExtract 2.0-2B GPU\\nLONG_TEXT_ES (25 frases sector bancario)', max_label=22)\n" + "legend = [Patch(facecolor=c, edgecolor='#333', label=t) for t, c in TYPE_COLOR.items() if t != '?']\n" + "ax.legend(handles=legend, loc='upper left', fontsize=10)\n" + "plt.tight_layout(); plt.show()" + )) + + cells.append(_md("## 8. PDF (5 chunks de muestra)")) + + cells.append(_code( + "def nuextract_gdpr_to_graph(parsed: dict) -> nx.DiGraph:\n" + " \"\"\"Schema GDPR: data_controller / dpo_contact / data_categories / rights / authorities / laws.\"\"\"\n" + " G = nx.DiGraph()\n" + " if not parsed: return G\n" + " \n" + " def add_node(name, typ):\n" + " if name and isinstance(name, str) and name.strip():\n" + " G.add_node(name.strip(), type=typ)\n" + " \n" + " dc = parsed.get('data_controller') or {}\n" + " if isinstance(dc, dict) and dc.get('name'):\n" + " add_node(dc['name'], 'organization')\n" + " if dc.get('address'):\n" + " add_node(dc['address'], 'location')\n" + " G.add_edge(dc['name'].strip(), dc['address'].strip(), kind='located_in')\n" + " dpo = parsed.get('dpo_contact') or {}\n" + " if isinstance(dpo, dict) and dpo.get('email'):\n" + " add_node(dpo['email'], 'email')\n" + " if isinstance(dc, dict) and dc.get('name'):\n" + " G.add_edge(dpo['email'].strip(), dc['name'].strip(), kind='dpo_of')\n" + " for cat in parsed.get('data_categories', []) or []:\n" + " if isinstance(cat, str) and cat.strip():\n" + " add_node(cat, 'data_category')\n" + " for r in parsed.get('rights_listed', []) or []:\n" + " if isinstance(r, str) and r.strip():\n" + " add_node(r, 'right')\n" + " for a in parsed.get('authorities_mentioned', []) or []:\n" + " if isinstance(a, dict) and a.get('name'):\n" + " add_node(a['name'], 'authority')\n" + " if a.get('url_or_contact'):\n" + " add_node(a['url_or_contact'], 'url')\n" + " G.add_edge(a['name'].strip(), a['url_or_contact'].strip(), kind='contact')\n" + " for l in parsed.get('laws_mentioned', []) or []:\n" + " if isinstance(l, str) and l.strip():\n" + " add_node(l, 'law')\n" + " return G\n" + "\n" + "# Combinar grafos de los 5 chunks del PDF\n" + "G_pdf_combined = nx.DiGraph()\n" + "if 'T4_pdf_chunks' in NUEX:\n" + " for cr in NUEX['T4_pdf_chunks']:\n" + " Gc = nuextract_gdpr_to_graph(cr.get('parsed'))\n" + " for n, d in Gc.nodes(data=True):\n" + " if n not in G_pdf_combined:\n" + " G_pdf_combined.add_node(n, **d)\n" + " for u, v, d in Gc.edges(data=True):\n" + " G_pdf_combined.add_edge(u, v, **d)\n" + "print(f'NuExtract PDF (5 chunks combinados): {G_pdf_combined.number_of_nodes()} nodos, {G_pdf_combined.number_of_edges()} aristas')" + )) + + cells.append(_code( + "PDF_TYPE_COLOR = {'organization':'#F17CB0','person':'#5DA5DA','location':'#60BD68',\n" + " 'email':'#FAA43A','authority':'#7C7C7C','right':'#B276B2',\n" + " 'data_category':'#DECF3F','law':'#F15854','url':'#DECF3F'}\n" + "\n" + "def draw_typed(ax, G, title, type_color):\n" + " if G.number_of_nodes() == 0:\n" + " ax.set_title(f'{title} (empty)'); ax.axis('off'); return\n" + " pos = nx.spring_layout(G, k=2.0, iterations=80, seed=42)\n" + " cols = [type_color.get(G.nodes[n].get('type'), '#bbb') for n in G.nodes]\n" + " nx.draw_networkx_nodes(G, pos, node_color=cols, node_size=1500, edgecolors='#333', linewidths=1.2, ax=ax)\n" + " labels = {n: (n if len(n) <= 22 else n[:21]+'…') for n in G.nodes}\n" + " nx.draw_networkx_labels(G, pos, labels=labels, font_size=7, font_weight='bold', ax=ax)\n" + " nx.draw_networkx_edges(G, pos, edge_color='#888', arrows=True, arrowsize=10, width=0.9, alpha=0.6, ax=ax, connectionstyle='arc3,rad=0.08')\n" + " el = {(u,v): d['kind'] for u,v,d in G.edges(data=True)}\n" + " nx.draw_networkx_edge_labels(G, pos, edge_labels=el, font_size=5.5, ax=ax,\n" + " bbox=dict(boxstyle='round,pad=0.05', fc='white', ec='none', alpha=0.85))\n" + " ax.set_title(f'{title}: {G.number_of_nodes()} nodos, {G.number_of_edges()} aristas', fontsize=10)\n" + " ax.axis('off')\n" + "\n" + "fig, axes = plt.subplots(1, 2, figsize=(20, 11))\n" + "draw_typed(axes[0], G_pdf_combined, 'NuExtract GPU\\nPDF — 5 chunks combinados', PDF_TYPE_COLOR)\n" + "\n" + "# GLiNER2 sobre el PDF entero (97 chunks) ya esta en GLNR — config B post-coref\n" + "# Si tenemos el grafo post-coref no esta en este JSON. Reconstruimos de lo que hay.\n" + "# El config A del benchmark_v2 tiene los stats — usamos eso como referencia textual.\n" + "axes[1].axis('off')\n" + "axes[1].text(0.05, 0.92, 'GLiNER2 CPU sobre PDF entero (97 chunks)', fontsize=14, fontweight='bold', transform=axes[1].transAxes)\n" + "stats_a = GLNR['configs'][0]['stats']\n" + "stats_b = GLNR['configs'][1]['stats']\n" + "summary = (\n" + " f\"Config A (t=0.5 default):\\n\"\n" + " f\" ents: {stats_a['n_ents']}\\n\"\n" + " f\" rels: {stats_a['n_rels']}\\n\"\n" + " f\" edges: {stats_a['n_edges']}\\n\"\n" + " f\" isolates: {stats_a['n_isolates']}\\n\"\n" + " f\" conn%: {stats_a['connect_pct']}%\\n\"\n" + " f\" time: {GLNR['configs'][0]['elapsed']}s\\n\\n\"\n" + " f\"Config B (t=0.3):\\n\"\n" + " f\" ents: {stats_b['n_ents']}\\n\"\n" + " f\" rels: {stats_b['n_rels']}\\n\"\n" + " f\" edges: {stats_b['n_edges']}\\n\"\n" + " f\" isolates: {stats_b['n_isolates']}\\n\"\n" + " f\" conn%: {stats_b['connect_pct']}%\\n\"\n" + " f\" time: {GLNR['configs'][1]['elapsed']}s\"\n" + ")\n" + "axes[1].text(0.05, 0.84, summary, fontsize=10, family='monospace', verticalalignment='top', transform=axes[1].transAxes)\n" + "\n" + "active = {G_pdf_combined.nodes[n].get('type') for n in G_pdf_combined.nodes}\n" + "legend = [Patch(facecolor=c, edgecolor='#333', label=t) for t, c in PDF_TYPE_COLOR.items() if t in active]\n" + "axes[0].legend(handles=legend, loc='upper left', fontsize=8)\n" + "plt.tight_layout(); plt.show()" + )) + + cells.append(_md( + "## 9. Conclusion — cuando usar cada uno\n\n" + "**Datos mas relevantes** (PDF de 89.882 chars / 97 chunks):\n\n" + "| | GLiNER2 CPU | NuExtract GPU 2B |\n" + "|---|---|---|\n" + "| Tiempo PDF entero | ~134s (a t=0.5) / ~139s (t=0.3) | extrapolado segun T4 |\n" + "| Modelo | 340M params | 2B params (6×) |\n" + "| Hardware | CPU | GPU dedicada |\n" + "| Output | Listas planas con tipos fijos | JSON arbitrario, anidado, atributos por entidad |\n" + "| Schema | `entities([...]).relations([...])` (palabras claves) | Plantilla JSON cualquiera (`{org: {ceo, ...}}`) |\n" + "| Riqueza | Limitada al schema declarado | Ilimitada — preguntas atributos arbitrarios |\n" + "| Determinismo | Alto (clasificador) | Generativo, puede tener variaciones |\n" + "| Licencia | Apache 2.0 | MIT (2B), Qwen Research (4B), MIT (8B) |\n\n" + "**Cuando GLiNER2:** alto throughput, schemas estables, tiempo critico, sin GPU. **Robusto frente a texto largo** (no degenera).\n\n" + "**Cuando NuExtract:** documento legal/financiero/OSINT donde quieres rellenar una ficha rica por entidad ('extrae para cada empresa: nombre, sede, CEO, presidencia, fundador, subsidiarias, normativa aplicable'), tienes GPU disponible, **y troceas el texto** (porque sin chunking degenera, ver §7).\n\n" + "**Decision para `graph_explorer`:** **GLiNER2 sigue siendo el motor por defecto**. Pero **anadir NuExtract como engine opcional** ('rich extraction') para documentos donde la riqueza estructural justifica el coste — y si el usuario tiene GPU detectable. El panel `paste_extract` puede ofrecer un toggle `[Quick (GLiNER2) | Rich (NuExtract GPU)]`.\n\n" + "**Numeros clave:**\n\n" + "| Metrica | GLiNER2 CPU | NuExtract CPU | NuExtract GPU |\n" + "|---|---|---|---|\n" + "| 8 frases ES (flat) | ~1s | 25s | **2.9s** |\n" + "| 8 frases ES (rich) | n/a (schema flat) | 117s | **9.9s** |\n" + "| 25 frases ES (rich) | ~1s | n/a | 53s + ⚠️ degeneracion |\n" + "| PDF entero (97 chunks) | 134s (2.2 min) | (estimado >2h) | 310s (5.2 min) — 2.3× mas lento |\n" + "| Modelo | 340M params, 700 MB disco | 2B params, 4 GB disco | mismo, BF16 |\n" + "| Speedup CPU→GPU | n/a | n/a | **8-12×** |" + )) + + nb = nbf.v4.new_notebook() + nb.cells = cells + nb.metadata = { + "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, + "language_info": {"name": "python"}, + } + NB_PATH.parent.mkdir(parents=True, exist_ok=True) + nbf.write(nb, NB_PATH) + print(f"[done] {NB_PATH} cells={len(cells)}") + + +if __name__ == "__main__": + build() diff --git a/build_notebook_pdf.py b/build_notebook_pdf.py new file mode 100644 index 0000000..455fb45 --- /dev/null +++ b/build_notebook_pdf.py @@ -0,0 +1,457 @@ +"""Construye notebooks/05_long_text_and_pdf.ipynb — demostracion E2E: + parte A: texto largo (escrito en el notebook) → GLiNER2 → grafo + parte B: pipeline PDF → extract_pdf_text (registry) → chunking → GLiNER2 → grafo +""" +from __future__ import annotations + +from pathlib import Path +import nbformat as nbf + +HERE = Path(__file__).resolve().parent +NB_PATH = HERE / "notebooks" / "05_long_text_and_pdf.ipynb" + +LONG_TEXT_ES = ( + # 25+ frases sobre sector bancario espanol — denso en entidades, conecta tematicamente con el PDF de BBVA + "BBVA, presidido por Carlos Torres, completo en 2024 la integracion operativa de Banco Sabadell tras la fusion. " + "Onur Genc, consejero delegado del banco desde 2018, lidero el proceso desde la sede central en Bilbao. " + "El banco mantiene oficinas en Plaza San Nicolas 4 y opera en mas de 25 paises. " + "Banco Santander, dirigido por Ana Botin, sigue siendo el primer banco espanol por capitalizacion bursatil. " + "Hector Grisi asumio el cargo de CEO global de Santander en enero de 2023, reemplazando a Jose Antonio Alvarez. " + "CaixaBank, presidida por Jose Ignacio Goirigolzarri y con sede en Valencia desde 2017, completo la fusion con Bankia. " + "Gonzalo Gortazar es el consejero delegado de CaixaBank y reporta al consejo formado en parte por La Caixa. " + "El Banco de Espana, gobernado por Pablo Hernandez de Cos hasta 2024 y por Margarita Delgado en 2025, supervisa el sector. " + "Luis de Guindos, vicepresidente del Banco Central Europeo, fue ministro de Economia en el gobierno de Mariano Rajoy. " + "La Comision Nacional del Mercado de Valores, presidida por Rodrigo Buenaventura, regula los mercados financieros. " + "BBVA anuncio en mayo de 2024 una OPA hostil sobre Banco Sabadell que el consejo del banco rechazo inicialmente. " + "Cesar Gonzalez-Bueno, CEO de Sabadell, defendio la independencia del banco junto con su presidente Josep Oliu. " + "Repsol, presidida por Antonio Brufau y con CEO Josu Jon Imaz, vendio su filial mexicana a Macquarie. " + "Iberdrola, liderada por Ignacio Galan, opera Avangrid en EEUU y firmo un acuerdo PPA con Amazon. " + "Andy Jassy, CEO de Amazon desde Seattle, agradecio el contrato a Iberdrola en una nota publica. " + "Endesa, filial de la italiana Enel, tiene como CEO a Marina Serrano y opera en Espana, Portugal y Marruecos. " + "Ferrovial, presidida por Rafael del Pino, traslado su sede social a Holanda en 2022 generando polemica politica. " + "ACS, presidida por Florentino Perez, sigue siendo lider mundial en concesiones de infraestructura. " + "Inditex, fundada por Amancio Ortega y presidida por Marta Ortega desde 2022, tiene su sede en Arteixo, A Coruna. " + "Pablo Isla, expresidente de Inditex y actual consejero de Telefonica, se incorporo al consejo en 2024. " + "Telefonica, presidida por Jose Maria Alvarez-Pallete, sufrio la entrada del estado en su capital con SEPI. " + "Saudi Telecom Company adquirio un 9.9% de Telefonica en 2023, lo que motivo la respuesta del gobierno espanol. " + "Cristina Aldamiz-Echevarria fue nombrada directora de Recursos Humanos del Grupo Mapfre, dirigido por Antonio Huertas. " + "Naturgy, presidida por Francisco Reynes, recibio una OPA parcial del fondo emirati IFM en 2021 que se cancelo. " + "Indra, con Marc Murtra como presidente, se ha posicionado como contratista clave de Defensa para el ministerio de Margarita Robles." +) + + +def _md(text: str): + return nbf.v4.new_markdown_cell(text) + + +def _code(src: str): + cell = nbf.v4.new_code_cell(src) + cell.outputs = [] + cell.execution_count = None + return cell + + +def build(): + cells = [] + + cells.append(_md( + "# Texto largo + PDF E2E con GLiNER2\n\n" + "Demostracion en dos partes del flujo elegido (decision del notebook 04):\n\n" + "**Parte A** — Texto largo en castellano (25 frases sobre sector bancario espanol) → GLiNER2 → grafo.\n\n" + "**Parte B** — Pipeline real con un documento PDF: `politica_proteccion_datos.pdf` (BBVA, 20 paginas, copiado al vault). El flujo es:\n\n" + "1. `extract_pdf_text_py_core` (funcion ya en el registry, PyPDF2) extrae el texto.\n" + "2. Chunking por bloques (GLiNER2 tiene recall bajo en texto largo monolitico — visto en notebook 04).\n" + "3. GLiNER2 sobre cada bloque + agregacion deduplicada.\n" + "4. Grafo final + tabla de entidades top.\n\n" + "El PDF reside en `vaults/osint_nlp_models/test_documents/politica_proteccion_datos.pdf` para que sea reproducible desde cualquier PC con el vault sincronizado." + )) + + cells.append(_md("## 0. Setup")) + + cells.append(_code( + "import os, sys, json, time, re, warnings\n" + "warnings.filterwarnings('ignore')\n" + "os.environ.setdefault('HF_HUB_DISABLE_PROGRESS_BARS', '1')\n" + "from pathlib import Path\n" + "from collections import Counter\n" + "\n" + "_pf = '/home/lucas/fn_registry/python/functions'\n" + "sys.path = [p for p in sys.path if not p.startswith(_pf + '/')]\n" + "if _pf not in sys.path: sys.path.insert(0, _pf)\n" + "\n" + "import pandas as pd\n" + "import networkx as nx\n" + "import matplotlib.pyplot as plt\n" + "from gliner2 import GLiNER2\n" + "# funcion del registry — ver registry.db para signature\n" + "from core.extract_pdf_text import extract_pdf_text\n" + "\n" + "VAULT = Path('/home/lucas/vaults/osint_nlp_models')\n" + "PDF_PATH = VAULT / 'test_documents' / 'politica_proteccion_datos.pdf'\n" + "print(f'PDF exists: {PDF_PATH.exists()}, size: {PDF_PATH.stat().st_size:,} bytes')" + )) + + cells.append(_md("## 1. Cargar GLiNER2")) + + cells.append(_code( + "t0 = time.time()\n" + "model = GLiNER2.from_pretrained('fastino/gliner2-large-v1')\n" + "print(f'GLiNER2 ready in {time.time()-t0:.1f}s')\n" + "\n" + "ENTITY_LABELS = ['person', 'organization', 'location']\n" + "RELATION_LABELS = [\n" + " 'works_at', 'located_in', 'appointed_as', 'ceo_of', 'president_of',\n" + " 'headquartered_in', 'subsidiary_of', 'parent_company', 'founded_by',\n" + " 'agreement_with', 'acquired', 'succeeded_by', 'governed_by',\n" + "]" + )) + + cells.append(_md( + "# PARTE A — Texto largo\n\n" + "## A.1 El texto" + )) + + cells.append(_code( + f"TEXTO = {LONG_TEXT_ES!r}\n" + "n_sentences = len(re.split(r'(?<=[\\.!?])\\s+', TEXTO))\n" + "print(f'{len(TEXTO)} chars / {len(TEXTO.split())} words / {n_sentences} sentences')\n" + "print()\n" + "print(TEXTO[:600] + '...')" + )) + + cells.append(_md("## A.2 GLiNER2 — extraccion en una pasada")) + + cells.append(_code( + "schema = (model.create_schema()\n" + " .entities(ENTITY_LABELS)\n" + " .relations(RELATION_LABELS))\n" + "\n" + "t0 = time.time()\n" + "result = model.extract(TEXTO, schema=schema)\n" + "elapsed = time.time() - t0\n" + "n_ents = sum(len(v) for v in result['entities'].values())\n" + "n_rels = sum(len(v) for v in result['relation_extraction'].values())\n" + "print(f'{n_ents} entidades, {n_rels} relaciones en {elapsed:.2f}s')" + )) + + cells.append(_md("## A.3 Tabla de entidades")) + + cells.append(_code( + "rows = []\n" + "for typ, names in result['entities'].items():\n" + " for n in names:\n" + " rows.append({'type': typ, 'name': n})\n" + "df_ents = pd.DataFrame(rows).drop_duplicates().sort_values(['type', 'name']).reset_index(drop=True)\n" + "df_ents" + )) + + cells.append(_md("## A.4 Tabla de relaciones")) + + cells.append(_code( + "rows = []\n" + "for rt, pairs in result['relation_extraction'].items():\n" + " for h, t in pairs:\n" + " rows.append({'from': h, 'kind': rt, 'to': t})\n" + "df_rels = pd.DataFrame(rows).drop_duplicates().reset_index(drop=True)\n" + "df_rels" + )) + + cells.append(_md("## A.5 Grafo del texto largo")) + + cells.append(_code( + "TYPE_COLOR = {'person': '#5DA5DA', 'organization': '#F17CB0', 'location': '#60BD68'}\n" + "\n" + "def build_graph_from_extract(extract_result):\n" + " G = nx.DiGraph()\n" + " for typ, names in extract_result['entities'].items():\n" + " for n in names:\n" + " G.add_node(n, type=typ)\n" + " seen = set()\n" + " for rt, pairs in extract_result['relation_extraction'].items():\n" + " for h, t in pairs:\n" + " if (h, t, rt) in seen: continue\n" + " seen.add((h, t, rt))\n" + " # Asegura que ambos nodos existen (mREBEL/GLiNER2 a veces emite spans no-entidad)\n" + " if h not in G.nodes: G.add_node(h, type='?')\n" + " if t not in G.nodes: G.add_node(t, type='?')\n" + " G.add_edge(h, t, kind=rt)\n" + " return G\n" + "\n" + "def draw_graph(G, ax, title, type_color=TYPE_COLOR, max_label=25):\n" + " if G.number_of_nodes() == 0:\n" + " ax.set_title(f'{title} (empty)'); ax.axis('off'); return\n" + " pos = nx.spring_layout(G, k=2.5, iterations=100, seed=42)\n" + " cols = [type_color.get(G.nodes[n].get('type'), '#bbb') for n in G.nodes]\n" + " nx.draw_networkx_nodes(G, pos, node_color=cols, node_size=1700, edgecolors='#333', linewidths=1.3, ax=ax)\n" + " labels = {n: (n if len(n) <= max_label else n[:max_label-1]+'…') for n in G.nodes}\n" + " nx.draw_networkx_labels(G, pos, labels=labels, font_size=7.5, font_weight='bold', ax=ax)\n" + " nx.draw_networkx_edges(G, pos, edge_color='#888', arrows=True, arrowsize=12, width=1.0, alpha=0.6, ax=ax, connectionstyle='arc3,rad=0.08')\n" + " el = {(u,v): d['kind'] for u,v,d in G.edges(data=True)}\n" + " nx.draw_networkx_edge_labels(G, pos, edge_labels=el, font_size=6, ax=ax,\n" + " bbox=dict(boxstyle='round,pad=0.1', fc='white', ec='none', alpha=0.85))\n" + " ax.set_title(f'{title}: {G.number_of_nodes()} ents, {G.number_of_edges()} rels', fontsize=11)\n" + " ax.axis('off')\n" + "\n" + "G_text = build_graph_from_extract(result)\n" + "fig, ax = plt.subplots(figsize=(15, 11))\n" + "draw_graph(G_text, ax, 'Texto largo (25 frases sector bancario ES)')\n" + "from matplotlib.patches import Patch\n" + "legend = [Patch(facecolor=c, edgecolor='#333', label=t) for t, c in TYPE_COLOR.items()]\n" + "ax.legend(handles=legend, loc='upper left', fontsize=10)\n" + "plt.tight_layout(); plt.show()" + )) + + cells.append(_md( + "# PARTE B — Pipeline real con PDF\n\n" + "## B.1 Extraccion de texto (`extract_pdf_text_py_core` del registry)\n\n" + "El PDF: politica de proteccion de datos personales de BBVA, 20 paginas, ~13k palabras." + )) + + cells.append(_code( + "t0 = time.time()\n" + "pdf_text = extract_pdf_text(str(PDF_PATH))\n" + "print(f'extract_pdf_text en {time.time()-t0:.2f}s')\n" + "print(f'chars: {len(pdf_text):,} words: {len(pdf_text.split()):,}')\n" + "print()\n" + "print('--- primeros 800 chars ---')\n" + "print(pdf_text[:800])\n" + "print()\n" + "print('--- ultimos 400 chars ---')\n" + "print(pdf_text[-400:])" + )) + + cells.append(_md( + "## B.2 Chunking por bloques\n\n" + "GLiNER2 tiene recall bajo en texto largo monolitico (visto en notebook 04: 30 frases → solo 6 relaciones). " + "Solucion: trocear en bloques de ~5-8 frases y agregar resultados deduplicados." + )) + + cells.append(_code( + "def chunk_by_sentences(text, max_chars=1500):\n" + " # split en frases, agrupar hasta max_chars\n" + " sentences = re.split(r'(?<=[\\.!?])\\s+', text)\n" + " chunks, current = [], ''\n" + " for s in sentences:\n" + " if not s.strip(): continue\n" + " if len(current) + len(s) > max_chars and current:\n" + " chunks.append(current.strip())\n" + " current = s\n" + " else:\n" + " current += ' ' + s\n" + " if current.strip(): chunks.append(current.strip())\n" + " return chunks\n" + "\n" + "chunks = chunk_by_sentences(pdf_text, max_chars=1500)\n" + "print(f'{len(chunks)} chunks (max 1500 chars cada uno)')\n" + "print(f'tamanos: {[len(c) for c in chunks][:10]}...')\n" + "print()\n" + "print('--- chunk 0 (primeras 500 chars) ---')\n" + "print(chunks[0][:500])" + )) + + cells.append(_md("## B.3 GLiNER2 sobre cada chunk + agregacion")) + + cells.append(_code( + "# Schema legal/proteccion-datos: anadimos labels especificas del dominio\n" + "PDF_ENTITY_LABELS = [\n" + " 'person', 'organization', 'location', 'email',\n" + " 'law', 'right', 'data_category', 'authority',\n" + "]\n" + "PDF_RELATION_LABELS = [\n" + " 'located_in', 'governed_by', 'subject_to', 'protected_by',\n" + " 'contact_for', 'rights_against', 'subsidiary_of', 'controlled_by',\n" + "]\n" + "\n" + "schema_pdf = (model.create_schema()\n" + " .entities(PDF_ENTITY_LABELS)\n" + " .relations(PDF_RELATION_LABELS))\n" + "\n" + "# Acumuladores con dedupe\n" + "all_entities = {} # (type, name_lower) -> {'type': type, 'name': name (canonical), 'count': N}\n" + "all_relations = Counter() # (from, kind, to) -> count\n" + "\n" + "t0 = time.time()\n" + "for i, chunk in enumerate(chunks):\n" + " r = model.extract(chunk, schema=schema_pdf)\n" + " # entidades\n" + " for typ, names in r['entities'].items():\n" + " for n in names:\n" + " n_clean = n.strip()\n" + " if not n_clean: continue\n" + " key = (typ, n_clean.lower())\n" + " if key not in all_entities:\n" + " all_entities[key] = {'type': typ, 'name': n_clean, 'count': 0}\n" + " all_entities[key]['count'] += 1\n" + " # relaciones\n" + " for rt, pairs in r['relation_extraction'].items():\n" + " for h, t in pairs:\n" + " all_relations[(h.strip(), rt, t.strip())] += 1\n" + " if (i+1) % 5 == 0:\n" + " print(f' chunk {i+1}/{len(chunks)} → ents acumuladas: {len(all_entities)}, rels: {len(all_relations)}')\n" + "elapsed = time.time() - t0\n" + "print(f'\\nTotal: {len(chunks)} chunks en {elapsed:.1f}s ({elapsed/len(chunks):.2f}s/chunk)')\n" + "print(f'Entidades unicas: {len(all_entities)}')\n" + "print(f'Relaciones unicas: {len(all_relations)}')" + )) + + cells.append(_md("## B.4 Top entidades por frecuencia de mencion")) + + cells.append(_code( + "ent_rows = [{'type': v['type'], 'name': v['name'], 'mentions': v['count']} for v in all_entities.values()]\n" + "df_pdf_ents = pd.DataFrame(ent_rows).sort_values(['mentions', 'type'], ascending=[False, True]).reset_index(drop=True)\n" + "print('TOP 25 entidades por menciones:')\n" + "df_pdf_ents.head(25)" + )) + + cells.append(_md("## B.5 Relaciones extraidas (top 25 por count)")) + + cells.append(_code( + "rel_rows = [{'from': h, 'kind': rt, 'to': t, 'count': c} for (h, rt, t), c in all_relations.items()]\n" + "df_pdf_rels = pd.DataFrame(rel_rows).sort_values('count', ascending=False).reset_index(drop=True)\n" + "print(f'{len(df_pdf_rels)} relaciones unicas')\n" + "df_pdf_rels.head(25)" + )) + + cells.append(_md( + "## B.6 Grafo del PDF — top entidades\n\n" + "Filtramos a las entidades mas mencionadas (mentions ≥ 3) + sus relaciones para que el grafo sea legible. " + "El PDF tiene cientos de entidades; un grafo sin filtrar seria ilegible." + )) + + cells.append(_code( + "MIN_MENTIONS = 3\n" + "kept_names = {v['name'] for v in all_entities.values() if v['count'] >= MIN_MENTIONS}\n" + "name_to_type = {v['name']: v['type'] for v in all_entities.values()}\n" + "\n" + "G_pdf = nx.DiGraph()\n" + "for n in kept_names:\n" + " G_pdf.add_node(n, type=name_to_type.get(n, '?'))\n" + "\n" + "for (h, rt, t), c in all_relations.items():\n" + " if h in kept_names and t in kept_names:\n" + " G_pdf.add_edge(h, t, kind=rt, count=c)\n" + "\n" + "# quitar nodos isolados\n" + "isolates = list(nx.isolates(G_pdf))\n" + "G_pdf.remove_nodes_from(isolates)\n" + "print(f'Filtrado: {len(kept_names)} ents con >={MIN_MENTIONS} menciones, {len(isolates)} aisladas removidas')\n" + "print(f'Grafo final: {G_pdf.number_of_nodes()} nodos, {G_pdf.number_of_edges()} aristas')\n" + "\n" + "PDF_TYPE_COLOR = {\n" + " 'person': '#5DA5DA', 'organization': '#F17CB0', 'location': '#60BD68',\n" + " 'email': '#FAA43A', 'law': '#F15854', 'right': '#B276B2',\n" + " 'data_category': '#DECF3F', 'authority': '#7C7C7C', '?': '#bbb',\n" + "}\n" + "\n" + "fig, ax = plt.subplots(figsize=(16, 12))\n" + "draw_graph(G_pdf, ax, f'PDF: politica BBVA — top entidades (≥{MIN_MENTIONS} menciones)', type_color=PDF_TYPE_COLOR)\n" + "from matplotlib.patches import Patch\n" + "active_types = {G_pdf.nodes[n].get('type') for n in G_pdf.nodes}\n" + "legend = [Patch(facecolor=c, edgecolor='#333', label=t) for t, c in PDF_TYPE_COLOR.items() if t in active_types]\n" + "ax.legend(handles=legend, loc='upper left', fontsize=9)\n" + "plt.tight_layout(); plt.show()" + )) + + cells.append(_md( + "## B.7 Sanity-check: tipos detectados\n\n" + "Distribucion de entidades por tipo en el PDF de BBVA. Esperamos:\n" + "- Mucha `organization` (BBVA, sus filiales, AEPD, autoridades europeas)\n" + "- `person` para directivos / DPO / responsables\n" + "- `email` para canales de contacto\n" + "- `right` para los derechos GDPR (acceso, rectificacion, supresion, oposicion...)\n" + "- `data_category` para tipos de datos personales (financiero, biometrico, comportamental...)" + )) + + cells.append(_code( + "by_type = df_pdf_ents.groupby('type').agg(\n" + " n_unique=('name', 'nunique'),\n" + " total_mentions=('mentions', 'sum'),\n" + ").sort_values('total_mentions', ascending=False)\n" + "by_type" + )) + + cells.append(_md( + "## B.8 Grafo completo sin filtrar — la marana\n\n" + "Por curiosidad, sin filtros: las 378 entidades y 54 relaciones del PDF entero. " + "No hay etiquetas (ilegibles a esta escala) — los nodos se colorean por tipo. Sirve para " + "ver la **forma** del grafo: clusters densos = empresas/personas con muchas menciones; " + "satellites aislados = entidades que el modelo extrajo una sola vez." + )) + + cells.append(_code( + "# Grafo completo (sin filtro de menciones)\n" + "G_full = nx.DiGraph()\n" + "for v in all_entities.values():\n" + " G_full.add_node(v['name'], type=v['type'], mentions=v['count'])\n" + "for (h, rt, t), c in all_relations.items():\n" + " if h not in G_full.nodes: G_full.add_node(h, type='?', mentions=0)\n" + " if t not in G_full.nodes: G_full.add_node(t, type='?', mentions=0)\n" + " G_full.add_edge(h, t, kind=rt, count=c)\n" + "\n" + "print(f'Grafo completo: {G_full.number_of_nodes()} nodos, {G_full.number_of_edges()} aristas')\n" + "isolates = list(nx.isolates(G_full))\n" + "print(f' de los cuales aislados: {len(isolates)}')\n" + "\n" + "fig, ax = plt.subplots(figsize=(20, 20))\n" + "# Layout que aguanta grafos grandes — spring con menos iteraciones\n" + "pos = nx.spring_layout(G_full, k=0.5, iterations=40, seed=42)\n" + "node_sizes = [60 + 25 * G_full.nodes[n].get('mentions', 0) for n in G_full.nodes]\n" + "node_colors = [PDF_TYPE_COLOR.get(G_full.nodes[n].get('type'), '#bbb') for n in G_full.nodes]\n" + "nx.draw_networkx_nodes(G_full, pos, node_size=node_sizes, node_color=node_colors,\n" + " edgecolors='#222', linewidths=0.4, alpha=0.85, ax=ax)\n" + "nx.draw_networkx_edges(G_full, pos, edge_color='#555', alpha=0.25, width=0.6,\n" + " arrows=False, ax=ax)\n" + "# Solo etiquetar las top-15 por menciones\n" + "top_labels = {v['name']: v['name'] for v in sorted(all_entities.values(), key=lambda x: -x['count'])[:15]}\n" + "nx.draw_networkx_labels(G_full, pos, labels=top_labels, font_size=8, font_weight='bold', ax=ax)\n" + "from matplotlib.patches import Patch\n" + "active_types = {G_full.nodes[n].get('type') for n in G_full.nodes}\n" + "legend = [Patch(facecolor=c, edgecolor='#333', label=t) for t, c in PDF_TYPE_COLOR.items() if t in active_types]\n" + "ax.legend(handles=legend, loc='upper left', fontsize=11)\n" + "ax.set_title(f'PDF completo SIN filtro: {G_full.number_of_nodes()} entidades, {G_full.number_of_edges()} relaciones',\n" + " fontsize=13)\n" + "ax.axis('off')\n" + "plt.tight_layout(); plt.show()" + )) + + cells.append(_md( + "**Lectura del grafo completo:**\n\n" + "- **Cluster central denso** = entidades muy mencionadas (BBVA, AEPD, los derechos GDPR, los responsables del tratamiento) — donde el modelo establece las relaciones reales.\n" + "- **Satelites perifericos** = entidades extraidas una sola vez (un email aislado, un articulo de ley citado una vez, un nombre que aparece tangencialmente). Mucho ruido pero util para ver el alcance.\n" + "- **Tamaño de nodo** ∝ menciones (los grandes son los protagonistas).\n" + "- **Color por tipo** — ves de un vistazo que dominan organizaciones (rosa) y categorias de datos (amarillo).\n" + "- Sin filtrado, el grafo es **una maraña** — exactamente por eso B.6 filtraba a entidades con ≥3 menciones." + )) + + cells.append(_md( + "# Conclusion\n\n" + "**Funciono el flujo end-to-end.** El pipeline:\n\n" + "1. **`extract_pdf_text_py_core`** (registry, PyPDF2): lee el PDF de BBVA en <1s, ~89k chars.\n" + "2. **Chunking** por bloques de 1500 chars (~25 chunks).\n" + "3. **GLiNER2** sobre cada chunk con un schema custom para legal/proteccion-datos.\n" + "4. **Agregacion deduplicada** con conteo de menciones.\n" + "5. **Filtro a top entidades** (>= 3 menciones) para que el grafo sea legible.\n\n" + "Lo que esto deja claro:\n\n" + "- **El stack GLiNER2 funciona en documentos reales** — no es solo el corpus de prueba.\n" + "- **Chunking es esencial** para textos > 30 frases.\n" + "- **Schemas custom por dominio** funcionan: para legal/GDPR pasamos labels como `right`, `data_category`, `authority`.\n" + "- **El registry ya tiene la infra** (`extract_pdf_text`) — un grafo desde un PDF son ~30 lineas Python.\n\n" + "Pendiente del proyecto (de la cola P0 del vault):\n\n" + "- Promover el flujo a una funcion `extract_graph_from_pdf_py_pipelines` reusable en el registry.\n" + "- Implementar `gliner2_load_model` y `extract_graph_gliner2` como funciones del registry (issue 0042).\n" + "- Probar `gliner2-base-v1` (mas pequeño y rapido) para ver si la calidad se mantiene en chunking masivo." + )) + + nb = nbf.v4.new_notebook() + nb.cells = cells + nb.metadata = { + "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, + "language_info": {"name": "python"}, + } + NB_PATH.parent.mkdir(parents=True, exist_ok=True) + nbf.write(nb, NB_PATH) + print(f"[done] {NB_PATH} cells={len(cells)}") + + +if __name__ == "__main__": + build() diff --git a/improvements.json b/improvements.json new file mode 100644 index 0000000..4df68b6 --- /dev/null +++ b/improvements.json @@ -0,0 +1,3501 @@ +{ + "meta": { + "raw_chars": 89882, + "clean_chars": 88714, + "n_chunks_overlap": 97, + "n_chunks_no_overlap": 66, + "first_clean_600": "Banco Bilbao Vizcaya Argentaria, S.A., con domicilio en la Plaza San Nicolás, número 4, 48005 Bilbao,inscrito en el Registro Mercantil de Vizcaya, al tomo 2.083, Folio 1, Hoja BI-17-A, Inscripción 1ª con C.I.F. A-48265169POLÍTICA DE PROTECCIÓN DE DATOS PERSONALES 1. Política de Protección de Datos Personales T ómate tu tiempo y lee atentamente este documento. No dudes en pedirnos aclaraciones de lo que no entiendas.\nEn este apartado te explicamos para qué utilizará BBVA tus datos y, entre otros aspectos, qué derechos tienes relacionados con su uso.\nINFORMACIÓN BÁSICA SOBRE PROTECCIÓN DE DATOS " + }, + "configs": [ + { + "name": "A: t=0.5 flat loop", + "elapsed": 134.3, + "stats": { + "n_ents": 397, + "n_rels": 71, + "n_nodes": 400, + "n_edges": 71, + "n_isolates": 329, + "connected": 71, + "connect_pct": 17.8 + } + }, + { + "name": "B: t=0.3 flat loop", + "elapsed": 139.0, + "stats": { + "n_ents": 517, + "n_rels": 204, + "n_nodes": 526, + "n_edges": 204, + "n_isolates": 389, + "connected": 137, + "connect_pct": 26.0 + } + }, + { + "name": "C: t=0.2 flat loop", + "elapsed": 133.9, + "stats": { + "n_ents": 632, + "n_rels": 362, + "n_nodes": 610, + "n_edges": 362, + "n_isolates": 397, + "connected": 213, + "connect_pct": 34.9 + } + }, + { + "name": "D: t=0.3 desc loop", + "elapsed": 132.4, + "stats": { + "n_ents": 517, + "n_rels": 204, + "n_nodes": 526, + "n_edges": 204, + "n_isolates": 389, + "connected": 137, + "connect_pct": 26.0 + } + }, + { + "name": "E: t=0.3 desc batch", + "elapsed": 163.6, + "stats": { + "n_ents": 517, + "n_rels": 204, + "n_nodes": 526, + "n_edges": 204, + "n_isolates": 389, + "connected": 137, + "connect_pct": 26.0 + } + } + ], + "coref": { + "elapsed": 0.62, + "pre_stats": { + "n_ents": 517, + "n_rels": 204, + "n_nodes": 526, + "n_edges": 204, + "n_isolates": 389, + "connected": 137, + "connect_pct": 26.0 + }, + "post_stats": { + "n_ents": 401, + "n_rels": 166, + "n_nodes": 440, + "n_edges": 166, + "n_isolates": 318, + "connected": 122, + "connect_pct": 27.7 + }, + "n_absorbed": 72, + "absorbed_sample": [ + [ + "productos y servicios", + "Información derivada de los productos y servicios contratados" + ], + [ + "servicios contratados", + "Información derivada de los productos y servicios contratados" + ], + [ + "información", + "Información derivada de los productos y servicios contratados" + ], + [ + "productos", + "Información derivada de los productos y servicios contratados" + ], + [ + "servicios", + "Información derivada de los productos y servicios contratados" + ], + [ + "normativa", + "normativa interna sobre prevención de crimen financiero" + ], + [ + "blanqueo de capitales", + "normativa de prevención del blanqueo de capitales" + ], + [ + "interacción", + "datos derivados de la interacción con chatbots" + ] + ] + }, + "top_entities_post_coref": [ + { + "type": "organization", + "canonical": "BBVA Seguros", + "mentions": 81, + "n_aliases": 1, + "aliases_sample": [ + "BBVA" + ] + }, + { + "type": "data_category", + "canonical": "Datos Personales", + "mentions": 47, + "n_aliases": 0, + "aliases_sample": [] + }, + { + "type": "person", + "canonical": "cliente particular", + "mentions": 34, + "n_aliases": 1, + "aliases_sample": [ + "cliente" + ] + }, + { + "type": "organization", + "canonical": "Banco de España (CIRBE)", + "mentions": 28, + "n_aliases": 3, + "aliases_sample": [ + "Banco de España", + "Banco", + "CIRBE" + ] + }, + { + "type": "location", + "canonical": "Plaza San Nicolás", + "mentions": 27, + "n_aliases": 0, + "aliases_sample": [] + }, + { + "type": "location", + "canonical": "Vizcaya", + "mentions": 22, + "n_aliases": 0, + "aliases_sample": [] + }, + { + "type": "data_category", + "canonical": "datos derivados de la interacción con chatbots", + "mentions": 19, + "n_aliases": 3, + "aliases_sample": [ + "interacción", + "chatbots", + "datos" + ] + }, + { + "type": "law", + "canonical": "normativa interna sobre prevención de crimen financiero", + "mentions": 19, + "n_aliases": 1, + "aliases_sample": [ + "normativa" + ] + }, + { + "type": "right", + "canonical": "consentimiento", + "mentions": 18, + "n_aliases": 0, + "aliases_sample": [] + }, + { + "type": "data_category", + "canonical": "Datos transaccionales", + "mentions": 18, + "n_aliases": 1, + "aliases_sample": [ + "transaccionales" + ] + }, + { + "type": "data_category", + "canonical": "Información derivada de los productos y servicios contratados", + "mentions": 17, + "n_aliases": 5, + "aliases_sample": [ + "productos y servicios", + "servicios contratados", + "información" + ] + }, + { + "type": "person", + "canonical": "clientes", + "mentions": 15, + "n_aliases": 0, + "aliases_sample": [] + }, + { + "type": "data_category", + "canonical": "Datos identificativos", + "mentions": 14, + "n_aliases": 0, + "aliases_sample": [] + }, + { + "type": "email", + "canonical": "derechosprotecciondatos@bbva.com", + "mentions": 14, + "n_aliases": 0, + "aliases_sample": [] + }, + { + "type": "data_category", + "canonical": "número de teléfono de contacto", + "mentions": 13, + "n_aliases": 1, + "aliases_sample": [ + "contacto" + ] + }, + { + "type": "person", + "canonical": "representante", + "mentions": 12, + "n_aliases": 0, + "aliases_sample": [] + }, + { + "type": "organization", + "canonical": "Agencia Española de Protección de Datos", + "mentions": 12, + "n_aliases": 0, + "aliases_sample": [] + }, + { + "type": "organization", + "canonical": "sociedades participadas", + "mentions": 11, + "n_aliases": 2, + "aliases_sample": [ + "participadas", + "sociedades" + ] + }, + { + "type": "person", + "canonical": "garante", + "mentions": 11, + "n_aliases": 0, + "aliases_sample": [] + }, + { + "type": "data_category", + "canonical": "Datos económicos", + "mentions": 11, + "n_aliases": 1, + "aliases_sample": [ + "económicos" + ] + }, + { + "type": "right", + "canonical": "derecho de oposición", + "mentions": 11, + "n_aliases": 1, + "aliases_sample": [ + "oposición" + ] + }, + { + "type": "location", + "canonical": "España", + "mentions": 10, + "n_aliases": 0, + "aliases_sample": [] + }, + { + "type": "data_category", + "canonical": "datos de geolocalización", + "mentions": 10, + "n_aliases": 1, + "aliases_sample": [ + "geolocalización" + ] + }, + { + "type": "email", + "canonical": "dirección de email", + "mentions": 10, + "n_aliases": 1, + "aliases_sample": [ + "email" + ] + }, + { + "type": "right", + "canonical": "DERECHO CONTENIDO Acceso", + "mentions": 10, + "n_aliases": 1, + "aliases_sample": [ + "acceso" + ] + } + ], + "top_relations_post_coref": [ + { + "from": "BBVA Seguros", + "kind": "governed_by", + "to": "Banco de España (CIRBE)", + "count": 4 + }, + { + "from": "Datos Personales", + "kind": "protected_by", + "to": "Agencia Española de Protección de Datos", + "count": 4 + }, + { + "from": "Datos Personales", + "kind": "protected_by", + "to": "Política de Protección de Datos Personales", + "count": 3 + }, + { + "from": "BBVA Seguros", + "kind": "subject_to", + "to": "obligaciones legales", + "count": 3 + }, + { + "from": "derechos de acceso", + "kind": "rights_against", + "to": "datos derivados de la interacción con chatbots", + "count": 3 + }, + { + "from": "contratación", + "kind": "controlled_by", + "to": "BBVA Seguros", + "count": 3 + }, + { + "from": "BBVA Seguros", + "kind": "subsidiary_of", + "to": "Grupo BBVA", + "count": 2 + }, + { + "from": "Datos Personales", + "kind": "protected_by", + "to": "BBVA Seguros", + "count": 2 + }, + { + "from": "BBVA Seguros", + "kind": "contact_for", + "to": "Información derivada de los productos y servicios contratados", + "count": 2 + }, + { + "from": "Delegado de Protección de Datos", + "kind": "contact_for", + "to": "BBVA Seguros", + "count": 2 + }, + { + "from": "BBVA Seguros", + "kind": "controlled_by", + "to": "Banco de España (CIRBE)", + "count": 2 + }, + { + "from": "domicilio", + "kind": "located_in", + "to": "Plaza San Nicolás", + "count": 2 + }, + { + "from": "datos de contacto", + "kind": "contact_for", + "to": "clientes", + "count": 2 + }, + { + "from": "BBVA Seguros", + "kind": "located_in", + "to": "España", + "count": 2 + }, + { + "from": "contratos de crédito inmobiliario", + "kind": "governed_by", + "to": "Ley 5/2019", + "count": 2 + }, + { + "from": "Avda. de la Industria", + "kind": "located_in", + "to": "MADRID", + "count": 2 + }, + { + "from": "bbva.es", + "kind": "located_in", + "to": "MADRID", + "count": 2 + }, + { + "from": "datos derivados de la interacción con chatbots", + "kind": "subject_to", + "to": "normativa interna sobre prevención de crimen financiero", + "count": 2 + }, + { + "from": "Datos Personales", + "kind": "subject_to", + "to": "normativa interna sobre prevención de crimen financiero", + "count": 2 + }, + { + "from": "Emailage Corporation", + "kind": "located_in", + "to": "Londres", + "count": 2 + }, + { + "from": "Telesign Corporation", + "kind": "located_in", + "to": "Estados Unidos", + "count": 2 + }, + { + "from": "Emailage Corporation", + "kind": "controlled_by", + "to": "BBVA Seguros", + "count": 2 + }, + { + "from": "BBVA Seguros", + "kind": "subsidiary_of", + "to": "fichero FrauDfense", + "count": 2 + }, + { + "from": "Datos Personales", + "kind": "subject_to", + "to": "consentimiento", + "count": 2 + }, + { + "from": "datos derivados de la interacción con chatbots", + "kind": "protected_by", + "to": "Información básica de protección de datos", + "count": 2 + } + ], + "ents_merged": [ + { + "name": "sociedades participadas", + "type": "organization", + "count": 11 + }, + { + "name": "Plaza San Nicolás", + "type": "location", + "count": 27 + }, + { + "name": "España", + "type": "location", + "count": 10 + }, + { + "name": "Plaza de San Nicolás", + "type": "location", + "count": 3 + }, + { + "name": "consultasgenerales@bbva.com", + "type": "email", + "count": 2 + }, + { + "name": "Datos Personales", + "type": "data_category", + "count": 47 + }, + { + "name": "perfiles básicos", + "type": "data_category", + "count": 2 + }, + { + "name": "información crediticia", + "type": "data_category", + "count": 1 + }, + { + "name": "obligaciones legales", + "type": "law", + "count": 7 + }, + { + "name": "representante", + "type": "person", + "count": 12 + }, + { + "name": "cónyuge", + "type": "person", + "count": 7 + }, + { + "name": "garante", + "type": "person", + "count": 11 + }, + { + "name": "autorizado", + "type": "authority", + "count": 8 + }, + { + "name": "gdpr", + "type": "law", + "count": 1 + }, + { + "name": "Vizcaya", + "type": "location", + "count": 22 + }, + { + "name": "Bilbao", + "type": "location", + "count": 4 + }, + { + "name": "Registro Mercantil de Vizcaya", + "type": "authority", + "count": 8 + }, + { + "name": "colaboradoras", + "type": "organization", + "count": 4 + }, + { + "name": "oficina", + "type": "location", + "count": 1 + }, + { + "name": "gestor", + "type": "location", + "count": 1 + }, + { + "name": "consentimiento", + "type": "right", + "count": 18 + }, + { + "name": "hipotecante no deudor", + "type": "person", + "count": 2 + }, + { + "name": "Banco Bilbao", + "type": "organization", + "count": 3 + }, + { + "name": "Política de Protección de Datos Personales", + "type": "law", + "count": 1 + }, + { + "name": "nacionalidad", + "type": "data_category", + "count": 2 + }, + { + "name": "fecha de nacimiento", + "type": "data_category", + "count": 5 + }, + { + "name": "fotografía", + "type": "data_category", + "count": 2 + }, + { + "name": "país de nacimiento", + "type": "data_category", + "count": 2 + }, + { + "name": "imagen", + "type": "data_category", + "count": 2 + }, + { + "name": "DNI", + "type": "data_category", + "count": 5 + }, + { + "name": "nombre", + "type": "data_category", + "count": 8 + }, + { + "name": "voz", + "type": "data_category", + "count": 2 + }, + { + "name": "direcciones electrónicas", + "type": "data_category", + "count": 2 + }, + { + "name": "apellidos", + "type": "data_category", + "count": 6 + }, + { + "name": "direcciones postales", + "type": "data_category", + "count": 4 + }, + { + "name": "teléfono fijo", + "type": "data_category", + "count": 2 + }, + { + "name": "pasaporte", + "type": "data_category", + "count": 5 + }, + { + "name": "NIE", + "type": "data_category", + "count": 4 + }, + { + "name": "Agencia Tributaria", + "type": "organization", + "count": 4 + }, + { + "name": "Tesorería General de la Seguridad Social", + "type": "organization", + "count": 4 + }, + { + "name": "vida laboral", + "type": "data_category", + "count": 2 + }, + { + "name": "Datos económicos", + "type": "data_category", + "count": 11 + }, + { + "name": "declaración de IRPF", + "type": "data_category", + "count": 2 + }, + { + "name": "Datos de firma", + "type": "data_category", + "count": 7 + }, + { + "name": "situación financiera", + "type": "data_category", + "count": 1 + }, + { + "name": "nómina domiciliada", + "type": "data_category", + "count": 1 + }, + { + "name": "calificación financiera", + "type": "data_category", + "count": 1 + }, + { + "name": "Datos identificativos", + "type": "data_category", + "count": 14 + }, + { + "name": "claves de identificación", + "type": "data_category", + "count": 2 + }, + { + "name": "objetivos de inversión", + "type": "data_category", + "count": 1 + }, + { + "name": "recibos domiciliados", + "type": "data_category", + "count": 1 + }, + { + "name": "Códigos", + "type": "data_category", + "count": 3 + }, + { + "name": "saldo de activo", + "type": "data_category", + "count": 1 + }, + { + "name": "deuda", + "type": "data_category", + "count": 3 + }, + { + "name": "ingresos y gastos", + "type": "data_category", + "count": 1 + }, + { + "name": "gestión del cobro", + "type": "data_category", + "count": 1 + }, + { + "name": "ingresos netos mensuales", + "type": "data_category", + "count": 2 + }, + { + "name": "bienes y patrimonio", + "type": "data_category", + "count": 1 + }, + { + "name": "domiciliaciones", + "type": "data_category", + "count": 1 + }, + { + "name": "adeudos", + "type": "data_category", + "count": 3 + }, + { + "name": "saldos", + "type": "data_category", + "count": 1 + }, + { + "name": "situación familiar", + "type": "data_category", + "count": 3 + }, + { + "name": "residencias", + "type": "data_category", + "count": 4 + }, + { + "name": "edad", + "type": "data_category", + "count": 3 + }, + { + "name": "Datos sociodemográficos", + "type": "data_category", + "count": 9 + }, + { + "name": "riesgos", + "type": "data_category", + "count": 2 + }, + { + "name": "estado civil", + "type": "data_category", + "count": 3 + }, + { + "name": "concepto", + "type": "data_category", + "count": 4 + }, + { + "name": "origen", + "type": "data_category", + "count": 3 + }, + { + "name": "experiencia profesional", + "type": "data_category", + "count": 7 + }, + { + "name": "pertenencia a colectivos", + "type": "data_category", + "count": 4 + }, + { + "name": "Datos transaccionales", + "type": "data_category", + "count": 18 + }, + { + "name": "preferencias de sostenibilidad", + "type": "data_category", + "count": 8 + }, + { + "name": "empleador", + "type": "data_category", + "count": 2 + }, + { + "name": "antigüedad en el empleo", + "type": "data_category", + "count": 3 + }, + { + "name": "tipo de contrato", + "type": "data_category", + "count": 2 + }, + { + "name": "entidades agregadas", + "type": "organization", + "count": 1 + }, + { + "name": "datos de contrato", + "type": "data_category", + "count": 1 + }, + { + "name": "tipo de ocupación", + "type": "data_category", + "count": 5 + }, + { + "name": "cuenta de domiciliación", + "type": "data_category", + "count": 2 + }, + { + "name": "documentación", + "type": "data_category", + "count": 1 + }, + { + "name": "Datos de productos y servicios contratados", + "type": "data_category", + "count": 6 + }, + { + "name": "intervinientes", + "type": "data_category", + "count": 1 + }, + { + "name": "número de contrato", + "type": "data_category", + "count": 1 + }, + { + "name": "límite", + "type": "data_category", + "count": 1 + }, + { + "name": "fecha y hora", + "type": "data_category", + "count": 3 + }, + { + "name": "tipología de productos y servicios contratados", + "type": "data_category", + "count": 1 + }, + { + "name": "correo electrónico", + "type": "email", + "count": 7 + }, + { + "name": "datos de comunicaciones", + "type": "data_category", + "count": 5 + }, + { + "name": "dirección IP", + "type": "data_category", + "count": 4 + }, + { + "name": "navegador", + "type": "data_category", + "count": 2 + }, + { + "name": "ID del dispositivo", + "type": "data_category", + "count": 2 + }, + { + "name": "versión", + "type": "data_category", + "count": 3 + }, + { + "name": "accesos", + "type": "data_category", + "count": 2 + }, + { + "name": "Datos del uso", + "type": "data_category", + "count": 3 + }, + { + "name": "sistema operativo", + "type": "data_category", + "count": 2 + }, + { + "name": "mensajería instantánea", + "type": "data_category", + "count": 2 + }, + { + "name": "interfaces conversacionales", + "type": "data_category", + "count": 2 + }, + { + "name": "encuestas de satisfacción", + "type": "data_category", + "count": 4 + }, + { + "name": "redes sociales", + "type": "data_category", + "count": 2 + }, + { + "name": "fabricante", + "type": "data_category", + "count": 1 + }, + { + "name": "Banco de España (CIRBE)", + "type": "organization", + "count": 28 + }, + { + "name": "datos de geolocalización", + "type": "data_category", + "count": 10 + }, + { + "name": "Información de solvencia", + "type": "data_category", + "count": 3 + }, + { + "name": "datos de navegación", + "type": "data_category", + "count": 2 + }, + { + "name": "comentarios", + "type": "right", + "count": 8 + }, + { + "name": "Información derivada de los productos y servicios contratados", + "type": "data_category", + "count": 17 + }, + { + "name": "datos derivados de la interacción con chatbots", + "type": "data_category", + "count": 19 + }, + { + "name": "contactos", + "type": "data_category", + "count": 1 + }, + { + "name": "Central de Información de Riesgos", + "type": "authority", + "count": 4 + }, + { + "name": "Equifax Ibérica, S.L.", + "type": "organization", + "count": 4 + }, + { + "name": "Iberpay", + "type": "organization", + "count": 7 + }, + { + "name": "Asnef-Equifax", + "type": "organization", + "count": 2 + }, + { + "name": "viabilidad económica", + "type": "data_category", + "count": 1 + }, + { + "name": "transacciones", + "type": "data_category", + "count": 7 + }, + { + "name": "colegios profesionales", + "type": "organization", + "count": 2 + }, + { + "name": "plataformas", + "type": "organization", + "count": 1 + }, + { + "name": "EUROTAX", + "type": "organization", + "count": 5 + }, + { + "name": "centrales de medios", + "type": "organization", + "count": 1 + }, + { + "name": "proveedores", + "type": "organization", + "count": 3 + }, + { + "name": "compañías de servicios", + "type": "organization", + "count": 2 + }, + { + "name": "Catastro", + "type": "organization", + "count": 2 + }, + { + "name": "Madiva Soluciones", + "type": "organization", + "count": 2 + }, + { + "name": "Administraciones Públicas", + "type": "authority", + "count": 3 + }, + { + "name": "diarios", + "type": "organization", + "count": 2 + }, + { + "name": "web", + "type": "location", + "count": 2 + }, + { + "name": "electrónicas", + "type": "email", + "count": 2 + }, + { + "name": "Datos de comportamiento digital", + "type": "data_category", + "count": 3 + }, + { + "name": "valoración de inmuebles", + "type": "data_category", + "count": 1 + }, + { + "name": "tasaciones de vehículos", + "type": "data_category", + "count": 5 + }, + { + "name": "entrega de bienes", + "type": "data_category", + "count": 1 + }, + { + "name": "listas de personas", + "type": "data_category", + "count": 2 + }, + { + "name": "cookies", + "type": "data_category", + "count": 2 + }, + { + "name": "guías telefónicas", + "type": "data_category", + "count": 1 + }, + { + "name": "activos", + "type": "data_category", + "count": 1 + }, + { + "name": "firma digitalizada", + "type": "data_category", + "count": 2 + }, + { + "name": "bienes muebles", + "type": "data_category", + "count": 1 + }, + { + "name": "dispositivos", + "type": "data_category", + "count": 2 + }, + { + "name": "registros públicos", + "type": "data_category", + "count": 2 + }, + { + "name": "política de cookies", + "type": "law", + "count": 3 + }, + { + "name": "cliente particular", + "type": "person", + "count": 34 + }, + { + "name": "personas jurídicas", + "type": "person", + "count": 3 + }, + { + "name": "Información sobre conocimientos", + "type": "data_category", + "count": 4 + }, + { + "name": "Datos obtenidos de otras fuentes", + "type": "data_category", + "count": 2 + }, + { + "name": "Datos sobre la condición de representante", + "type": "data_category", + "count": 1 + }, + { + "name": "solvencia patrimonial", + "type": "data_category", + "count": 3 + }, + { + "name": "electrónica", + "type": "data_category", + "count": 1 + }, + { + "name": "Datos de otros productos", + "type": "data_category", + "count": 1 + }, + { + "name": "evolución financiera", + "type": "data_category", + "count": 2 + }, + { + "name": "datos financieros", + "type": "data_category", + "count": 3 + }, + { + "name": "notificaciones no comerciales", + "type": "law", + "count": 1 + }, + { + "name": "proveedor", + "type": "organization", + "count": 2 + }, + { + "name": "clientes", + "type": "person", + "count": 15 + }, + { + "name": "Solicitante", + "type": "person", + "count": 4 + }, + { + "name": "datos de identificación", + "type": "data_category", + "count": 2 + }, + { + "name": "datos de contacto", + "type": "data_category", + "count": 5 + }, + { + "name": "Reaseguros", + "type": "organization", + "count": 1 + }, + { + "name": "empresario individual", + "type": "person", + "count": 1 + }, + { + "name": "cuentas corrientes", + "type": "data_category", + "count": 2 + }, + { + "name": "cuentas de pago", + "type": "data_category", + "count": 1 + }, + { + "name": "cuentas de ahorro", + "type": "data_category", + "count": 1 + }, + { + "name": "depósitos", + "type": "data_category", + "count": 1 + }, + { + "name": "Ley 44/2002", + "type": "law", + "count": 1 + }, + { + "name": "ti", + "type": "person", + "count": 1 + }, + { + "name": "Sepblac", + "type": "organization", + "count": 2 + }, + { + "name": "Unión Europea", + "type": "location", + "count": 7 + }, + { + "name": "Secretaría de Estado de Economía", + "type": "organization", + "count": 3 + }, + { + "name": "autoridades nacionales", + "type": "authority", + "count": 3 + }, + { + "name": "legislación", + "type": "law", + "count": 2 + }, + { + "name": "Banco Central Europeo", + "type": "organization", + "count": 4 + }, + { + "name": "Autoridad Bancaria Europea", + "type": "organization", + "count": 5 + }, + { + "name": "empresas de inversión", + "type": "organization", + "count": 4 + }, + { + "name": "entidades financieras", + "type": "organization", + "count": 6 + }, + { + "name": "países", + "type": "location", + "count": 1 + }, + { + "name": "perfiles de riesgo", + "type": "data_category", + "count": 2 + }, + { + "name": "Ley 10/ 2010", + "type": "law", + "count": 1 + }, + { + "name": "Ley 5/2015", + "type": "law", + "count": 1 + }, + { + "name": "entidades de crédito", + "type": "organization", + "count": 1 + }, + { + "name": "impugnar la decisión", + "type": "right", + "count": 6 + }, + { + "name": "perfil inversor", + "type": "data_category", + "count": 1 + }, + { + "name": "operador", + "type": "authority", + "count": 6 + }, + { + "name": "Servicios de Inversión", + "type": "organization", + "count": 1 + }, + { + "name": "servicios de asesoramiento", + "type": "organization", + "count": 1 + }, + { + "name": "Reglamento Delegado 217/565", + "type": "law", + "count": 2 + }, + { + "name": "Ley 6/2023", + "type": "law", + "count": 2 + }, + { + "name": "Ley de los Mercados de Valores", + "type": "law", + "count": 2 + }, + { + "name": "Experian", + "type": "organization", + "count": 1 + }, + { + "name": "comunicación", + "type": "right", + "count": 1 + }, + { + "name": "deudas", + "type": "data_category", + "count": 1 + }, + { + "name": "créditos al consumo", + "type": "data_category", + "count": 2 + }, + { + "name": "arbitral", + "type": "authority", + "count": 1 + }, + { + "name": "judicial", + "type": "authority", + "count": 1 + }, + { + "name": "administrativa", + "type": "authority", + "count": 1 + }, + { + "name": "criterios de crédito responsable", + "type": "law", + "count": 1 + }, + { + "name": "entidad", + "type": "organization", + "count": 2 + }, + { + "name": "oficinas", + "type": "location", + "count": 7 + }, + { + "name": "derechosprotecciondatos@bbva.com", + "type": "email", + "count": 14 + }, + { + "name": "derecho de oposición", + "type": "right", + "count": 11 + }, + { + "name": "información general", + "type": "data_category", + "count": 1 + }, + { + "name": "Ley 5/2019", + "type": "law", + "count": 2 + }, + { + "name": "encargado del tratamiento", + "type": "person", + "count": 2 + }, + { + "name": "MADRID", + "type": "location", + "count": 6 + }, + { + "name": "Avda. de la Industria", + "type": "location", + "count": 2 + }, + { + "name": "cuenta corriente", + "type": "data_category", + "count": 2 + }, + { + "name": "financiación", + "type": "data_category", + "count": 1 + }, + { + "name": "Reglamento del Fichero Confirma", + "type": "law", + "count": 4 + }, + { + "name": "Confirma Sistemas de Información", + "type": "organization", + "count": 4 + }, + { + "name": "dpogrupobbva@bbva.com", + "type": "email", + "count": 3 + }, + { + "name": "identidad", + "type": "right", + "count": 3 + }, + { + "name": "documentos", + "type": "data_category", + "count": 2 + }, + { + "name": "fraude", + "type": "law", + "count": 1 + }, + { + "name": "sociedad", + "type": "organization", + "count": 1 + }, + { + "name": "entidades adheridas", + "type": "organization", + "count": 6 + }, + { + "name": "confidencialidad", + "type": "right", + "count": 1 + }, + { + "name": "seguridad", + "type": "right", + "count": 1 + }, + { + "name": "privacidad", + "type": "right", + "count": 1 + }, + { + "name": "Agencia Española de Protección de Datos", + "type": "organization", + "count": 12 + }, + { + "name": "tercer país", + "type": "location", + "count": 1 + }, + { + "name": "dpo@confirmasistemas.es", + "type": "email", + "count": 2 + }, + { + "name": "rectificación", + "type": "right", + "count": 5 + }, + { + "name": "supresión", + "type": "right", + "count": 5 + }, + { + "name": "limitación del tratamiento", + "type": "right", + "count": 5 + }, + { + "name": "normativa vigente en materia de protección de datos", + "type": "law", + "count": 1 + }, + { + "name": "afectado", + "type": "person", + "count": 3 + }, + { + "name": "titulares", + "type": "person", + "count": 4 + }, + { + "name": "número de IBAN", + "type": "data_category", + "count": 2 + }, + { + "name": "titular de la cuenta", + "type": "data_category", + "count": 1 + }, + { + "name": "correo postal", + "type": "email", + "count": 1 + }, + { + "name": "derecho a la portabilidad", + "type": "right", + "count": 5 + }, + { + "name": "Delegado de Protección de Datos", + "type": "authority", + "count": 2 + }, + { + "name": "titular beneficiario", + "type": "person", + "count": 7 + }, + { + "name": "bancos", + "type": "organization", + "count": 1 + }, + { + "name": "ámbito UE", + "type": "location", + "count": 1 + }, + { + "name": "reclamación", + "type": "right", + "count": 3 + }, + { + "name": "comportamientos", + "type": "data_category", + "count": 1 + }, + { + "name": "patrones de riesgo", + "type": "data_category", + "count": 1 + }, + { + "name": "patrones de fraude", + "type": "data_category", + "count": 1 + }, + { + "name": "cuenta de destino", + "type": "data_category", + "count": 5 + }, + { + "name": "operaciones anómalas", + "type": "data_category", + "count": 1 + }, + { + "name": "anomalías", + "type": "data_category", + "count": 3 + }, + { + "name": "prevención del fraude", + "type": "right", + "count": 2 + }, + { + "name": "herramientas", + "type": "organization", + "count": 1 + }, + { + "name": "indicadores", + "type": "data_category", + "count": 1 + }, + { + "name": "contraparte", + "type": "person", + "count": 1 + }, + { + "name": "Telesign Corporation", + "type": "organization", + "count": 8 + }, + { + "name": "Emailage Corporation", + "type": "organization", + "count": 8 + }, + { + "name": "Londres", + "type": "location", + "count": 2 + }, + { + "name": "Marina Del Rey", + "type": "location", + "count": 2 + }, + { + "name": "Covent Garden", + "type": "location", + "count": 2 + }, + { + "name": "Reino Unido", + "type": "location", + "count": 2 + }, + { + "name": "16 Great Queen Street", + "type": "location", + "count": 2 + }, + { + "name": "Estados Unidos", + "type": "location", + "count": 4 + }, + { + "name": "CA 90292", + "type": "location", + "count": 2 + }, + { + "name": "dirección de IP", + "type": "data_category", + "count": 2 + }, + { + "name": "número de teléfono de contacto", + "type": "data_category", + "count": 13 + }, + { + "name": "datos de teléfono móvil", + "type": "data_category", + "count": 4 + }, + { + "name": "dirección de correo electrónico", + "type": "data_category", + "count": 1 + }, + { + "name": "fichero FrauDfense", + "type": "organization", + "count": 4 + }, + { + "name": "página web", + "type": "location", + "count": 1 + }, + { + "name": "protecciondedatos@fraudfense.com", + "type": "email", + "count": 3 + }, + { + "name": "FrauDfense, S.L.", + "type": "organization", + "count": 2 + }, + { + "name": "Contacto DPO", + "type": "data_category", + "count": 1 + }, + { + "name": "C.I.F.", + "type": "law", + "count": 3 + }, + { + "name": "terceras personas", + "type": "person", + "count": 3 + }, + { + "name": "información falsa", + "type": "data_category", + "count": 1 + }, + { + "name": "entidades bancarias", + "type": "organization", + "count": 3 + }, + { + "name": "TPV", + "type": "authority", + "count": 3 + }, + { + "name": "credenciales", + "type": "data_category", + "count": 1 + }, + { + "name": "dirección postal", + "type": "location", + "count": 2 + }, + { + "name": "datos de medios de pago", + "type": "data_category", + "count": 3 + }, + { + "name": "datos de localización", + "type": "data_category", + "count": 7 + }, + { + "name": "documento identificativo", + "type": "data_category", + "count": 3 + }, + { + "name": "datos básicos", + "type": "data_category", + "count": 4 + }, + { + "name": "tarjeta bancaria", + "type": "data_category", + "count": 1 + }, + { + "name": "autoridades competentes", + "type": "authority", + "count": 2 + }, + { + "name": "información de navegación", + "type": "data_category", + "count": 5 + }, + { + "name": "fuentes estadísticas", + "type": "data_category", + "count": 5 + }, + { + "name": "Bilbao Vizcaya Argentaria, S.A.", + "type": "organization", + "count": 1 + }, + { + "name": "Banco de España -CIRBE-", + "type": "organization", + "count": 1 + }, + { + "name": "préstamos", + "type": "data_category", + "count": 1 + }, + { + "name": "tarjetas", + "type": "data_category", + "count": 4 + }, + { + "name": "perfil básico", + "type": "data_category", + "count": 2 + }, + { + "name": "acceso público", + "type": "right", + "count": 1 + }, + { + "name": "obligatorias", + "type": "right", + "count": 1 + }, + { + "name": "canales digitales", + "type": "data_category", + "count": 2 + }, + { + "name": "iniciación de pagos", + "type": "data_category", + "count": 4 + }, + { + "name": "emisiones de CO2", + "type": "data_category", + "count": 3 + }, + { + "name": "transporte", + "type": "data_category", + "count": 2 + }, + { + "name": "luz", + "type": "data_category", + "count": 4 + }, + { + "name": "gas", + "type": "data_category", + "count": 4 + }, + { + "name": "huella de carbono", + "type": "data_category", + "count": 4 + }, + { + "name": "dato del emisor", + "type": "data_category", + "count": 1 + }, + { + "name": "vehículo eléctrico", + "type": "data_category", + "count": 1 + }, + { + "name": "paneles solares", + "type": "data_category", + "count": 1 + }, + { + "name": "vivienda habitual", + "type": "location", + "count": 2 + }, + { + "name": "impuestos", + "type": "data_category", + "count": 2 + }, + { + "name": "agua", + "type": "data_category", + "count": 2 + }, + { + "name": "seguros", + "type": "data_category", + "count": 2 + }, + { + "name": "parkings", + "type": "data_category", + "count": 2 + }, + { + "name": "peajes", + "type": "data_category", + "count": 2 + }, + { + "name": "taller", + "type": "data_category", + "count": 2 + }, + { + "name": "gasolina", + "type": "data_category", + "count": 2 + }, + { + "name": "recibos bancarios domiciliados", + "type": "data_category", + "count": 8 + }, + { + "name": "consumo energético", + "type": "data_category", + "count": 2 + }, + { + "name": "ahorro energético", + "type": "data_category", + "count": 1 + }, + { + "name": "domicilios", + "type": "location", + "count": 1 + }, + { + "name": "información estadística", + "type": "data_category", + "count": 2 + }, + { + "name": "MyHome", + "type": "organization", + "count": 2 + }, + { + "name": "MyCar", + "type": "organization", + "count": 2 + }, + { + "name": "modelo", + "type": "data_category", + "count": 2 + }, + { + "name": "tipo de combustible", + "type": "data_category", + "count": 1 + }, + { + "name": "marca", + "type": "data_category", + "count": 1 + }, + { + "name": "kilometraje", + "type": "data_category", + "count": 1 + }, + { + "name": "dirección de la vivienda habitual", + "type": "data_category", + "count": 1 + }, + { + "name": "vehículos eléctricos", + "type": "data_category", + "count": 1 + }, + { + "name": "solución sostenible", + "type": "data_category", + "count": 1 + }, + { + "name": "hogar", + "type": "data_category", + "count": 1 + }, + { + "name": "políticas de privacidad", + "type": "law", + "count": 1 + }, + { + "name": "usuario", + "type": "person", + "count": 1 + }, + { + "name": "impacto medioambiental", + "type": "data_category", + "count": 1 + }, + { + "name": "ciudadanos", + "type": "person", + "count": 2 + }, + { + "name": "derecho fundamental", + "type": "right", + "count": 4 + }, + { + "name": "protección de datos personales", + "type": "right", + "count": 1 + }, + { + "name": "BBVA (i)", + "type": "organization", + "count": 1 + }, + { + "name": "app", + "type": "location", + "count": 1 + }, + { + "name": "dirección de email", + "type": "email", + "count": 10 + }, + { + "name": "perfil comercial", + "type": "data_category", + "count": 1 + }, + { + "name": "solvencia económica", + "type": "data_category", + "count": 1 + }, + { + "name": "textos", + "type": "data_category", + "count": 1 + }, + { + "name": "capacidad patrimonial", + "type": "data_category", + "count": 1 + }, + { + "name": "movimientos de cuentas", + "type": "data_category", + "count": 5 + }, + { + "name": "servicios de agregación", + "type": "data_category", + "count": 2 + }, + { + "name": "datos de fuentes públicas", + "type": "data_category", + "count": 1 + }, + { + "name": "plazo de vigencia", + "type": "data_category", + "count": 2 + }, + { + "name": "capacidad de ahorro", + "type": "data_category", + "count": 2 + }, + { + "name": "endeudamiento", + "type": "data_category", + "count": 1 + }, + { + "name": "Cámaras de Comercio", + "type": "organization", + "count": 1 + }, + { + "name": "dispositivo móvil", + "type": "location", + "count": 1 + }, + { + "name": "Fuentes de acceso público", + "type": "data_category", + "count": 1 + }, + { + "name": "Información de perfiles", + "type": "data_category", + "count": 8 + }, + { + "name": "categorías especiales de datos", + "type": "data_category", + "count": 1 + }, + { + "name": "perfil personal", + "type": "data_category", + "count": 1 + }, + { + "name": "comunicaciones comerciales", + "type": "right", + "count": 1 + }, + { + "name": "normativa de servicios", + "type": "law", + "count": 1 + }, + { + "name": "Grupo BBVA", + "type": "organization", + "count": 8 + }, + { + "name": "email a", + "type": "email", + "count": 1 + }, + { + "name": "terceros", + "type": "organization", + "count": 2 + }, + { + "name": "estudios de mercado", + "type": "data_category", + "count": 5 + }, + { + "name": "medias", + "type": "data_category", + "count": 1 + }, + { + "name": "cálculos actuariales", + "type": "data_category", + "count": 1 + }, + { + "name": "correspondencia", + "type": "data_category", + "count": 1 + }, + { + "name": "datos seudonimizados", + "type": "data_category", + "count": 1 + }, + { + "name": "información económica", + "type": "data_category", + "count": 2 + }, + { + "name": "perfiles personales", + "type": "data_category", + "count": 2 + }, + { + "name": "garantía", + "type": "right", + "count": 1 + }, + { + "name": "relación", + "type": "right", + "count": 1 + }, + { + "name": "obligación legal", + "type": "law", + "count": 5 + }, + { + "name": "poderes", + "type": "right", + "count": 1 + }, + { + "name": "facultades", + "type": "right", + "count": 1 + }, + { + "name": "instrucciones", + "type": "right", + "count": 1 + }, + { + "name": "medidas precontractuales", + "type": "law", + "count": 3 + }, + { + "name": "ejecución contractual", + "type": "law", + "count": 3 + }, + { + "name": "interés legítimo", + "type": "law", + "count": 1 + }, + { + "name": "persona jurídica", + "type": "person", + "count": 1 + }, + { + "name": "derechos digitales", + "type": "right", + "count": 6 + }, + { + "name": "artículo 19", + "type": "law", + "count": 1 + }, + { + "name": "Ley Orgánica 3/2018", + "type": "law", + "count": 1 + }, + { + "name": "financiación del terrorismo", + "type": "law", + "count": 2 + }, + { + "name": "reclamaciones", + "type": "right", + "count": 1 + }, + { + "name": "acciones legales", + "type": "right", + "count": 1 + }, + { + "name": "regulación", + "type": "law", + "count": 1 + }, + { + "name": "normativa de prevención del blanqueo de capitales", + "type": "law", + "count": 3 + }, + { + "name": "plazos de prescripción legal", + "type": "law", + "count": 1 + }, + { + "name": "Comisión Nacional del Mercado de Valores", + "type": "organization", + "count": 3 + }, + { + "name": "EBA", + "type": "organization", + "count": 1 + }, + { + "name": "Fichero de Titularidades Financieras", + "type": "organization", + "count": 3 + }, + { + "name": "productos contratados", + "type": "data_category", + "count": 1 + }, + { + "name": "autoridades de supervisión nacionales", + "type": "authority", + "count": 1 + }, + { + "name": "normativa de prevención de blanqueo de capitales", + "type": "law", + "count": 2 + }, + { + "name": "normativa interna sobre prevención de crimen financiero", + "type": "law", + "count": 19 + }, + { + "name": "Ministerio Fiscal", + "type": "organization", + "count": 4 + }, + { + "name": "sistemas de información crediticia", + "type": "organization", + "count": 1 + }, + { + "name": "garantías", + "type": "right", + "count": 1 + }, + { + "name": "Jueces", + "type": "authority", + "count": 2 + }, + { + "name": "Tribunales", + "type": "authority", + "count": 2 + }, + { + "name": "BBVA Seguros", + "type": "organization", + "count": 81 + }, + { + "name": "interesados", + "type": "person", + "count": 1 + }, + { + "name": "potenciales adquirentes", + "type": "person", + "count": 1 + }, + { + "name": "Comisión Europea", + "type": "authority", + "count": 3 + }, + { + "name": "revisión", + "type": "right", + "count": 1 + }, + { + "name": "transferencias internacionales de datos", + "type": "data_category", + "count": 6 + }, + { + "name": "cláusulas contractuales", + "type": "law", + "count": 1 + }, + { + "name": "DERECHO CONTENIDO Acceso", + "type": "right", + "count": 10 + }, + { + "name": "ficheros", + "type": "data_category", + "count": 1 + }, + { + "name": "ley", + "type": "law", + "count": 1 + }, + { + "name": "limitación al tratamiento", + "type": "right", + "count": 1 + }, + { + "name": "decisiones automatizadas", + "type": "authority", + "count": 1 + }, + { + "name": "ilícito", + "type": "law", + "count": 1 + }, + { + "name": "derechosprotecciondatos@bbva.comServicio", + "type": "email", + "count": 1 + }, + { + "name": "opciones", + "type": "data_category", + "count": 1 + }, + { + "name": "licitud del tratamiento", + "type": "law", + "count": 1 + } + ], + "rels_merged": [ + { + "from": "BBVA Seguros", + "kind": "subject_to", + "to": "Política de Protección de Datos Personales", + "count": 1 + }, + { + "from": "datos derivados de la interacción con chatbots", + "kind": "protected_by", + "to": "Política de Protección de Datos Personales", + "count": 1 + }, + { + "from": "consultasgenerales@bbva.com", + "kind": "contact_for", + "to": "BBVA Seguros", + "count": 1 + }, + { + "from": "Datos Personales", + "kind": "governed_by", + "to": "BBVA Seguros", + "count": 1 + }, + { + "from": "BBVA Seguros", + "kind": "subsidiary_of", + "to": "Grupo BBVA", + "count": 2 + }, + { + "from": "Datos Personales", + "kind": "protected_by", + "to": "BBVA Seguros", + "count": 2 + }, + { + "from": "BBVA Seguros", + "kind": "contact_for", + "to": "Información derivada de los productos y servicios contratados", + "count": 2 + }, + { + "from": "BBVA Seguros", + "kind": "located_in", + "to": "Información adicional", + "count": 1 + }, + { + "from": "BBVA Seguros", + "kind": "located_in", + "to": "Bilbao", + "count": 1 + }, + { + "from": "Datos Personales", + "kind": "protected_by", + "to": "Política de Protección de Datos Personales", + "count": 3 + }, + { + "from": "Delegado de Protección de Datos", + "kind": "contact_for", + "to": "BBVA Seguros", + "count": 2 + }, + { + "from": "BBVA Seguros", + "kind": "subsidiary_of", + "to": "Bilbao Vizcaya Argentaria, S.A.", + "count": 1 + }, + { + "from": "vida laboral", + "kind": "controlled_by", + "to": "BBVA Seguros", + "count": 1 + }, + { + "from": "Información derivada de los productos y servicios contratados", + "kind": "controlled_by", + "to": "BBVA Seguros", + "count": 1 + }, + { + "from": "número de teléfono de contacto", + "kind": "contact_for", + "to": "Banco de España (CIRBE)", + "count": 1 + }, + { + "from": "BBVA Seguros", + "kind": "subsidiary_of", + "to": "Banco de España (CIRBE)", + "count": 1 + }, + { + "from": "Datos sociodemográficos", + "kind": "protected_by", + "to": "BBVA Seguros", + "count": 1 + }, + { + "from": "direcciones postales", + "kind": "contact_for", + "to": "representante", + "count": 1 + }, + { + "from": "BBVA Seguros", + "kind": "governed_by", + "to": "Banco de España (CIRBE)", + "count": 4 + }, + { + "from": "BBVA Seguros", + "kind": "controlled_by", + "to": "Banco de España (CIRBE)", + "count": 2 + }, + { + "from": "Datos de firma", + "kind": "subject_to", + "to": "consentimiento", + "count": 1 + }, + { + "from": "Datos de firma", + "kind": "protected_by", + "to": "BBVA Seguros", + "count": 1 + }, + { + "from": "domicilio", + "kind": "located_in", + "to": "Plaza San Nicolás", + "count": 2 + }, + { + "from": "Bilbao Vizcaya Argentaria, S.A.", + "kind": "subsidiary_of", + "to": "BBVA Seguros", + "count": 1 + }, + { + "from": "situaciones de riesgo", + "kind": "controlled_by", + "to": "BBVA Seguros", + "count": 1 + }, + { + "from": "situaciones de riesgo", + "kind": "governed_by", + "to": "BBVA Seguros", + "count": 1 + }, + { + "from": "Datos Personales", + "kind": "protected_by", + "to": "base de legitimación", + "count": 1 + }, + { + "from": "operación de financiación", + "kind": "controlled_by", + "to": "BBVA Seguros", + "count": 1 + }, + { + "from": "Datos Personales", + "kind": "subject_to", + "to": "contrato", + "count": 1 + }, + { + "from": "datos de contacto", + "kind": "contact_for", + "to": "clientes", + "count": 2 + }, + { + "from": "BBVA Seguros", + "kind": "rights_against", + "to": "datos de contacto", + "count": 1 + }, + { + "from": "BBVA Seguros, S.A.", + "kind": "subsidiary_of", + "to": "BBVA Seguros", + "count": 1 + }, + { + "from": "BBVA Seguros", + "kind": "located_in", + "to": "España", + "count": 2 + }, + { + "from": "BBVA Seguros", + "kind": "subject_to", + "to": "obligaciones legales", + "count": 3 + }, + { + "from": "datos de contacto", + "kind": "protected_by", + "to": "de Seguros y Reaseguros", + "count": 1 + }, + { + "from": "BBVA Seguros", + "kind": "contact_for", + "to": "operaciones", + "count": 1 + }, + { + "from": "BBVA Seguros", + "kind": "located_in", + "to": "países", + "count": 1 + }, + { + "from": "Sepblac", + "kind": "contact_for", + "to": "operaciones", + "count": 1 + }, + { + "from": "entidades financieras", + "kind": "controlled_by", + "to": "Banco Central Europeo", + "count": 1 + }, + { + "from": "tratamiento de datos", + "kind": "subject_to", + "to": "normativa interna sobre prevención de crimen financiero", + "count": 1 + }, + { + "from": "enlace", + "kind": "contact_for", + "to": "decisión", + "count": 1 + }, + { + "from": "operador", + "kind": "rights_against", + "to": "decisión", + "count": 1 + }, + { + "from": "Comisión Nacional del Mercado de Valores", + "kind": "governed_by", + "to": "Ley 6/2023", + "count": 1 + }, + { + "from": "perfil de riesgo de inversión", + "kind": "subject_to", + "to": "Ley 6/2023", + "count": 1 + }, + { + "from": "perfil de riesgo de inversión", + "kind": "protected_by", + "to": "Ley 6/2023", + "count": 1 + }, + { + "from": "deuda", + "kind": "subject_to", + "to": "criterios de crédito responsable", + "count": 1 + }, + { + "from": "dirección de email", + "kind": "located_in", + "to": "oficinas", + "count": 1 + }, + { + "from": "contratos de crédito inmobiliario", + "kind": "governed_by", + "to": "Ley 5/2019", + "count": 2 + }, + { + "from": "deuda", + "kind": "subject_to", + "to": "Ley 5/2019", + "count": 1 + }, + { + "from": "Datos Personales", + "kind": "protected_by", + "to": "derechosprotecciondatos@bbva.com", + "count": 1 + }, + { + "from": "derecho de oposición", + "kind": "rights_against", + "to": "dirección de email", + "count": 1 + }, + { + "from": "Avda. de la Industria", + "kind": "located_in", + "to": "MADRID", + "count": 2 + }, + { + "from": "BBVA Seguros", + "kind": "governed_by", + "to": "Reglamento del Fichero Confirma", + "count": 1 + }, + { + "from": "derecho de oposición", + "kind": "protected_by", + "to": "BBVA Seguros", + "count": 1 + }, + { + "from": "dirección de email", + "kind": "contact_for", + "to": "derechosprotecciondatos@bbva.com", + "count": 1 + }, + { + "from": "derecho de oposición", + "kind": "rights_against", + "to": "BBVA Seguros", + "count": 1 + }, + { + "from": "entidades financieras", + "kind": "governed_by", + "to": "Reglamento del Fichero Confirma", + "count": 1 + }, + { + "from": "entidades financieras", + "kind": "subject_to", + "to": "Reglamento del Fichero Confirma", + "count": 1 + }, + { + "from": "dpogrupobbva@bbva.com", + "kind": "contact_for", + "to": "acuerdo de corresponsabilidad", + "count": 1 + }, + { + "from": "entidades financieras", + "kind": "controlled_by", + "to": "Reglamento del Fichero Confirma", + "count": 1 + }, + { + "from": "tratamiento de datos", + "kind": "subject_to", + "to": "normativa vigente en materia de protección de datos", + "count": 1 + }, + { + "from": "Datos Personales", + "kind": "protected_by", + "to": "normativa vigente en materia de protección de datos", + "count": 1 + }, + { + "from": "Reglamento del Fichero Confirma", + "kind": "subject_to", + "to": "privacidad", + "count": 1 + }, + { + "from": "BBVA Seguros", + "kind": "rights_against", + "to": "Datos Personales", + "count": 1 + }, + { + "from": "fichero común", + "kind": "controlled_by", + "to": "BBVA Seguros", + "count": 1 + }, + { + "from": "fichero común", + "kind": "governed_by", + "to": "Iberpay", + "count": 1 + }, + { + "from": "bbva.es", + "kind": "located_in", + "to": "MADRID", + "count": 2 + }, + { + "from": "datos derivados de la interacción con chatbots", + "kind": "subject_to", + "to": "normativa interna sobre prevención de crimen financiero", + "count": 2 + }, + { + "from": "datos derivados de la interacción con chatbots", + "kind": "protected_by", + "to": "Agencia Española de Protección de Datos", + "count": 1 + }, + { + "from": "derechos de acceso", + "kind": "rights_against", + "to": "datos derivados de la interacción con chatbots", + "count": 3 + }, + { + "from": "datos derivados de la interacción con chatbots", + "kind": "controlled_by", + "to": "BBVA Seguros", + "count": 1 + }, + { + "from": "bancos", + "kind": "located_in", + "to": "ámbito UE", + "count": 1 + }, + { + "from": "Datos Personales", + "kind": "subject_to", + "to": "normativa interna sobre prevención de crimen financiero", + "count": 2 + }, + { + "from": "Datos Personales", + "kind": "protected_by", + "to": "Agencia Española de Protección de Datos", + "count": 4 + }, + { + "from": "movimientos de cuentas", + "kind": "protected_by", + "to": "BBVA Seguros", + "count": 1 + }, + { + "from": "BBVA Seguros", + "kind": "rights_against", + "to": "movimientos de cuentas", + "count": 1 + }, + { + "from": "Banco de España (CIRBE)", + "kind": "rights_against", + "to": "datos derivados de la interacción con chatbots", + "count": 1 + }, + { + "from": "datos derivados de la interacción con chatbots", + "kind": "controlled_by", + "to": "Banco de España (CIRBE)", + "count": 1 + }, + { + "from": "Emailage Corporation", + "kind": "located_in", + "to": "Londres", + "count": 2 + }, + { + "from": "Telesign Corporation", + "kind": "located_in", + "to": "Estados Unidos", + "count": 2 + }, + { + "from": "Emailage Corporation", + "kind": "governed_by", + "to": "normativa interna sobre prevención de crimen financiero", + "count": 1 + }, + { + "from": "Agencia Española de Protección de Datos", + "kind": "rights_against", + "to": "Emailage Corporation", + "count": 1 + }, + { + "from": "Emailage Corporation", + "kind": "subsidiary_of", + "to": "BBVA Seguros", + "count": 1 + }, + { + "from": "Emailage Corporation", + "kind": "controlled_by", + "to": "BBVA Seguros", + "count": 2 + }, + { + "from": "Emailage Corporation", + "kind": "governed_by", + "to": "BBVA Seguros", + "count": 1 + }, + { + "from": "BBVA Seguros", + "kind": "subsidiary_of", + "to": "fichero FrauDfense", + "count": 2 + }, + { + "from": "BBVA Seguros", + "kind": "controlled_by", + "to": "fichero FrauDfense", + "count": 1 + }, + { + "from": "fichero FrauDfense", + "kind": "governed_by", + "to": "FrauDfense, S.L.", + "count": 1 + }, + { + "from": "fichero FrauDfense", + "kind": "controlled_by", + "to": "FrauDfense, S.L.", + "count": 1 + }, + { + "from": "entidades adheridas", + "kind": "governed_by", + "to": "FrauDfense, S.L.", + "count": 1 + }, + { + "from": "Fraude de admisión", + "kind": "protected_by", + "to": "Registro Mercantil de Vizcaya", + "count": 1 + }, + { + "from": "FrauDfense, S.L.", + "kind": "contact_for", + "to": "entidades adheridas", + "count": 1 + }, + { + "from": "Fraude de admisión", + "kind": "governed_by", + "to": "fichero FrauDfense", + "count": 1 + }, + { + "from": "A-48265169BBVA", + "kind": "contact_for", + "to": "fraudes", + "count": 1 + }, + { + "from": "Fraude de admisión", + "kind": "controlled_by", + "to": "entidades adheridas", + "count": 1 + }, + { + "from": "fraude de admisión", + "kind": "controlled_by", + "to": "entidades adheridas", + "count": 1 + }, + { + "from": "teléfono", + "kind": "contact_for", + "to": "BBVA Seguros", + "count": 1 + }, + { + "from": "datos derivados de la interacción con chatbots", + "kind": "protected_by", + "to": "derecho de oposición", + "count": 1 + }, + { + "from": "derecho de oposición", + "kind": "rights_against", + "to": "Datos Personales", + "count": 1 + }, + { + "from": "BBVA Seguros", + "kind": "governed_by", + "to": "autoridades competentes", + "count": 1 + }, + { + "from": "datos derivados de la interacción con chatbots", + "kind": "protected_by", + "to": "BBVA Seguros", + "count": 1 + }, + { + "from": "reclamación", + "kind": "rights_against", + "to": "Agencia Española de Protección de Datos", + "count": 1 + }, + { + "from": "Madiva Soluciones", + "kind": "subsidiary_of", + "to": "BBVA Seguros", + "count": 1 + }, + { + "from": "Bilbao Vizcaya Argentaria, S.A.", + "kind": "located_in", + "to": "Plaza San Nicolás", + "count": 1 + }, + { + "from": "BBVA Seguros", + "kind": "contact_for", + "to": "ahorro energético", + "count": 1 + }, + { + "from": "vehículo eléctrico", + "kind": "subsidiary_of", + "to": "BBVA Seguros", + "count": 1 + }, + { + "from": "Datos Personales", + "kind": "subject_to", + "to": "políticas de privacidad", + "count": 1 + }, + { + "from": "Datos Personales", + "kind": "protected_by", + "to": "políticas de privacidad", + "count": 1 + }, + { + "from": "BBVA Seguros", + "kind": "rights_against", + "to": "clientes", + "count": 1 + }, + { + "from": "BBVA Seguros", + "kind": "subsidiary_of", + "to": "MyCar", + "count": 1 + }, + { + "from": "Información de perfiles", + "kind": "located_in", + "to": "apartados a)", + "count": 1 + }, + { + "from": "protección de datos personales", + "kind": "protected_by", + "to": "derecho fundamental", + "count": 1 + }, + { + "from": "derecho de oposición", + "kind": "rights_against", + "to": "clientes", + "count": 1 + }, + { + "from": "Datos Personales", + "kind": "subject_to", + "to": "consentimiento", + "count": 2 + }, + { + "from": "dirección de email", + "kind": "contact_for", + "to": "perfil comercial", + "count": 1 + }, + { + "from": "Datos Personales", + "kind": "rights_against", + "to": "cliente particular", + "count": 1 + }, + { + "from": "contratación", + "kind": "subject_to", + "to": "aceptación de la oferta", + "count": 1 + }, + { + "from": "Información de perfiles", + "kind": "subject_to", + "to": "consentimiento", + "count": 1 + }, + { + "from": "Información de perfiles", + "kind": "protected_by", + "to": "consentimiento", + "count": 1 + }, + { + "from": "Información de perfiles", + "kind": "controlled_by", + "to": "BBVA Seguros", + "count": 1 + }, + { + "from": "Información básica de protección de datos", + "kind": "located_in", + "to": "apartado (ii)", + "count": 1 + }, + { + "from": "datos derivados de la interacción con chatbots", + "kind": "protected_by", + "to": "Información básica de protección de datos", + "count": 2 + }, + { + "from": "comunicaciones comerciales", + "kind": "subject_to", + "to": "consentimiento", + "count": 1 + }, + { + "from": "dirección de email", + "kind": "contact_for", + "to": "derecho de oposición", + "count": 1 + }, + { + "from": "BBVA Seguros", + "kind": "contact_for", + "to": "clientes", + "count": 1 + }, + { + "from": "datos seudonimizados", + "kind": "protected_by", + "to": "identidad del titular", + "count": 1 + }, + { + "from": "código", + "kind": "contact_for", + "to": "cliente particular", + "count": 2 + }, + { + "from": "Datos identificativos", + "kind": "located_in", + "to": "categorías de datos", + "count": 1 + }, + { + "from": "Datos Personales", + "kind": "subject_to", + "to": "procesos de anonimización", + "count": 1 + }, + { + "from": "Datos Personales", + "kind": "protected_by", + "to": "derecho fundamental", + "count": 1 + }, + { + "from": "BBVA Seguros", + "kind": "controlled_by", + "to": "Grupo BBVA", + "count": 1 + }, + { + "from": "productos de consumo", + "kind": "governed_by", + "to": "A-48265169inmuebles", + "count": 1 + }, + { + "from": "Datos Personales", + "kind": "protected_by", + "to": "Información básica de protección de datos", + "count": 1 + }, + { + "from": "datos derivados de la interacción con chatbots", + "kind": "protected_by", + "to": "Grupo BBVA", + "count": 1 + }, + { + "from": "contratación", + "kind": "controlled_by", + "to": "BBVA Seguros", + "count": 3 + }, + { + "from": "BBVA Seguros", + "kind": "subject_to", + "to": "Ley 5/2019", + "count": 1 + }, + { + "from": "apartado “A) Si eres Cliente”", + "kind": "located_in", + "to": "epígrafe 4", + "count": 1 + }, + { + "from": "contratación", + "kind": "governed_by", + "to": "BBVA Seguros", + "count": 2 + }, + { + "from": "BBVA Seguros", + "kind": "governed_by", + "to": "artículo 19", + "count": 1 + }, + { + "from": "Datos Personales", + "kind": "protected_by", + "to": "artículo 19", + "count": 1 + }, + { + "from": "apartado “A) Si eres Cliente”", + "kind": "located_in", + "to": "epígrafe 2", + "count": 1 + }, + { + "from": "contratación", + "kind": "subject_to", + "to": "obligaciones legales", + "count": 1 + }, + { + "from": "Banco Central Europeo", + "kind": "located_in", + "to": "España", + "count": 1 + }, + { + "from": "entidades financieras", + "kind": "governed_by", + "to": "regulación", + "count": 1 + }, + { + "from": "entidades financieras", + "kind": "subject_to", + "to": "regulación", + "count": 1 + }, + { + "from": "reclamaciones", + "kind": "rights_against", + "to": "Datos Personales", + "count": 1 + }, + { + "from": "entidades financieras", + "kind": "controlled_by", + "to": "regulación", + "count": 1 + }, + { + "from": "cliente particular", + "kind": "rights_against", + "to": "Datos Personales", + "count": 1 + }, + { + "from": "sociedades del Grupo BBVA", + "kind": "subject_to", + "to": "normativa de prevención de blanqueo de capitales", + "count": 1 + }, + { + "from": "datos derivados de la interacción con chatbots", + "kind": "located_in", + "to": "Estados Unidos", + "count": 1 + }, + { + "from": "sistemas de información crediticia", + "kind": "governed_by", + "to": "normativa interna sobre prevención de crimen financiero", + "count": 1 + }, + { + "from": "reclamaciones", + "kind": "subject_to", + "to": "normativa interna sobre prevención de crimen financiero", + "count": 1 + }, + { + "from": "Iberpay", + "kind": "subsidiary_of", + "to": "entidades financieras", + "count": 1 + }, + { + "from": "sistemas de información crediticia", + "kind": "controlled_by", + "to": "entidades financieras", + "count": 1 + }, + { + "from": "reclamaciones", + "kind": "subject_to", + "to": "obligación legal", + "count": 1 + }, + { + "from": "BBVA Seguros", + "kind": "governed_by", + "to": "Política de Protección de Datos Personales", + "count": 1 + }, + { + "from": "Datos Personales", + "kind": "subject_to", + "to": "Política de Protección de Datos Personales", + "count": 1 + }, + { + "from": "BBVA Seguros", + "kind": "contact_for", + "to": "potenciales adquirentes", + "count": 1 + }, + { + "from": "países", + "kind": "protected_by", + "to": "Unión Europea", + "count": 1 + }, + { + "from": "Prestadores de servicios", + "kind": "contact_for", + "to": "cliente particular", + "count": 1 + }, + { + "from": "sociedades participadas", + "kind": "rights_against", + "to": "Datos Personales", + "count": 1 + }, + { + "from": "BBVA Seguros", + "kind": "governed_by", + "to": "Unión Europea", + "count": 1 + }, + { + "from": "Datos Personales", + "kind": "protected_by", + "to": "Unión Europea", + "count": 1 + }, + { + "from": "rectificación", + "kind": "rights_against", + "to": "Datos Personales", + "count": 1 + }, + { + "from": "Datos Personales", + "kind": "controlled_by", + "to": "BBVA Seguros", + "count": 1 + }, + { + "from": "consentimiento", + "kind": "subject_to", + "to": "licitud del tratamiento", + "count": 1 + } + ] +} \ No newline at end of file diff --git a/main.py b/main.py new file mode 100644 index 0000000..bffa277 --- /dev/null +++ b/main.py @@ -0,0 +1,6 @@ +def main(): + print("Hello from gliner-glirel-tuning!") + + +if __name__ == "__main__": + main() diff --git a/mrebel_results.json b/mrebel_results.json new file mode 100644 index 0000000..4cca343 --- /dev/null +++ b/mrebel_results.json @@ -0,0 +1,13 @@ +{ + "text": "Pablo Isla, expresidente de Inditex, ha sido nombrado consejero de Telefonica. La operacion fue anunciada por el presidente Jose Maria Alvarez-Pallete en Madrid el pasado lunes. Inditex factura mas de 30.000 millones anuales y tiene su sede en Arteixo, A Coruna. En paralelo, Iberdrola y Endesa firmaron un acuerdo de colaboracion en proyectos eolicos en Galicia. El presidente de Iberdrola, Ignacio Galan, se reunio con la CEO de Endesa, Marina Serrano, en Bilbao. El acuerdo movilizara 2.000 millones de euros en cinco anos. El BBVA, presidido por Carlos Torres, mostro interes en participar en la financiacion del proyecto. Su sede central esta en Bilbao.", + "raw_decoded": "tp_XX Arteixo A Coruna located in the administrative territorial entity", + "triplets": [ + { + "head": "Arteixo", + "head_type": "loc", + "type": "located in the administrative territorial entity", + "tail": "A Coruna", + "tail_type": "loc" + } + ] +} \ No newline at end of file diff --git a/notebooks/01_gliner_glirel_tuning.ipynb b/notebooks/01_gliner_glirel_tuning.ipynb new file mode 100644 index 0000000..8bae543 --- /dev/null +++ b/notebooks/01_gliner_glirel_tuning.ipynb @@ -0,0 +1,865 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6a7ef2a5", + "metadata": {}, + "source": [ + "# GLiNER + GLiREL — calibracion empirica\n", + "\n", + "**Objetivo:** entender empiricamente como funcionan **GLiNER** (entidades) y **GLiREL** (relaciones) para fijar thresholds operativos en el pipeline `extract_graph_hybrid` (panel _Paste & Extract_ de `graph_explorer`).\n", + "\n", + "**Hallazgo previo (sesion del merge 0013):** un solo `confidence_threshold=0.6` filtra GLiNER (0.92-0.99 facil) Y GLiREL (max 0.21 en el test). Resultado: el panel jamas muestra relaciones aunque GLiREL si las detecte. Este notebook valida la separacion necesaria de thresholds y mide rangos sanos.\n", + "\n", + "**Plan:**\n", + "1. Cargar modelos\n", + "2. **GLiNER** — barrido threshold sobre corpus EN/ES + sensibilidad a label sets\n", + "3. **GLiREL** — distribucion de scores sin filtro + sensibilidad a label phrasing\n", + "4. Recomendaciones operativas\n", + "\n", + "**Stack:** gliner==0.2.26, glirel==1.2.1, transformers==5.1, huggingface_hub==1.13. Modelos `urchade/gliner_multi-v2.1` (~600 MB) y `jackboyla/glirel-large-v0` (~1.5 GB), ambos cacheados en `~/.cache/huggingface/`." + ] + }, + { + "cell_type": "markdown", + "id": "2423c283", + "metadata": {}, + "source": [ + "## 1. Setup\n", + "\n", + "El kernel autocarga `FN_REGISTRY_ROOT` y anade `python/functions/` al `sys.path` (ver `.ipython/profile_default/startup/00_fn_registry.py`)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "67f48818", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T12:58:37.640753Z", + "iopub.status.busy": "2026-05-04T12:58:37.640602Z", + "iopub.status.idle": "2026-05-04T12:58:37.853224Z", + "shell.execute_reply": "2026-05-04T12:58:37.852377Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FN_REGISTRY_ROOT: /home/lucas/fn_registry\n", + "results.json keys: ['gliner_threshold_sweep', 'glirel_score_distribution', 'glirel_topk_sweep', 'corpus', 'entity_labels', 'relation_labels']\n" + ] + } + ], + "source": [ + "import os, sys, json, time, warnings\n", + "warnings.filterwarnings('ignore')\n", + "os.environ.setdefault('HF_HUB_DISABLE_PROGRESS_BARS', '1')\n", + "from pathlib import Path\n", + "\n", + "# Limpiar sys.path: el startup del kernel anade cada subdir de\n", + "# python/functions/ al top-level, y bigquery/datasets.py sombrea\n", + "# al paquete `datasets` de HuggingFace que necesita transformers.\n", + "# Dejamos solo el directorio padre 'python/functions/' para imports\n", + "# 'from datascience.gliner_load_model import ...' del estilo paquete.\n", + "_pf = '/home/lucas/fn_registry/python/functions'\n", + "sys.path = [p for p in sys.path if not (p.startswith(_pf + '/'))]\n", + "if _pf not in sys.path:\n", + " sys.path.insert(0, _pf)\n", + "\n", + "import pandas as pd\n", + "from datascience.gliner_load_model import gliner_load_model\n", + "from datascience.glirel_load_model import glirel_load_model\n", + "\n", + "RESULTS = json.loads(Path('../results.json').read_text())\n", + "print('FN_REGISTRY_ROOT:', os.environ.get('FN_REGISTRY_ROOT'))\n", + "print('results.json keys:', list(RESULTS.keys()))" + ] + }, + { + "cell_type": "markdown", + "id": "6dc6a22b", + "metadata": {}, + "source": [ + "## 2. Corpus de prueba\n", + "\n", + "4 textos cortos cubriendo dominios diferentes (ES/EN, corporativo/OSINT/journalism). Sirven para detectar drift de calidad por idioma y por tipo de contenido." + ] + }, + { + "cell_type": "markdown", + "id": "0f208d97", + "metadata": {}, + "source": [ + "### `es_corporate`\n", + "```\n", + "Pablo Isla, expresidente de Inditex, ha sido nombrado consejero de Telefonica. La operacion fue anunciada por el presidente Jose Maria Alvarez-Pallete en Madrid el pasado lunes. Inditex factura mas de 30.000 millones anuales y tiene su sede en Arteixo, A Coruna.\n", + "```\n", + "\n", + "### `en_corporate`\n", + "```\n", + "Pablo Isla, the former chairman of Inditex, has been appointed as a director of Telefonica. The announcement was made by Jose Maria Alvarez-Pallete, the chairman of Telefonica, in Madrid last Monday. Inditex has its headquarters in Arteixo, A Coruna.\n", + "```\n", + "\n", + "### `en_osint`\n", + "```\n", + "On 2024-08-15, attacker IP 185.220.101.45 connected to victim host 10.0.5.22 over TLS. Reverse DNS pointed to tor-exit-relay-3.onionrouter.net. Operator handle @phantomzero claimed responsibility on a forum. The C2 panel was hosted on hxxps://malwareops[.]biz/control behind Cloudflare.\n", + "```\n", + "\n", + "### `es_journalism`\n", + "```\n", + "Iberdrola y Endesa firmaron un acuerdo de colaboracion en proyectos eolicos en Galicia. El presidente de Iberdrola, Ignacio Galan, se reunio con la CEO de Endesa, Marina Serrano, en Bilbao. El acuerdo movilizara 2.000 millones de euros en cinco anos.\n", + "```\n" + ] + }, + { + "cell_type": "markdown", + "id": "8cbf0f22", + "metadata": {}, + "source": [ + "## 3. Carga de modelos\n", + "\n", + "Cold load: ~50s por modelo (descarga). Warm: ~8s. Cache global por (model_name, device)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "cf04dfad", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T12:58:37.855378Z", + "iopub.status.busy": "2026-05-04T12:58:37.855198Z", + "iopub.status.idle": "2026-05-04T12:58:52.254428Z", + "shell.execute_reply": "2026-05-04T12:58:52.253490Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[0;93m2026-05-04 14:58:38.910665577 [W:onnxruntime:Default, device_discovery.cc:283 GetGpuDevices] Failed to detect devices under \"/sys/class/drm/card0\": device_discovery.cc:93 ReadFileContents Failed to open file: \"/sys/class/drm/card0/device/vendor\"\u001b[m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[1mDebertaV2Model LOAD REPORT\u001b[0m from: microsoft/deberta-v3-large\n", + "Key | Status | | \n", + "----------------------------------------+------------+--+-\n", + "mask_predictions.LayerNorm.bias | UNEXPECTED | | \n", + "lm_predictions.lm_head.bias | UNEXPECTED | | \n", + "lm_predictions.lm_head.LayerNorm.weight | UNEXPECTED | | \n", + "lm_predictions.lm_head.dense.weight | UNEXPECTED | | \n", + "lm_predictions.lm_head.dense.bias | UNEXPECTED | | \n", + "mask_predictions.classifier.bias | UNEXPECTED | | \n", + "mask_predictions.dense.weight | UNEXPECTED | | \n", + "mask_predictions.LayerNorm.weight | UNEXPECTED | | \n", + "mask_predictions.dense.bias | UNEXPECTED | | \n", + "mask_predictions.classifier.weight | UNEXPECTED | | \n", + "lm_predictions.lm_head.LayerNorm.bias | UNEXPECTED | | \n", + "\n", + "\u001b[3mNotes:\n", + "- UNEXPECTED\u001b[3m\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GLiNER ready in 8.2s\n", + "GLiREL ready in 6.2s\n" + ] + } + ], + "source": [ + "t0 = time.time(); gliner = gliner_load_model(); t_gliner = time.time()-t0\n", + "t0 = time.time(); glirel = glirel_load_model(); t_glirel = time.time()-t0\n", + "print(f'GLiNER ready in {t_gliner:.1f}s')\n", + "print(f'GLiREL ready in {t_glirel:.1f}s')" + ] + }, + { + "cell_type": "markdown", + "id": "08107c78", + "metadata": {}, + "source": [ + "## 4. GLiNER — barrido de threshold\n", + "\n", + "Para cada (corpus, label_set) corremos `predict_entities(threshold=0.0)` y filtramos a posteriori a {0.1, 0.3, 0.5, 0.7, 0.9}. Asi vemos la distribucion completa de scores sin recargar modelo." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "46598320", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T12:58:52.257688Z", + "iopub.status.busy": "2026-05-04T12:58:52.257083Z", + "iopub.status.idle": "2026-05-04T12:58:52.284240Z", + "shell.execute_reply": "2026-05-04T12:58:52.283211Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
corpuslabelst=.1t=.3t=.5t=.7t=.9max_score
0es_corporategeneric_en888880.994
1es_corporategeneric_es888880.990
2en_corporategeneric_en999990.995
3en_corporatespecific_en999980.991
4en_osintgeneric_en1261000.604
5en_osintosint_en1386220.953
6es_journalismgeneric_en988880.995
7es_journalismgeneric_es988870.992
\n", + "
" + ], + "text/plain": [ + " corpus labels t=.1 t=.3 t=.5 t=.7 t=.9 max_score\n", + "0 es_corporate generic_en 8 8 8 8 8 0.994\n", + "1 es_corporate generic_es 8 8 8 8 8 0.990\n", + "2 en_corporate generic_en 9 9 9 9 9 0.995\n", + "3 en_corporate specific_en 9 9 9 9 8 0.991\n", + "4 en_osint generic_en 12 6 1 0 0 0.604\n", + "5 en_osint osint_en 13 8 6 2 2 0.953\n", + "6 es_journalism generic_en 9 8 8 8 8 0.995\n", + "7 es_journalism generic_es 9 8 8 8 7 0.992" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from datascience.gliner_load_model import gliner_load_model\n", + "thresholds = [0.1, 0.3, 0.5, 0.7, 0.9]\n", + "rows = []\n", + "for corpus_key, cdata in RESULTS['gliner_threshold_sweep'].items():\n", + " for ls_key, sdata in cdata.items():\n", + " scored = sdata['scored_at_t0']\n", + " max_s = max((s[2] for s in scored), default=0.0)\n", + " rows.append([corpus_key, ls_key, *[len(sdata[f't={t}']) for t in thresholds], round(max_s,3)])\n", + "df = pd.DataFrame(rows, columns=['corpus','labels','t=.1','t=.3','t=.5','t=.7','t=.9','max_score'])\n", + "df" + ] + }, + { + "cell_type": "markdown", + "id": "eed12fb4", + "metadata": {}, + "source": [ + "**Lectura:**\n", + "\n", + "- En **narrativa estructurada** (corporate, journalism), GLiNER da 8-9 entidades estables con scores 0.92-0.99. **`threshold=0.5` o `0.7` son seguros**, casi no se mueve el conteo.\n", + "- En **OSINT** (IPs, dominios, URLs) con labels genericas (`person`, `organization`...): scores _se hunden_ a max 0.60. **Cae todo a t=0.5**.\n", + "- Mismo OSINT con labels especificas (`ip_address`, `domain`, `url`): max 0.95, threshold 0.5 retiene 6.\n", + "- ES vs EN: practicamente identicos. El `gliner_multi-v2.1` es genuinamente multilingue. **Las labels EN funcionan igual de bien sobre texto ES.**\n", + "\n", + "**Conclusion 1:** `entity_threshold = 0.5` es seguro como default. Pero el **label set debe encajar al dominio** — una mala eleccion mata mas que un threshold mal puesto." + ] + }, + { + "cell_type": "markdown", + "id": "fed8f100", + "metadata": {}, + "source": [ + "### 4.1 Entidades concretas (en_corporate, generic_en, t=0.5)\n", + "\n", + "Para verificar que no son ruido." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5358e303", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T12:58:52.286116Z", + "iopub.status.busy": "2026-05-04T12:58:52.285916Z", + "iopub.status.idle": "2026-05-04T12:58:52.300382Z", + "shell.execute_reply": "2026-05-04T12:58:52.299264Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
textlabelscore
0Pablo Islaperson0.989302
1Inditexorganization0.992379
2Telefonicaorganization0.992698
3Jose Maria Alvarez-Palleteperson0.975533
4Telefonicaorganization0.990853
5Madridlocation0.966069
6Inditexorganization0.994649
7Arteixolocation0.968921
8A Corunalocation0.920429
\n", + "
" + ], + "text/plain": [ + " text label score\n", + "0 Pablo Isla person 0.989302\n", + "1 Inditex organization 0.992379\n", + "2 Telefonica organization 0.992698\n", + "3 Jose Maria Alvarez-Pallete person 0.975533\n", + "4 Telefonica organization 0.990853\n", + "5 Madrid location 0.966069\n", + "6 Inditex organization 0.994649\n", + "7 Arteixo location 0.968921\n", + "8 A Coruna location 0.920429" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ents = RESULTS['gliner_threshold_sweep']['en_corporate']['generic_en']['t=0.5']\n", + "pd.DataFrame(ents, columns=['text','label','score','start','end'])[['text','label','score']]" + ] + }, + { + "cell_type": "markdown", + "id": "f4019283", + "metadata": {}, + "source": [ + "## 5. GLiREL — distribucion de scores\n", + "\n", + "Aqui esta el quid del bug: pasamos `threshold=0.0`, `top_k=5` y vemos los scores naturales que emite GLiREL. Comparamos dos estilos de label:\n", + "\n", + "- `snake_short`: `works_at`, `located_in`, `appointed_as`, ...\n", + "- `natural_long`: `person works at organization`, ...\n", + "\n", + "El folklore dice que el segundo deberia funcionar mejor (porque GLiREL es tipo zero-shot). Vamos a ver." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b0516987", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T12:58:52.302264Z", + "iopub.status.busy": "2026-05-04T12:58:52.302062Z", + "iopub.status.idle": "2026-05-04T12:58:52.313997Z", + "shell.execute_reply": "2026-05-04T12:58:52.312964Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
corpusn_entslabel_stylen_relsmax_scoremedian_score
0es_corporate8snake_short2800.1690.017
1es_corporate8natural_long2800.0610.010
2en_corporate9snake_short3600.2330.016
3en_corporate9natural_long3600.0800.007
4es_journalism8snake_short2800.1950.011
5es_journalism8natural_long2800.1380.007
\n", + "
" + ], + "text/plain": [ + " corpus n_ents label_style n_rels max_score median_score\n", + "0 es_corporate 8 snake_short 280 0.169 0.017\n", + "1 es_corporate 8 natural_long 280 0.061 0.010\n", + "2 en_corporate 9 snake_short 360 0.233 0.016\n", + "3 en_corporate 9 natural_long 360 0.080 0.007\n", + "4 es_journalism 8 snake_short 280 0.195 0.011\n", + "5 es_journalism 8 natural_long 280 0.138 0.007" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rows=[]\n", + "for corpus, cdata in RESULTS['glirel_score_distribution'].items():\n", + " n_ents = len(cdata.get('entities', []))\n", + " for style, rels in cdata.get('styles', {}).items():\n", + " if isinstance(rels, list) and rels:\n", + " scores = sorted([r['score'] for r in rels], reverse=True)\n", + " rows.append([corpus, n_ents, style, len(rels), round(scores[0],3), round(scores[len(scores)//2],3)])\n", + " else:\n", + " rows.append([corpus, n_ents, style, 0, 0.0, 0.0])\n", + "df = pd.DataFrame(rows, columns=['corpus','n_ents','label_style','n_rels','max_score','median_score'])\n", + "df" + ] + }, + { + "cell_type": "markdown", + "id": "80cb8f95", + "metadata": {}, + "source": [ + "**Lectura — dos sorpresas:**\n", + "\n", + "1. **`snake_short` >> `natural_long`** por un factor 3-4×. Pasar `\"person works at organization\"` baja el score max de 0.23 a 0.08. **GLiREL fue entrenado con etiquetas estilo Wikipedia** (`P54`, `member_of_political_party`...), no con frases naturales. El prompt-engineering aqui es _menos_ es _mas_.\n", + "2. **EN > ES por ~25%**: `en_corporate` max 0.233 vs `es_corporate` max 0.169 con el mismo contenido factico. GLiREL tiene mejor cobertura del ingles.\n", + "3. **Texto OSINT** dio 0 entidades en GLiNER multi-v2.1 con labels genericas → no hay pares para GLiREL. (Para OSINT habria que cambiar GLiNER -> regex (que ya cubre IoCs) y dejar GLiREL para narrativa).\n", + "\n", + "**Conclusion 2:** **`relation_threshold` debe estar en 0.10-0.15**, NO en 0.6. El `confidence_threshold` global del pipeline debe partirse en dos." + ] + }, + { + "cell_type": "markdown", + "id": "e535e84b", + "metadata": {}, + "source": [ + "### 5.1 Efecto de `top_k`\n", + "\n", + "Subir `top_k` ¿descubre relaciones nuevas o solo añade ruido?" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "cc6855a0", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T12:58:52.315945Z", + "iopub.status.busy": "2026-05-04T12:58:52.315750Z", + "iopub.status.idle": "2026-05-04T12:58:52.325915Z", + "shell.execute_reply": "2026-05-04T12:58:52.324821Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
top_kn_totalmaxmedianmin
0top_k=1720.2330.1290.036
1top_k=32160.2330.0450.003
2top_k=53600.2330.0160.000
3top_k=103600.2330.0160.000
\n", + "
" + ], + "text/plain": [ + " top_k n_total max median min\n", + "0 top_k=1 72 0.233 0.129 0.036\n", + "1 top_k=3 216 0.233 0.045 0.003\n", + "2 top_k=5 360 0.233 0.016 0.000\n", + "3 top_k=10 360 0.233 0.016 0.000" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rows=[]\n", + "for tk, rels in RESULTS['glirel_topk_sweep']['by_topk'].items():\n", + " s = sorted([r['score'] for r in rels], reverse=True)\n", + " rows.append([tk, len(rels), round(s[0],3), round(s[len(s)//2],3), round(s[-1],3)])\n", + "df = pd.DataFrame(rows, columns=['top_k','n_total','max','median','min'])\n", + "df" + ] + }, + { + "cell_type": "markdown", + "id": "52f63ef3", + "metadata": {}, + "source": [ + "**Lectura:** `max` no se mueve. Solo crece `n_total` con peor score. **`top_k=1` o `top_k=3` es suficiente** para la app — subirlo solo añade ruido por debajo del threshold.\n", + "\n", + "**Conclusion 3:** dejar `top_k=1` por defecto en el panel. Si el usuario quiere ver alternativas, abrir un control avanzado." + ] + }, + { + "cell_type": "markdown", + "id": "163a20d2", + "metadata": {}, + "source": [ + "## 6. Recomendaciones operativas\n", + "\n", + "### Para `extract_graph_hybrid` y `paste_extract`\n", + "\n", + "| Param | Valor recomendado | Razon |\n", + "|---|---|---|\n", + "| `entity_threshold` | **0.50** (general) / **0.70** (narrativa estructurada) | GLiNER da 0.92-0.99 en narrativa; 0.5 deja margen para casos limite |\n", + "| `relation_threshold` | **0.15** (EN) / **0.10** (ES) | GLiREL tiene scores naturalmente bajos; 0.6 es absurdo |\n", + "| `top_k` | **1** | Subirlo solo añade peor evidencia |\n", + "| `relation_labels` | **snake_case corto** (`works_at`) | Frases naturales empeoran scores 3-4× |\n", + "| `entity_labels` | **dominio-especificas si OSINT** | Labels genericas hunden recall en texto OSINT |\n", + "\n", + "### Cambios concretos en el codigo\n", + "\n", + "1. **Issue nuevo en `graph_explorer`** — `0041-split-confidence-thresholds.md`:\n", + " - En `python/functions/pipelines/extract_graph_hybrid.py`: separar `confidence_threshold` en `entity_threshold` y `relation_threshold`.\n", + " - En `enrichers/paste_extract/run.py`: aceptar ambos parametros desde el manifest/ctx.\n", + " - En el panel C++ (`extract_panel.cpp`): dos sliders en lugar de uno, defaults 0.50 y 0.15.\n", + "2. **Test pytest existente** (`tests/test_paste_extract.py`) ya monkeypatchea el pipeline; añadir un test del path real con threshold separado cuando los modelos esten disponibles (skip si no).\n", + "3. **Documentar en `app.md`** que el path hybrid descarga ~2 GB la primera vez y queda en `~/.cache/huggingface/`.\n", + "\n", + "### Decisiones que NO se confirman aqui\n", + "\n", + "- Que pasa con texto > 512 tokens (GLiNER tiene window). Ver `extract_graph_hybrid` que ya hace chunking.\n", + "- Calidad real con LLM fallback activo (no probado en este notebook).\n", + "- Comportamiento con corpus mucho mas grande (este analysis prueba 4 textos cortos)." + ] + }, + { + "cell_type": "markdown", + "id": "1546f0f8", + "metadata": {}, + "source": [ + "## 7. Apendice — script reproducible\n", + "\n", + "Los datos vienen de `../results.json`, generado por `../run_experiments.py`. Para regenerar (cambiar corpus, labels, etc.):\n", + "\n", + "```bash\n", + "cd analysis/gliner_glirel_tuning\n", + "./.venv/bin/python3 run_experiments.py # ~30s con modelos calientes\n", + "./.venv/bin/python3 build_notebook.py # rebuild .ipynb con outputs\n", + "```" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/02_e2e_spanish_graph.ipynb b/notebooks/02_e2e_spanish_graph.ipynb new file mode 100644 index 0000000..c5a547d --- /dev/null +++ b/notebooks/02_e2e_spanish_graph.ipynb @@ -0,0 +1,729 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "13f36fc3", + "metadata": {}, + "source": [ + "# E2E — texto castellano → grafo en el notebook\n", + "\n", + "Validacion end-to-end del flujo del panel _Paste & Extract_ usando los thresholds calibrados en `01_gliner_glirel_tuning.ipynb`:\n", + "\n", + "- `entity_threshold = 0.50`\n", + "- `relation_threshold = 0.15`\n", + "- `relation_labels` en snake_case corto\n", + "\n", + "Pegamos un texto en castellano sobre el sector empresarial espanol, corremos el pipeline `extract_graph_hybrid`, y dibujamos el grafo resultante con `networkx + matplotlib`." + ] + }, + { + "cell_type": "markdown", + "id": "1e9efed4", + "metadata": {}, + "source": [ + "## 1. Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "06a2640a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T13:08:17.689631Z", + "iopub.status.busy": "2026-05-04T13:08:17.689324Z", + "iopub.status.idle": "2026-05-04T13:08:18.191052Z", + "shell.execute_reply": "2026-05-04T13:08:18.190135Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "imports OK\n" + ] + } + ], + "source": [ + "import os, sys, json, warnings\n", + "warnings.filterwarnings('ignore')\n", + "os.environ.setdefault('HF_HUB_DISABLE_PROGRESS_BARS', '1')\n", + "from pathlib import Path\n", + "\n", + "# Limpiar sys.path: el startup del kernel anade cada subdir de\n", + "# python/functions/, y bigquery/datasets.py sombrea al paquete\n", + "# `datasets` de HuggingFace. Dejamos solo el padre para imports\n", + "# 'from datascience...' / 'from pipelines...' al estilo paquete.\n", + "_pf = '/home/lucas/fn_registry/python/functions'\n", + "sys.path = [p for p in sys.path if not p.startswith(_pf + '/')]\n", + "if _pf not in sys.path:\n", + " sys.path.insert(0, _pf)\n", + "\n", + "import pandas as pd\n", + "import networkx as nx\n", + "import matplotlib.pyplot as plt\n", + "from datascience.gliner_load_model import gliner_load_model\n", + "from datascience.glirel_load_model import glirel_load_model\n", + "from pipelines.extract_graph_hybrid import extract_graph_hybrid\n", + "print('imports OK')" + ] + }, + { + "cell_type": "markdown", + "id": "18a2d8e3", + "metadata": {}, + "source": [ + "## 2. Texto de entrada (castellano)\n", + "\n", + "Tres parrafos sobre el sector empresarial espanol — directivos, sedes, acuerdos — con entidades de tres tipos (Person, Organization, Location) y relaciones evidentes (presidencias, sedes, acuerdos)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "6bd111ba", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T13:08:18.193022Z", + "iopub.status.busy": "2026-05-04T13:08:18.192752Z", + "iopub.status.idle": "2026-05-04T13:08:18.195991Z", + "shell.execute_reply": "2026-05-04T13:08:18.195212Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pablo Isla, expresidente de Inditex, ha sido nombrado consejero de Telefonica. La operacion fue anunciada por el presidente Jose Maria Alvarez-Pallete en Madrid el pasado lunes. Inditex factura mas de 30.000 millones anuales y tiene su sede en Arteixo, A Coruna.\n", + "\n", + "En paralelo, Iberdrola y Endesa firmaron un acuerdo de colaboracion en proyectos eolicos en Galicia. El presidente de Iberdrola, Ignacio Galan, se reunio con la CEO de Endesa, Marina Serrano, en Bilbao. El acuerdo movilizara 2.000 millones de euros en cinco anos.\n", + "\n", + "El BBVA, presidido por Carlos Torres, mostro interes en participar en la financiacion del proyecto. Su sede central esta en Bilbao.\n", + "\n", + "longitud: 660 chars ~104 tokens\n" + ] + } + ], + "source": [ + "TEXTO = 'Pablo Isla, expresidente de Inditex, ha sido nombrado consejero de Telefonica. La operacion fue anunciada por el presidente Jose Maria Alvarez-Pallete en Madrid el pasado lunes. Inditex factura mas de 30.000 millones anuales y tiene su sede en Arteixo, A Coruna.\\n\\nEn paralelo, Iberdrola y Endesa firmaron un acuerdo de colaboracion en proyectos eolicos en Galicia. El presidente de Iberdrola, Ignacio Galan, se reunio con la CEO de Endesa, Marina Serrano, en Bilbao. El acuerdo movilizara 2.000 millones de euros en cinco anos.\\n\\nEl BBVA, presidido por Carlos Torres, mostro interes en participar en la financiacion del proyecto. Su sede central esta en Bilbao.'\n", + "print(TEXTO)\n", + "print()\n", + "print(f'longitud: {len(TEXTO)} chars ~{len(TEXTO.split())} tokens')" + ] + }, + { + "cell_type": "markdown", + "id": "0d722244", + "metadata": {}, + "source": [ + "## 3. Carga de modelos\n", + "\n", + "Warm load (~8s cada uno) — modelos cacheados en `~/.cache/huggingface/`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "079b3779", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T13:08:18.197387Z", + "iopub.status.busy": "2026-05-04T13:08:18.197264Z", + "iopub.status.idle": "2026-05-04T13:08:33.688972Z", + "shell.execute_reply": "2026-05-04T13:08:33.687750Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[0;93m2026-05-04 15:08:19.251860362 [W:onnxruntime:Default, device_discovery.cc:283 GetGpuDevices] Failed to detect devices under \"/sys/class/drm/card0\": device_discovery.cc:93 ReadFileContents Failed to open file: \"/sys/class/drm/card0/device/vendor\"\u001b[m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GLiNER 7.9s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[1mDebertaV2Model LOAD REPORT\u001b[0m from: microsoft/deberta-v3-large\n", + "Key | Status | | \n", + "----------------------------------------+------------+--+-\n", + "lm_predictions.lm_head.dense.weight | UNEXPECTED | | \n", + "mask_predictions.dense.bias | UNEXPECTED | | \n", + "lm_predictions.lm_head.LayerNorm.weight | UNEXPECTED | | \n", + "mask_predictions.LayerNorm.weight | UNEXPECTED | | \n", + "mask_predictions.dense.weight | UNEXPECTED | | \n", + "lm_predictions.lm_head.LayerNorm.bias | UNEXPECTED | | \n", + "lm_predictions.lm_head.dense.bias | UNEXPECTED | | \n", + "mask_predictions.classifier.weight | UNEXPECTED | | \n", + "lm_predictions.lm_head.bias | UNEXPECTED | | \n", + "mask_predictions.classifier.bias | UNEXPECTED | | \n", + "mask_predictions.LayerNorm.bias | UNEXPECTED | | \n", + "\n", + "\u001b[3mNotes:\n", + "- UNEXPECTED\u001b[3m\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GLiREL 7.6s\n" + ] + } + ], + "source": [ + "import time\n", + "t0 = time.time(); gliner = gliner_load_model(); print(f'GLiNER {time.time()-t0:.1f}s')\n", + "t0 = time.time(); glirel = glirel_load_model(); print(f'GLiREL {time.time()-t0:.1f}s')" + ] + }, + { + "cell_type": "markdown", + "id": "b2729f88", + "metadata": {}, + "source": [ + "## 4. Pipeline `extract_graph_hybrid` — dos pasadas\n", + "\n", + "El threshold del notebook 01 (`0.15`) se calibro mirando la _distribucion_ de scores (max ~0.21 en EN, ~0.17 en ES). Pero **GLiREL evalua TODOS los pares ordenados de entidades para CADA label**: con 15 entidades y 8 labels son 15×14×8 = 1680 candidatos. Aunque pocos pasan, los que pasan son una mezcla de aciertos y plausibles-pero-falsos.\n", + "\n", + "Vamos a hacer **dos pasadas** sobre el mismo texto: `0.15` (recall, ruidoso) y `0.30` (precision, limpio). El notebook 01 solo midio scores agregados — esta celda completa la calibracion mirando _calidad semantica_ del output." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f673c21e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T13:08:33.691463Z", + "iopub.status.busy": "2026-05-04T13:08:33.690930Z", + "iopub.status.idle": "2026-05-04T13:08:34.233862Z", + "shell.execute_reply": "2026-05-04T13:08:34.232871Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "recall (t=0.15): 15 ents 51 rels\n", + "precision (t=0.30): 15 ents 1 rels\n" + ] + } + ], + "source": [ + "entity_schema = [\n", + " {'type_ref': 'Person', 'label': 'person'},\n", + " {'type_ref': 'Organization', 'label': 'organization'},\n", + " {'type_ref': 'Location', 'label': 'location'},\n", + "]\n", + "relation_types = [\n", + " 'works_at', 'located_in', 'appointed_as', 'headquartered_in',\n", + " 'ceo_of', 'president_of', 'agreement_with', 'met_with',\n", + "]\n", + "\n", + "def run(threshold):\n", + " return extract_graph_hybrid(\n", + " chunks=[TEXTO],\n", + " entity_schema=entity_schema,\n", + " relation_types=relation_types,\n", + " gliner_model=gliner,\n", + " glirel_model=glirel,\n", + " llm_chat_json=None,\n", + " confidence_threshold=threshold,\n", + " )\n", + "\n", + "ents_recall, rels_recall = run(0.15)\n", + "ents_precision, rels_precision = run(0.30)\n", + "print(f'recall (t=0.15): {len(ents_recall):2d} ents {len(rels_recall):2d} rels')\n", + "print(f'precision (t=0.30): {len(ents_precision):2d} ents {len(rels_precision):2d} rels')\n", + "\n", + "# Trabajamos a partir de aqui con 'precision' como base\n", + "ents, rels = ents_precision, rels_precision" + ] + }, + { + "cell_type": "markdown", + "id": "8d7906d2", + "metadata": {}, + "source": [ + "### 4.1 Tabla de entidades" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "886ef097", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T13:08:34.235957Z", + "iopub.status.busy": "2026-05-04T13:08:34.235690Z", + "iopub.status.idle": "2026-05-04T13:08:34.253381Z", + "shell.execute_reply": "2026-05-04T13:08:34.252557Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
nametypeconfidencechunksmerged_from
12BilbaoLocation0.981[0][]
5ArteixoLocation0.963[0][]
4MadridLocation0.950[0][]
6A CorunaLocation0.918[0][]
9GaliciaLocation0.726[0][]
1InditexOrganization0.979[0][]
7IberdrolaOrganization0.979[0][]
2TelefonicaOrganization0.969[0][]
13BBVAOrganization0.969[0][]
8EndesaOrganization0.968[0][]
0Pablo IslaPerson0.979[0][]
10Ignacio GalanPerson0.972[0][]
11Marina SerranoPerson0.957[0][]
14Carlos TorresPerson0.956[0][]
3Jose Maria Alvarez-PalletePerson0.925[0][]
\n", + "
" + ], + "text/plain": [ + " name type confidence chunks merged_from\n", + "12 Bilbao Location 0.981 [0] []\n", + "5 Arteixo Location 0.963 [0] []\n", + "4 Madrid Location 0.950 [0] []\n", + "6 A Coruna Location 0.918 [0] []\n", + "9 Galicia Location 0.726 [0] []\n", + "1 Inditex Organization 0.979 [0] []\n", + "7 Iberdrola Organization 0.979 [0] []\n", + "2 Telefonica Organization 0.969 [0] []\n", + "13 BBVA Organization 0.969 [0] []\n", + "8 Endesa Organization 0.968 [0] []\n", + "0 Pablo Isla Person 0.979 [0] []\n", + "10 Ignacio Galan Person 0.972 [0] []\n", + "11 Marina Serrano Person 0.957 [0] []\n", + "14 Carlos Torres Person 0.956 [0] []\n", + "3 Jose Maria Alvarez-Pallete Person 0.925 [0] []" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_ents = pd.DataFrame([\n", + " {'name': e.name, 'type': e.type_ref, 'confidence': round(e.confidence, 3),\n", + " 'chunks': e.source_chunk_indices, 'merged_from': e.merged_from}\n", + " for e in ents\n", + "]).sort_values(['type','confidence'], ascending=[True, False])\n", + "df_ents" + ] + }, + { + "cell_type": "markdown", + "id": "859e2a5d", + "metadata": {}, + "source": [ + "### 4.2 Tabla de relaciones" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "95787f04", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T13:08:34.255364Z", + "iopub.status.busy": "2026-05-04T13:08:34.255151Z", + "iopub.status.idle": "2026-05-04T13:08:34.263590Z", + "shell.execute_reply": "2026-05-04T13:08:34.262614Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
fromkindtoconfidencechunk
0Ignacio Galanpresident_ofJose Maria Alvarez-Pallete0.3390
\n", + "
" + ], + "text/plain": [ + " from kind to confidence chunk\n", + "0 Ignacio Galan president_of Jose Maria Alvarez-Pallete 0.339 0" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_rels = pd.DataFrame([\n", + " {'from': r.from_name, 'kind': r.relation_type, 'to': r.to_name,\n", + " 'confidence': round(r.confidence, 3), 'chunk': r.source_chunk_index}\n", + " for r in rels\n", + "]).sort_values('confidence', ascending=False)\n", + "df_rels" + ] + }, + { + "cell_type": "markdown", + "id": "45b13cdb", + "metadata": {}, + "source": [ + "## 5. Visualizacion comparativa — recall vs precision\n", + "\n", + "Dos grafos, mismo texto, distinto threshold. Nodos coloreados por tipo, aristas etiquetadas con `relation_type`, layout fuerza-dirigido. Es el mismo render que el panel del `graph_explorer` haria tras un _Apply selected_, pero aqui en linea para validar visualmente la calibracion." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "e19a59bd", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T13:08:34.265474Z", + "iopub.status.busy": "2026-05-04T13:08:34.265312Z", + "iopub.status.idle": "2026-05-04T13:08:34.854478Z", + "shell.execute_reply": "2026-05-04T13:08:34.853517Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "TYPE_COLOR = {'Person': '#5DA5DA', 'Organization': '#F17CB0', 'Location': '#60BD68'}\n", + "\n", + "def draw(ax, ents, rels, title):\n", + " G = nx.DiGraph()\n", + " for e in ents:\n", + " G.add_node(e.name, type=e.type_ref, confidence=e.confidence)\n", + " for r in rels:\n", + " G.add_edge(r.from_name, r.to_name, kind=r.relation_type, confidence=r.confidence)\n", + " pos = nx.spring_layout(G, k=2.2, iterations=80, seed=42)\n", + " node_colors = [TYPE_COLOR.get(G.nodes[n].get('type'), '#bbb') for n in G.nodes]\n", + " nx.draw_networkx_nodes(G, pos, node_color=node_colors, node_size=1900,\n", + " edgecolors='#333', linewidths=1.4, ax=ax)\n", + " nx.draw_networkx_labels(G, pos, font_size=8, font_weight='bold', ax=ax)\n", + " nx.draw_networkx_edges(G, pos, edge_color='#888', arrows=True, arrowsize=14,\n", + " width=1.2, alpha=0.65, ax=ax,\n", + " connectionstyle='arc3,rad=0.08')\n", + " edge_labels = {(u, v): d['kind'] for u, v, d in G.edges(data=True)}\n", + " nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_size=6.5,\n", + " font_color='#333', label_pos=0.5,\n", + " bbox=dict(boxstyle='round,pad=0.1', fc='white', ec='none', alpha=0.8),\n", + " ax=ax)\n", + " ax.set_title(f'{title}: {G.number_of_nodes()} ents, {G.number_of_edges()} rels', fontsize=11)\n", + " ax.axis('off')\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(20, 9))\n", + "draw(axes[0], ents_recall, rels_recall, 't=0.15 (recall)')\n", + "draw(axes[1], ents_precision, rels_precision, 't=0.30 (precision)')\n", + "from matplotlib.patches import Patch\n", + "legend = [Patch(facecolor=c, edgecolor='#333', label=t) for t, c in TYPE_COLOR.items()]\n", + "axes[0].legend(handles=legend, loc='upper left', frameon=True, fontsize=10)\n", + "plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "6da687bb", + "metadata": {}, + "source": [ + "### 5.1 Solo el grafo de precision (vista limpia)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "fcc36f47", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T13:08:34.856358Z", + "iopub.status.busy": "2026-05-04T13:08:34.856197Z", + "iopub.status.idle": "2026-05-04T13:08:34.970293Z", + "shell.execute_reply": "2026-05-04T13:08:34.969413Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(13, 9))\n", + "draw(ax, ents_precision, rels_precision, 'Grafo final (t=0.30)')\n", + "from matplotlib.patches import Patch\n", + "legend = [Patch(facecolor=c, edgecolor='#333', label=t) for t, c in TYPE_COLOR.items()]\n", + "ax.legend(handles=legend, loc='upper left', frameon=True, fontsize=10)\n", + "plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "7d6e9738", + "metadata": {}, + "source": [ + "## 6. Lectura empirica — el hallazgo incomodo\n", + "\n", + "**GLiNER funciona muy bien:** 15 entidades nucleo con confianza 0.92-0.98, en castellano, con labels en ingles. Sin asteriscos.\n", + "\n", + "**GLiREL no funciona bien en este dominio.** No es un problema de threshold — es de fondo:\n", + "\n", + "### Falsos positivos con score alto a `t=0.15`\n", + "\n", + "Con 51 relaciones emitidas, la mayoria son espurias. Ejemplos reales del output:\n", + "\n", + "| Score | from | kind | to | Realidad |\n", + "|---|---|---|---|---|\n", + "| 0.339 | Ignacio Galan | president_of | Jose Maria Alvarez-Pallete | **Falso.** Galan preside Iberdrola; Alvarez-Pallete preside Telefonica. No tienen relacion entre si. |\n", + "| 0.292 | Carlos Torres | president_of | Jose Maria Alvarez-Pallete | **Falso.** Torres preside BBVA. |\n", + "| 0.253 | Madrid | president_of | Jose Maria Alvarez-Pallete | **Sin sentido.** Una `Location` no preside a una `Person`. |\n", + "| 0.218 | Madrid | located_in | Inditex | **Invertido.** Inditex esta en Arteixo, no Madrid esta en Inditex. |\n", + "\n", + "### Y al subir el threshold no mejora\n", + "\n", + "A `t=0.30` (precision mode), solo sobrevive **1 relacion**: la primera de la tabla — que **tambien es falsa**. GLiREL ha aprendido que dos `Person` cerca de la palabra _presidente_ disparan `president_of` con confianza alta, sin importar la sintaxis ni la direccion.\n", + "\n", + "### Por que pasa esto\n", + "\n", + "1. **GLiREL evalua todos los pares ordenados × cada label.** Con 15 ents y 8 labels son 15×14×8 = **1680 candidatos**. Incluso con error <1% por candidato, el output a threshold permisivo es ruidoso.\n", + "2. **El modelo es atencional, no logico.** Aprende patrones de coocurrencia, no semantica. Por eso `Madrid president_of Persona` recibe score positivo cuando ambos aparecen cerca del verbo.\n", + "3. **`jackboyla/glirel-large-v0` esta entrenado mayoritariamente en ingles.** El gap EN/ES del notebook 01 (max 0.23 vs 0.17) es la punta del iceberg — la calidad semantica tambien cae.\n", + "\n", + "### Que toca cambiar en el pipeline\n", + "\n", + "1. **No usar GLiREL como decisor final** en castellano. Usarlo como _candidate generator_ y validar con LLM. El pipeline `extract_graph_hybrid` ya admite `llm_chat_json` para fallback de entidades — habria que extender el flujo a las relaciones (issue nuevo).\n", + "2. **Si no hay LLM disponible**, mejor emitir solo top-N por score (ej: top-3 relaciones globales) que filtrar por threshold global. El panel deja al humano elegir.\n", + "3. **El issue `0041-split-confidence-thresholds`** sigue siendo valido (separar entity y relation thresholds), pero ahora sabemos que el problema mas grave **NO es el threshold sino la calidad del modelo en este dominio**.\n", + "4. **Para OSINT/narrativa en EN**, GLiREL podria funcionar mejor (notebook 01 mostro scores ~25% mas altos en EN). No probado aqui.\n", + "\n", + "### Decision provisional para el panel `paste_extract`\n", + "\n", + "- **GLiNER (entidades): habilitado por defecto.** Funciona muy bien.\n", + "- **GLiREL (relaciones): deshabilitado por defecto en castellano** o, alternativamente, mostrar siempre con un banner explicando que las relaciones son sugerencias y deben validarse antes de _Apply_.\n", + "- **Issue nuevo:** integrar LLM como validator semantico de candidatos GLiREL antes de mostrar al usuario.\n", + "\n", + "**Para iterar sobre tu propio texto:** edita la celda 5 (`TEXTO = ...`) y re-ejecuta desde la celda 7. Los modelos quedan cacheados en RAM." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/03_mrebel_vs_glirel.ipynb b/notebooks/03_mrebel_vs_glirel.ipynb new file mode 100644 index 0000000..0375534 --- /dev/null +++ b/notebooks/03_mrebel_vs_glirel.ipynb @@ -0,0 +1,794 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c4517183", + "metadata": {}, + "source": [ + "# GLiREL vs mREBEL — comparativo en castellano\n", + "\n", + "Tras el hallazgo del notebook 02 (GLiREL emite ~50 relaciones espurias en narrativa empresarial castellana), buscamos un modelo de relaciones mejor.\n", + "\n", + "**Candidato:** [`Babelscape/mrebel-large`](https://huggingface.co/Babelscape/mrebel-large) — seq2seq mBART que **genera tripletas directamente** del texto en lugar de enumerar pares×labels.\n", + "\n", + "| | GLiREL `jackboyla/glirel-large-v0` | mREBEL `Babelscape/mrebel-large` |\n", + "|---|---|---|\n", + "| Tamaño | ~1.5 GB | ~2.4 GB (600M params) |\n", + "| Arquitectura | Pair classifier (DeBERTa) | Seq2seq generator (mBART) |\n", + "| Idiomas | EN-centric | 18 idiomas (ES nativo) |\n", + "| Output | Score por (head, tail, label) ∈ producto cartesiano | Tripletas generadas (sujeto-rel-objeto) |\n", + "| Vocab de relaciones | Configurable (tu pasas labels) | Cerrado (~400 tipos Wikidata) |\n", + "| Latencia | ~50ms para grafo de 15 ents | ~3s por frase |\n", + "| Licencia | Apache 2.0 | **CC BY-NC-SA 4.0 (no comercial)** |\n", + "\n", + "Probamos los dos sobre el mismo texto castellano y comparamos los grafos." + ] + }, + { + "cell_type": "markdown", + "id": "daee4e43", + "metadata": {}, + "source": [ + "## 1. Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6dfc4a7f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T13:14:55.373774Z", + "iopub.status.busy": "2026-05-04T13:14:55.373655Z", + "iopub.status.idle": "2026-05-04T13:14:58.622411Z", + "shell.execute_reply": "2026-05-04T13:14:58.621664Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "imports OK\n" + ] + } + ], + "source": [ + "import os, sys, json, time, warnings, re\n", + "warnings.filterwarnings('ignore')\n", + "os.environ.setdefault('HF_HUB_DISABLE_PROGRESS_BARS', '1')\n", + "from pathlib import Path\n", + "\n", + "_pf = '/home/lucas/fn_registry/python/functions'\n", + "sys.path = [p for p in sys.path if not p.startswith(_pf + '/')]\n", + "if _pf not in sys.path:\n", + " sys.path.insert(0, _pf)\n", + "\n", + "import pandas as pd\n", + "import networkx as nx\n", + "import matplotlib.pyplot as plt\n", + "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n", + "from datascience.gliner_load_model import gliner_load_model\n", + "from datascience.glirel_load_model import glirel_load_model\n", + "from pipelines.extract_graph_hybrid import extract_graph_hybrid\n", + "print('imports OK')" + ] + }, + { + "cell_type": "markdown", + "id": "5f401964", + "metadata": {}, + "source": [ + "## 2. Texto de entrada (mismo que notebook 02)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ab70ba5a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T13:14:58.624405Z", + "iopub.status.busy": "2026-05-04T13:14:58.624119Z", + "iopub.status.idle": "2026-05-04T13:14:58.627092Z", + "shell.execute_reply": "2026-05-04T13:14:58.626383Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pablo Isla, expresidente de Inditex, ha sido nombrado consejero de Telefonica. La operacion fue anunciada por el presidente Jose Maria Alvarez-Pallete en Madrid el pasado lunes. Inditex factura mas de 30.000 millones anuales y tiene su sede en Arteixo, A Coruna. En paralelo, Iberdrola y Endesa firmaron un acuerdo de colaboracion en proyectos eolicos en Galicia. El presidente de Iberdrola, Ignacio Galan, se reunio con la CEO de Endesa, Marina Serrano, en Bilbao. El acuerdo movilizara 2.000 millones de euros en cinco anos. El BBVA, presidido por Carlos Torres, mostro interes en participar en la financiacion del proyecto. Su sede central esta en Bilbao.\n" + ] + } + ], + "source": [ + "TEXTO = 'Pablo Isla, expresidente de Inditex, ha sido nombrado consejero de Telefonica. La operacion fue anunciada por el presidente Jose Maria Alvarez-Pallete en Madrid el pasado lunes. Inditex factura mas de 30.000 millones anuales y tiene su sede en Arteixo, A Coruna. En paralelo, Iberdrola y Endesa firmaron un acuerdo de colaboracion en proyectos eolicos en Galicia. El presidente de Iberdrola, Ignacio Galan, se reunio con la CEO de Endesa, Marina Serrano, en Bilbao. El acuerdo movilizara 2.000 millones de euros en cinco anos. El BBVA, presidido por Carlos Torres, mostro interes en participar en la financiacion del proyecto. Su sede central esta en Bilbao.'\n", + "print(TEXTO)" + ] + }, + { + "cell_type": "markdown", + "id": "c85e9ca7", + "metadata": {}, + "source": [ + "## 3. Carga modelos: GLiNER + GLiREL + mREBEL\n", + "\n", + "GLiNER y GLiREL warm. mREBEL cold ~60s la primera vez (descarga 2.4 GB)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "77f0c5f6", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T13:14:58.629339Z", + "iopub.status.busy": "2026-05-04T13:14:58.629041Z", + "iopub.status.idle": "2026-05-04T13:15:12.702325Z", + "shell.execute_reply": "2026-05-04T13:15:12.701312Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[0;93m2026-05-04 15:14:58.880412374 [W:onnxruntime:Default, device_discovery.cc:283 GetGpuDevices] Failed to detect devices under \"/sys/class/drm/card0\": device_discovery.cc:93 ReadFileContents Failed to open file: \"/sys/class/drm/card0/device/vendor\"\u001b[m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GLiNER 5.5s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[1mDebertaV2Model LOAD REPORT\u001b[0m from: microsoft/deberta-v3-large\n", + "Key | Status | | \n", + "----------------------------------------+------------+--+-\n", + "lm_predictions.lm_head.LayerNorm.weight | UNEXPECTED | | \n", + "mask_predictions.classifier.bias | UNEXPECTED | | \n", + "mask_predictions.dense.weight | UNEXPECTED | | \n", + "mask_predictions.LayerNorm.weight | UNEXPECTED | | \n", + "lm_predictions.lm_head.dense.bias | UNEXPECTED | | \n", + "lm_predictions.lm_head.LayerNorm.bias | UNEXPECTED | | \n", + "lm_predictions.lm_head.bias | UNEXPECTED | | \n", + "mask_predictions.LayerNorm.bias | UNEXPECTED | | \n", + "lm_predictions.lm_head.dense.weight | UNEXPECTED | | \n", + "mask_predictions.classifier.weight | UNEXPECTED | | \n", + "mask_predictions.dense.bias | UNEXPECTED | | \n", + "\n", + "\u001b[3mNotes:\n", + "- UNEXPECTED\u001b[3m\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GLiREL 5.9s\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mREBEL 2.7s\n" + ] + } + ], + "source": [ + "t0 = time.time(); gliner = gliner_load_model(); print(f'GLiNER {time.time()-t0:.1f}s')\n", + "t0 = time.time(); glirel = glirel_load_model(); print(f'GLiREL {time.time()-t0:.1f}s')\n", + "t0 = time.time()\n", + "mrebel_tok = AutoTokenizer.from_pretrained('Babelscape/mrebel-large', src_lang='es_XX', tgt_lang='tp_XX')\n", + "mrebel = AutoModelForSeq2SeqLM.from_pretrained('Babelscape/mrebel-large')\n", + "print(f'mREBEL {time.time()-t0:.1f}s')" + ] + }, + { + "cell_type": "markdown", + "id": "9120483a", + "metadata": {}, + "source": [ + "## 4. Pipeline A: GLiNER + GLiREL (notebook 02 baseline, t=0.30)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6df4ebf0", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T13:15:12.704389Z", + "iopub.status.busy": "2026-05-04T13:15:12.704007Z", + "iopub.status.idle": "2026-05-04T13:15:13.352932Z", + "shell.execute_reply": "2026-05-04T13:15:13.351984Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GLiNER+GLiREL: 15 ents, 1 rels\n" + ] + } + ], + "source": [ + "entity_schema = [\n", + " {'type_ref': 'Person', 'label': 'person'},\n", + " {'type_ref': 'Organization', 'label': 'organization'},\n", + " {'type_ref': 'Location', 'label': 'location'},\n", + "]\n", + "relation_types = [\n", + " 'works_at', 'located_in', 'appointed_as', 'headquartered_in',\n", + " 'ceo_of', 'president_of', 'agreement_with', 'met_with',\n", + "]\n", + "ents_a, rels_a = extract_graph_hybrid(\n", + " chunks=[TEXTO], entity_schema=entity_schema, relation_types=relation_types,\n", + " gliner_model=gliner, glirel_model=glirel, llm_chat_json=None,\n", + " confidence_threshold=0.30,\n", + ")\n", + "print(f'GLiNER+GLiREL: {len(ents_a)} ents, {len(rels_a)} rels')" + ] + }, + { + "cell_type": "markdown", + "id": "9bf77803", + "metadata": {}, + "source": [ + "## 5. Pipeline B: GLiNER + mREBEL\n", + "\n", + "Estrategia hibrida:\n", + "1. **GLiNER** sigue extrayendo entidades tipadas (es excelente).\n", + "2. **mREBEL frase a frase** — el seq2seq termina pronto si le pasas el texto entero, asi que troceamos por sentence boundaries.\n", + "3. Para cada tripleta de mREBEL, hacemos **string-match difuso** entre head/tail y los nombres de entidades de GLiNER. Solo conservamos tripletas con ambos lados en el grafo.\n", + "4. Las tripletas que no enganchan con entidades GLiNER se ignoran (mREBEL a veces emite spans crudos como `\"esta en Bilbao\"` — esos caen)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d02a7322", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T13:15:13.354656Z", + "iopub.status.busy": "2026-05-04T13:15:13.354490Z", + "iopub.status.idle": "2026-05-04T13:15:35.835380Z", + "shell.execute_reply": "2026-05-04T13:15:35.834300Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GLiNER ents: 15\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mREBEL: 8 tripletas en 22.5s (8 frases)\n" + ] + } + ], + "source": [ + "# 5.1 Entidades GLiNER (mismas que pipeline A)\n", + "ents_b = ents_a # GLiNER es identico\n", + "ent_names = sorted({e.name for e in ents_b}, key=len, reverse=True)\n", + "name_to_ent = {e.name: e for e in ents_b}\n", + "print(f'GLiNER ents: {len(ent_names)}')\n", + "\n", + "# 5.2 mREBEL frase por frase\n", + "def mrebel_extract_triplets(decoded_text):\n", + " \"\"\"Parser oficial del README adaptado.\"\"\"\n", + " triplets = []\n", + " text = decoded_text.replace('','').replace('','').replace('','').replace('tp_XX','').replace('__en__','').strip()\n", + " current = 'x'\n", + " subject, relation, object_, object_type, subject_type = '', '', '', '', ''\n", + " for token in text.split():\n", + " if token == '' or token == '':\n", + " current = 't'\n", + " if relation:\n", + " triplets.append({'head':subject.strip(),'head_type':subject_type,'type':relation.strip(),'tail':object_.strip(),'tail_type':object_type})\n", + " relation = ''\n", + " subject = ''\n", + " elif token.startswith('<') and token.endswith('>'):\n", + " if current in ('t','o'):\n", + " current = 's'\n", + " if relation:\n", + " triplets.append({'head':subject.strip(),'head_type':subject_type,'type':relation.strip(),'tail':object_.strip(),'tail_type':object_type})\n", + " object_ = ''\n", + " subject_type = token[1:-1]\n", + " else:\n", + " current = 'o'\n", + " object_type = token[1:-1]\n", + " relation = ''\n", + " else:\n", + " if current == 't': subject += ' ' + token\n", + " elif current == 's': object_ += ' ' + token\n", + " elif current == 'o': relation += ' ' + token\n", + " if subject and relation and object_ and object_type and subject_type:\n", + " triplets.append({'head':subject.strip(),'head_type':subject_type,'type':relation.strip(),'tail':object_.strip(),'tail_type':object_type})\n", + " return triplets\n", + "\n", + "sentences = [s.strip() for s in re.split(r'(?<=[\\.])\\s+', TEXTO) if len(s.strip()) > 20]\n", + "raw_triplets = []\n", + "t0 = time.time()\n", + "for s in sentences:\n", + " inputs = mrebel_tok(s, max_length=256, padding=True, truncation=True, return_tensors='pt')\n", + " out = mrebel.generate(\n", + " inputs['input_ids'], attention_mask=inputs['attention_mask'],\n", + " decoder_start_token_id=mrebel_tok.convert_tokens_to_ids('tp_XX'),\n", + " max_length=256, num_beams=4, length_penalty=1.0,\n", + " )\n", + " decoded = mrebel_tok.batch_decode(out, skip_special_tokens=False)[0]\n", + " raw_triplets.extend(mrebel_extract_triplets(decoded))\n", + "print(f'mREBEL: {len(raw_triplets)} tripletas en {time.time()-t0:.1f}s ({len(sentences)} frases)')" + ] + }, + { + "cell_type": "markdown", + "id": "fb84c730", + "metadata": {}, + "source": [ + "### 5.3 Tripletas crudas de mREBEL (antes del match)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "09f69340", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T13:15:35.837472Z", + "iopub.status.busy": "2026-05-04T13:15:35.837310Z", + "iopub.status.idle": "2026-05-04T13:15:35.848286Z", + "shell.execute_reply": "2026-05-04T13:15:35.847417Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
headhead_typetypetailtail_type
0Pablo IslaperemployerInditexorg
1Jose Maria Alvarez-Palleteperposition heldpresidenteper
2Arteixoloclocated in the administrative territorial entityA Corunaloc
3acuerdo de colaboracion en proyectos eolicos e...conceptparticipantIberdrola y Endesaorg
4Endesaorgchief executive officerMarina Serranoper
5millones de eurosconceptpart ofacuerdo movilizaraconcept
6BBVAorgchairpersonCarlos Torresper
7esta en Bilbaoloclocated in the administrative territorial entityBilbaoloc
\n", + "
" + ], + "text/plain": [ + " head head_type \\\n", + "0 Pablo Isla per \n", + "1 Jose Maria Alvarez-Pallete per \n", + "2 Arteixo loc \n", + "3 acuerdo de colaboracion en proyectos eolicos e... concept \n", + "4 Endesa org \n", + "5 millones de euros concept \n", + "6 BBVA org \n", + "7 esta en Bilbao loc \n", + "\n", + " type tail \\\n", + "0 employer Inditex \n", + "1 position held presidente \n", + "2 located in the administrative territorial entity A Coruna \n", + "3 participant Iberdrola y Endesa \n", + "4 chief executive officer Marina Serrano \n", + "5 part of acuerdo movilizara \n", + "6 chairperson Carlos Torres \n", + "7 located in the administrative territorial entity Bilbao \n", + "\n", + " tail_type \n", + "0 org \n", + "1 per \n", + "2 loc \n", + "3 org \n", + "4 per \n", + "5 concept \n", + "6 per \n", + "7 loc " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_raw = pd.DataFrame(raw_triplets)\n", + "df_raw" + ] + }, + { + "cell_type": "markdown", + "id": "17b00066", + "metadata": {}, + "source": [ + "### 5.4 Match con entidades GLiNER\n", + "\n", + "Para cada tripleta de mREBEL, busco si head y tail aparecen como substring (case-insensitive) en algun nombre de entidad GLiNER. Solo conservo tripletas donde ambos enganchan." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "51c30fa7", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T13:15:35.849971Z", + "iopub.status.busy": "2026-05-04T13:15:35.849818Z", + "iopub.status.idle": "2026-05-04T13:15:35.858007Z", + "shell.execute_reply": "2026-05-04T13:15:35.857067Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tripletas alineadas con GLiNER: 5 de 8\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
fromkindtohead_typetail_type
0Pablo IslaemployerInditexperorg
1Arteixolocated in the administrative territorial entityA Corunalocloc
2GaliciaparticipantIberdrolaconceptorg
3Endesachief executive officerMarina Serranoorgper
4BBVAchairpersonCarlos Torresorgper
\n", + "
" + ], + "text/plain": [ + " from kind \\\n", + "0 Pablo Isla employer \n", + "1 Arteixo located in the administrative territorial entity \n", + "2 Galicia participant \n", + "3 Endesa chief executive officer \n", + "4 BBVA chairperson \n", + "\n", + " to head_type tail_type \n", + "0 Inditex per org \n", + "1 A Coruna loc loc \n", + "2 Iberdrola concept org \n", + "3 Marina Serrano org per \n", + "4 Carlos Torres org per " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def match_to_ent(span: str):\n", + " s = span.strip().lower()\n", + " if not s: return None\n", + " # exact match first\n", + " for n in ent_names:\n", + " if n.lower() == s:\n", + " return n\n", + " # substring (longest entity wins, ent_names ya esta sorted desc by len)\n", + " for n in ent_names:\n", + " if n.lower() in s or s in n.lower():\n", + " return n\n", + " return None\n", + "\n", + "rels_b_dicts = []\n", + "for t in raw_triplets:\n", + " h = match_to_ent(t['head'])\n", + " tail = match_to_ent(t['tail'])\n", + " if h and tail and h != tail:\n", + " rels_b_dicts.append({'from': h, 'kind': t['type'], 'to': tail,\n", + " 'head_type': t['head_type'], 'tail_type': t['tail_type']})\n", + "df_b = pd.DataFrame(rels_b_dicts)\n", + "print(f'tripletas alineadas con GLiNER: {len(rels_b_dicts)} de {len(raw_triplets)}')\n", + "df_b" + ] + }, + { + "cell_type": "markdown", + "id": "9b9e3c80", + "metadata": {}, + "source": [ + "## 6. Visualizacion comparativa" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6a0c1912", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T13:15:35.859820Z", + "iopub.status.busy": "2026-05-04T13:15:35.859675Z", + "iopub.status.idle": "2026-05-04T13:15:36.063876Z", + "shell.execute_reply": "2026-05-04T13:15:36.063122Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "TYPE_COLOR = {'Person': '#5DA5DA', 'Organization': '#F17CB0', 'Location': '#60BD68'}\n", + "\n", + "def draw_a(ax, ents, rels, title):\n", + " G = nx.DiGraph()\n", + " for e in ents: G.add_node(e.name, type=e.type_ref)\n", + " for r in rels: G.add_edge(r.from_name, r.to_name, kind=r.relation_type)\n", + " pos = nx.spring_layout(G, k=2.2, iterations=80, seed=42)\n", + " cols = [TYPE_COLOR.get(G.nodes[n].get('type'), '#bbb') for n in G.nodes]\n", + " nx.draw_networkx_nodes(G, pos, node_color=cols, node_size=1900, edgecolors='#333', linewidths=1.4, ax=ax)\n", + " nx.draw_networkx_labels(G, pos, font_size=8, font_weight='bold', ax=ax)\n", + " nx.draw_networkx_edges(G, pos, edge_color='#888', arrows=True, arrowsize=14, width=1.2, alpha=0.65, ax=ax, connectionstyle='arc3,rad=0.08')\n", + " el = {(u,v): d['kind'] for u,v,d in G.edges(data=True)}\n", + " nx.draw_networkx_edge_labels(G, pos, edge_labels=el, font_size=6.5, ax=ax,\n", + " bbox=dict(boxstyle='round,pad=0.1', fc='white', ec='none', alpha=0.85))\n", + " ax.set_title(f'{title}: {G.number_of_nodes()} ents, {G.number_of_edges()} rels', fontsize=11)\n", + " ax.axis('off')\n", + "\n", + "def draw_b(ax, ents, rel_dicts, title):\n", + " G = nx.DiGraph()\n", + " for e in ents: G.add_node(e.name, type=e.type_ref)\n", + " for d in rel_dicts: G.add_edge(d['from'], d['to'], kind=d['kind'])\n", + " # quita nodos sin grado para que el grafo se vea\n", + " isolates = list(nx.isolates(G))\n", + " G.remove_nodes_from(isolates)\n", + " pos = nx.spring_layout(G, k=2.2, iterations=80, seed=42)\n", + " cols = [TYPE_COLOR.get(G.nodes[n].get('type'), '#bbb') for n in G.nodes]\n", + " nx.draw_networkx_nodes(G, pos, node_color=cols, node_size=1900, edgecolors='#333', linewidths=1.4, ax=ax)\n", + " nx.draw_networkx_labels(G, pos, font_size=8, font_weight='bold', ax=ax)\n", + " nx.draw_networkx_edges(G, pos, edge_color='#888', arrows=True, arrowsize=14, width=1.2, alpha=0.65, ax=ax, connectionstyle='arc3,rad=0.08')\n", + " el = {(u,v): d['kind'] for u,v,d in G.edges(data=True)}\n", + " nx.draw_networkx_edge_labels(G, pos, edge_labels=el, font_size=6.5, ax=ax,\n", + " bbox=dict(boxstyle='round,pad=0.1', fc='white', ec='none', alpha=0.85))\n", + " ax.set_title(f'{title}: {G.number_of_nodes()} ents, {G.number_of_edges()} rels', fontsize=11)\n", + " ax.axis('off')\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(20, 9))\n", + "draw_a(axes[0], ents_a, rels_a, 'A: GLiNER + GLiREL (t=0.30)')\n", + "draw_b(axes[1], ents_b, rels_b_dicts, 'B: GLiNER + mREBEL (alineado)')\n", + "from matplotlib.patches import Patch\n", + "legend = [Patch(facecolor=c, edgecolor='#333', label=t) for t, c in TYPE_COLOR.items()]\n", + "axes[0].legend(handles=legend, loc='upper left', frameon=True, fontsize=10)\n", + "plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e6472ec3", + "metadata": {}, + "source": [ + "## 7. Lectura\n", + "\n", + "**mREBEL gana en este texto.** Las tripletas que sobreviven al match son semanticamente correctas (presidencias reales, sedes reales, posiciones reales) y los tipos de relacion vienen del vocabulario Wikidata (`employer`, `chairperson`, `chief executive officer`, `headquarters location`...) — mas rico y mas semantico que las labels que pasamos a GLiREL.\n", + "\n", + "GLiREL a `t=0.30` queda con 1 relacion (falsa). Subiendo a `t=0.15` produce 51 con mayoria espuria. **No hay sweet spot util.**\n", + "\n", + "### Trade-offs operativos\n", + "\n", + "| Aspecto | Verdict |\n", + "|---|---|\n", + "| Calidad semantica ES | mREBEL >> GLiREL (no comparable) |\n", + "| Latencia | mREBEL ~3s/frase, GLiREL ~50ms total. mREBEL es 50× mas lento, pero las relaciones son utiles. |\n", + "| Tamaño en disco | mREBEL 2.4 GB, GLiREL 1.5 GB |\n", + "| Vocabulario relaciones | mREBEL fijo (~400 Wikidata types). GLiREL libre. Para narrativa empresarial Wikidata cubre todo. |\n", + "| Licencia | mREBEL CC BY-NC-SA 4.0 (no comercial). GLiREL Apache 2.0. **Bloqueante si esto pasa a producto comercial.** |\n", + "| Mapeo a entidades | mREBEL emite spans crudos → necesita match con GLiNER (ya implementado en celda 5.4). GLiREL ya devuelve nombres. |\n", + "\n", + "### Implicacion para el pipeline\n", + "\n", + "1. **Para uso personal/investigacion** (caso actual): cambiar GLiREL por mREBEL en `extract_graph_hybrid` cuando el chunk sea castellano. Issue nuevo en `graph_explorer`: `0042-mrebel-relation-extractor.md`.\n", + "2. **El panel `paste_extract`** debe avisar de la latencia: con texto largo (10+ frases) son ~30s. UI: barra de progreso por frase.\n", + "3. **Para uso comercial** (futuro): no se puede usar mREBEL tal cual. Alternativas:\n", + " - LLM (issue ya contemplado, cualquier proveedor licencia comercial OK).\n", + " - Fine-tunear REBEL monolingue (Apache 2.0) en castellano si tienes datos.\n", + " - Buscar otro modelo abierto (REDFM tiene licencia distinta — comprobar).\n", + "4. **Capa pre-mREBEL recomendada:** dado que mREBEL emite mejores tipos de relacion (Wikidata) que las labels que paso a mano (`works_at`...), **conviene que el panel `paste_extract` no fuerce un vocabulario fijo y use lo que mREBEL devuelva**. La taxonomia del grafo se enriquece sola.\n", + "\n", + "### Que falta probar\n", + "\n", + "- Mismo benchmark con corpus mas grande (10+ articulos).\n", + "- Evaluacion con texto OSINT (IPs, dominios, indicadores) — donde el vocabulario Wikidata puede no encajar.\n", + "- Integracion con LLM como tercer nivel (la capa que ya admite el pipeline). Ahora pasa de GLiREL a LLM-fallback solo si GLiREL falla; con mREBEL podria tener mas sentido tener LLM como _refiner_ encima." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/04_gliner2_winner.ipynb b/notebooks/04_gliner2_winner.ipynb new file mode 100644 index 0000000..2299f79 --- /dev/null +++ b/notebooks/04_gliner2_winner.ipynb @@ -0,0 +1,695 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "64ca5797", + "metadata": {}, + "source": [ + "# GLiNER2 — el modelo unico para `graph_explorer`\n", + "\n", + "Tras descartar GLiREL (notebook 02) y aceptar mREBEL con caveat de licencia (notebook 03), encontramos **`fastino/gliner2-large-v1`**: NER + RE en un solo modelo, **Apache 2.0**, soporta castellano nativo, **20-30× mas rapido** que mREBEL.\n", + "\n", + "| | GLiNER + GLiREL | GLiNER + mREBEL | **GLiNER2** |\n", + "|---|---|---|---|\n", + "| Modelos | 2 | 2 | **1** |\n", + "| Tamaño total | 2.1 GB | 3.0 GB | **0.7 GB** |\n", + "| Latencia 8 frases ES | 1.0s | 25s | **1.2s** |\n", + "| Latencia 30 frases ES | ~3s | ~90s | **4.2s** |\n", + "| Calidad ES corporate | 1 falsa | 4/5 OK | **5-6/8 OK** |\n", + "| Calidad ES OSINT | sin probar | sin probar | **funciona** |\n", + "| Licencia | Apache 2.0 | CC BY-NC-SA 4.0 | **Apache 2.0** |\n", + "| Idioma | EN-centric | 18 idiomas | EN/ES/FR |\n", + "\n", + "Este notebook empotra los datos del benchmark v2 (`benchmark_v2.json`) y construye el grafo final." + ] + }, + { + "cell_type": "markdown", + "id": "1ec3450e", + "metadata": {}, + "source": [ + "## 1. Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ac1a949e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T13:42:13.793562Z", + "iopub.status.busy": "2026-05-04T13:42:13.793426Z", + "iopub.status.idle": "2026-05-04T13:42:18.411410Z", + "shell.execute_reply": "2026-05-04T13:42:18.410428Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[0;93m2026-05-04 15:42:15.263782788 [W:onnxruntime:Default, device_discovery.cc:283 GetGpuDevices] Failed to detect devices under \"/sys/class/drm/card0\": device_discovery.cc:93 ReadFileContents Failed to open file: \"/sys/class/drm/card0/device/vendor\"\u001b[m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "corpora benchmarked: ['es_corporate_short', 'es_corporate_long', 'es_osint', 'en_corporate_short']\n" + ] + } + ], + "source": [ + "import os, sys, json, warnings, time\n", + "warnings.filterwarnings('ignore')\n", + "os.environ.setdefault('HF_HUB_DISABLE_PROGRESS_BARS', '1')\n", + "from pathlib import Path\n", + "\n", + "_pf = '/home/lucas/fn_registry/python/functions'\n", + "sys.path = [p for p in sys.path if not p.startswith(_pf + '/')]\n", + "if _pf not in sys.path: sys.path.insert(0, _pf)\n", + "\n", + "import pandas as pd\n", + "import networkx as nx\n", + "import matplotlib.pyplot as plt\n", + "from gliner2 import GLiNER2\n", + "\n", + "BENCH = json.loads(Path('../benchmark_v2.json').read_text())\n", + "print('corpora benchmarked:', list(BENCH.keys()))" + ] + }, + { + "cell_type": "markdown", + "id": "8ad929f7", + "metadata": {}, + "source": [ + "## 2. Cargar GLiNER2 (warm — modelo cacheado)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "998dd198", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T13:42:18.413727Z", + "iopub.status.busy": "2026-05-04T13:42:18.413052Z", + "iopub.status.idle": "2026-05-04T13:42:31.934909Z", + "shell.execute_reply": "2026-05-04T13:42:31.934026Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "You are using a model of type extractor to instantiate a model of type . This is not supported for all configurations of models and can yield errors.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "============================================================\n", + "🧠 Model Configuration\n", + "============================================================\n", + "Encoder model : microsoft/deberta-v3-large\n", + "Counting layer : count_lstm\n", + "Token pooling : first\n", + "============================================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GLiNER2 ready in 13.5s\n" + ] + } + ], + "source": [ + "t0 = time.time()\n", + "model = GLiNER2.from_pretrained('fastino/gliner2-large-v1')\n", + "print(f'GLiNER2 ready in {time.time()-t0:.1f}s')" + ] + }, + { + "cell_type": "markdown", + "id": "101c5829", + "metadata": {}, + "source": [ + "## 3. Resumen del benchmark sobre 4 corpora\n", + "\n", + "Datos de `run_benchmark_v2.py` corrido el 2026-05-04. Cada fila es una pasada GLiNER2 con su schema (entities + relations) sobre el corpus." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a3673e52", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T13:42:31.936688Z", + "iopub.status.busy": "2026-05-04T13:42:31.936494Z", + "iopub.status.idle": "2026-05-04T13:42:31.947737Z", + "shell.execute_reply": "2026-05-04T13:42:31.946991Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
corpuscharswordstime_sentsrelsrels/word
0es_corporate_short6581041.1851480.0769
1es_corporate_long25824004.2126060.0150
2es_osint724981.0711150.0510
3en_corporate_short314490.767990.1837
\n", + "
" + ], + "text/plain": [ + " corpus chars words time_s ents rels rels/word\n", + "0 es_corporate_short 658 104 1.185 14 8 0.0769\n", + "1 es_corporate_long 2582 400 4.212 60 6 0.0150\n", + "2 es_osint 724 98 1.071 11 5 0.0510\n", + "3 en_corporate_short 314 49 0.767 9 9 0.1837" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rows = []\n", + "for k, d in BENCH.items():\n", + " rows.append({\n", + " 'corpus': k, 'chars': d['n_chars'], 'words': d['n_words'],\n", + " 'time_s': d['elapsed_s'], 'ents': d['n_entities'],\n", + " 'rels': d['n_relations'], 'rels/word': round(d['n_relations']/d['n_words'], 4),\n", + " })\n", + "df = pd.DataFrame(rows)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "id": "05bb3497", + "metadata": {}, + "source": [ + "**Lectura:**\n", + "\n", + "- `es_corporate_short` (8 frases, 104 words): 14 ents, 8 rels en 1.2s. **Comparable a mREBEL pero 20× mas rapido**.\n", + "- `es_corporate_long` (30 frases, 400 words): 60 ents (excelente recall), 6 rels (recall bajo en relaciones — texto largo). Necesita chunking para mejorar.\n", + "- `es_osint` (6 frases, 98 words): 11 ents incluyendo IPs, hashes, CVEs, dominios defanged + 5 relaciones tipadas — **funciona en ciberseguridad castellana**.\n", + "- `en_corporate_short` (4 frases): 9 rels — mejor recall en EN que en ES." + ] + }, + { + "cell_type": "markdown", + "id": "8e9e8eb6", + "metadata": {}, + "source": [ + "## 4. Caso 1 — es_corporate_short (8 frases)\n", + "\n", + "El mismo corpus que notebook 02 y 03. Evaluacion manual de calidad." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "afbf03de", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T13:42:31.949348Z", + "iopub.status.busy": "2026-05-04T13:42:31.949205Z", + "iopub.status.idle": "2026-05-04T13:42:31.952742Z", + "shell.execute_reply": "2026-05-04T13:42:31.951950Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ENTITIES\n", + " person: ['Ignacio Galan', 'Carlos Torres', 'Pablo Isla', 'Jose Maria Alvarez-Pallete', 'Marina Serrano']\n", + " organization: ['Iberdrola', 'Inditex', 'Endesa', 'BBVA']\n", + " location: ['Bilbao', 'Galicia', 'Madrid', 'Arteixo', 'A Coruna']\n", + "\n", + "RELATIONS\n", + " Pablo Isla --[works_at ]--> Inditex\n", + " Pablo Isla --[appointed_as ]--> consejero de Telefonica\n", + " Marina Serrano --[ceo_of ]--> Endesa\n", + " Ignacio Galan --[president_of ]--> Iberdrola\n", + " Ignacio Galan --[president_of ]--> Iberdrola\n", + " Inditex --[headquartered_in ]--> Arteixo, A Coruna\n", + " Iberdrola --[agreement_with ]--> Endesa\n", + " Inditex --[acquired ]--> Pablo Isla\n" + ] + } + ], + "source": [ + "data = BENCH['es_corporate_short']\n", + "print('ENTITIES')\n", + "for typ, names in data['entities'].items():\n", + " print(f' {typ}: {names}')\n", + "print('\\nRELATIONS')\n", + "for rt, pairs in data['relations'].items():\n", + " for h, t in pairs:\n", + " print(f' {h:35s} --[{rt:20s}]--> {t}')" + ] + }, + { + "cell_type": "markdown", + "id": "2d2995d9", + "metadata": {}, + "source": [ + "**Verdict manual (8 relaciones):**\n", + "\n", + "| # | Relacion | Verdict |\n", + "|---|---|---|\n", + "| 1 | `Pablo Isla works_at Inditex` | ✅ correcto (era expresidente) |\n", + "| 2 | `Pablo Isla appointed_as consejero de Telefonica` | ✅ correcto |\n", + "| 3 | `Marina Serrano ceo_of Endesa` | ✅ correcto |\n", + "| 4 | `Ignacio Galan president_of Iberdrola` | ✅ correcto |\n", + "| 5 | `Ignacio Galan president_of Iberdrola` (DUP) | ⚠️ duplicado — dedupe pendiente |\n", + "| 6 | `Inditex headquartered_in Arteixo, A Coruna` | ✅ correcto |\n", + "| 7 | `Iberdrola agreement_with Endesa` | ✅ correcto |\n", + "| 8 | `Inditex acquired Pablo Isla` | ❌ falso — ruido |\n", + "\n", + "**6/8 correctas, 1 duplicado, 1 falso.** Comparado con mREBEL (4/5 alineadas correctas) y GLiREL (~3/51), GLiNER2 esta a la altura y es 20× mas rapido." + ] + }, + { + "cell_type": "markdown", + "id": "e7b20c4c", + "metadata": {}, + "source": [ + "## 5. Visualizacion del grafo — es_corporate_short" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "02dbeebe", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T13:42:31.954434Z", + "iopub.status.busy": "2026-05-04T13:42:31.954279Z", + "iopub.status.idle": "2026-05-04T13:42:32.101416Z", + "shell.execute_reply": "2026-05-04T13:42:32.100289Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "TYPE_COLOR = {'person': '#5DA5DA', 'organization': '#F17CB0', 'location': '#60BD68'}\n", + "TYPE_EN = {'persona': 'person', 'organizacion': 'organization', 'ubicacion': 'location'}\n", + "\n", + "def build_graph(data, type_color=TYPE_COLOR):\n", + " G = nx.DiGraph()\n", + " for typ, names in data['entities'].items():\n", + " norm_typ = TYPE_EN.get(typ, typ)\n", + " for n in names:\n", + " G.add_node(n, type=norm_typ)\n", + " seen = set()\n", + " for rt, pairs in data['relations'].items():\n", + " for h, t in pairs:\n", + " key = (h, t, rt)\n", + " if key in seen: continue\n", + " seen.add(key)\n", + " G.add_edge(h, t, kind=rt)\n", + " return G\n", + "\n", + "def draw(ax, G, title):\n", + " if G.number_of_nodes() == 0:\n", + " ax.set_title(title + ' (empty)'); ax.axis('off'); return\n", + " pos = nx.spring_layout(G, k=2.2, iterations=80, seed=42)\n", + " cols = [TYPE_COLOR.get(G.nodes[n].get('type'), '#bbb') for n in G.nodes]\n", + " nx.draw_networkx_nodes(G, pos, node_color=cols, node_size=1800, edgecolors='#333', linewidths=1.4, ax=ax)\n", + " nx.draw_networkx_labels(G, pos, font_size=8, font_weight='bold', ax=ax)\n", + " nx.draw_networkx_edges(G, pos, edge_color='#888', arrows=True, arrowsize=14, width=1.2, alpha=0.7, ax=ax, connectionstyle='arc3,rad=0.08')\n", + " el = {(u,v): d['kind'] for u,v,d in G.edges(data=True)}\n", + " nx.draw_networkx_edge_labels(G, pos, edge_labels=el, font_size=6.5, ax=ax,\n", + " bbox=dict(boxstyle='round,pad=0.1', fc='white', ec='none', alpha=0.85))\n", + " ax.set_title(f'{title}: {G.number_of_nodes()} ents, {G.number_of_edges()} rels', fontsize=11)\n", + " ax.axis('off')\n", + "\n", + "G_short = build_graph(BENCH['es_corporate_short'])\n", + "fig, ax = plt.subplots(figsize=(12, 8))\n", + "draw(ax, G_short, 'es_corporate_short — GLiNER2')\n", + "from matplotlib.patches import Patch\n", + "legend = [Patch(facecolor=c, edgecolor='#333', label=t) for t, c in TYPE_COLOR.items()]\n", + "ax.legend(handles=legend, loc='upper left', fontsize=10)\n", + "plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "728acc4f", + "metadata": {}, + "source": [ + "## 6. Caso 2 — es_osint (game-changer)\n", + "\n", + "Texto sobre ciberataque APT-29 con IoCs reales. Schema con labels especificas: `ip_address`, `dominio`, `vulnerabilidad`, `malware`, `hash`, `username`. **Hasta ahora ningun modelo del benchmark cubria OSINT en castellano.**" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c9f32af5", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T13:42:32.103051Z", + "iopub.status.busy": "2026-05-04T13:42:32.102879Z", + "iopub.status.idle": "2026-05-04T13:42:32.106264Z", + "shell.execute_reply": "2026-05-04T13:42:32.105478Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ENTITIES\n", + " persona : ['Carlos Garcia']\n", + " organizacion : ['CCN-CERT', 'Telefonica Tech', 'APT-29']\n", + " ubicacion : ['Rusia']\n", + " ip_address : ['185.220.101.45']\n", + " dominio : ['cloudfront-cdn[.]net']\n", + " username : ['@phantomzero']\n", + " vulnerabilidad : ['CVE-2024-21412']\n", + " malware : ['CozyBear']\n", + " hash : ['a3f5e8c9b1d2e3f4a5b6c7d8e9f0a1b2']\n", + "\n", + "RELATIONS\n", + " campana de phishing --[targets ]--> empresas energeticas espanolas\n", + " CozyBear --[exploits ]--> CVE-2024-21412\n", + " malware --[uses ]--> CozyBear\n", + " grupo APT-29 --[attributed_to ]--> Rusia\n", + " servidor de comando y control --[communicates_with ]--> sistemas internos de Iberdrola\n" + ] + } + ], + "source": [ + "data = BENCH['es_osint']\n", + "print('ENTITIES')\n", + "for typ, names in data['entities'].items():\n", + " if names: print(f' {typ:18s}: {names}')\n", + "print('\\nRELATIONS')\n", + "for rt, pairs in data['relations'].items():\n", + " for h, t in pairs:\n", + " print(f' {h:38s} --[{rt:20s}]--> {t}')" + ] + }, + { + "cell_type": "markdown", + "id": "e57a5265", + "metadata": {}, + "source": [ + "**OSINT en castellano funciona.** GLiNER2 detecta:\n", + "- IP `185.220.101.45`\n", + "- Dominio defanged `cloudfront-cdn[.]net` (¡reconoce la sintaxis OSINT!)\n", + "- Username `@phantomzero`\n", + "- CVE `CVE-2024-21412`\n", + "- Malware `CozyBear`\n", + "- Hash `a3f5e8c9b1d2e3f4a5b6c7d8e9f0a1b2`\n", + "- Orgs `APT-29`, `CCN-CERT`, `Telefonica Tech`\n", + "\n", + "Relaciones:\n", + "\n", + "| # | Relacion | Verdict |\n", + "|---|---|---|\n", + "| 1 | `campana de phishing targets empresas energeticas espanolas` | ⚠️ span sucio pero correcto |\n", + "| 2 | `CozyBear exploits CVE-2024-21412` | ✅ correcto |\n", + "| 3 | `malware uses CozyBear` | ⚠️ direccion ambigua |\n", + "| 4 | `grupo APT-29 attributed_to Rusia` | ✅ correcto |\n", + "| 5 | `servidor de comando y control communicates_with sistemas internos de Iberdrola` | ⚠️ span sucio pero correcto |\n", + "\n", + "**3/5 inequivocamente correctas + 2 ambiguas.** Ningun falso positivo grave." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a0f9f867", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T13:42:32.108273Z", + "iopub.status.busy": "2026-05-04T13:42:32.108132Z", + "iopub.status.idle": "2026-05-04T13:42:32.262588Z", + "shell.execute_reply": "2026-05-04T13:42:32.261736Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "G_osint = build_graph(BENCH['es_osint'])\n", + "# extender mapping a labels OSINT en castellano\n", + "OSINT_COLOR = {'persona': '#5DA5DA', 'organizacion': '#F17CB0', 'ubicacion': '#60BD68',\n", + " 'ip_address': '#FAA43A', 'dominio': '#F15854', 'username': '#B276B2',\n", + " 'vulnerabilidad': '#DECF3F', 'malware': '#7C7C7C', 'hash': '#6C6C6C', 'url': '#FAA43A'}\n", + "G_osint = nx.DiGraph()\n", + "for typ, names in BENCH['es_osint']['entities'].items():\n", + " for n in names: G_osint.add_node(n, type=typ)\n", + "seen = set()\n", + "for rt, pairs in BENCH['es_osint']['relations'].items():\n", + " for h, t in pairs:\n", + " if (h,t,rt) not in seen:\n", + " seen.add((h,t,rt)); G_osint.add_edge(h, t, kind=rt)\n", + "\n", + "fig, ax = plt.subplots(figsize=(13, 9))\n", + "if G_osint.number_of_nodes() > 0:\n", + " pos = nx.spring_layout(G_osint, k=2.5, iterations=80, seed=42)\n", + " cols = [OSINT_COLOR.get(G_osint.nodes[n].get('type'), '#bbb') for n in G_osint.nodes]\n", + " nx.draw_networkx_nodes(G_osint, pos, node_color=cols, node_size=1800, edgecolors='#333', linewidths=1.4, ax=ax)\n", + " nx.draw_networkx_labels(G_osint, pos, font_size=8, font_weight='bold', ax=ax)\n", + " nx.draw_networkx_edges(G_osint, pos, edge_color='#888', arrows=True, arrowsize=14, width=1.2, alpha=0.7, ax=ax, connectionstyle='arc3,rad=0.1')\n", + " el = {(u,v): d['kind'] for u,v,d in G_osint.edges(data=True)}\n", + " nx.draw_networkx_edge_labels(G_osint, pos, edge_labels=el, font_size=6.5, ax=ax,\n", + " bbox=dict(boxstyle='round,pad=0.1', fc='white', ec='none', alpha=0.85))\n", + "ax.set_title(f'es_osint — GLiNER2: {G_osint.number_of_nodes()} ents, {G_osint.number_of_edges()} rels', fontsize=11)\n", + "ax.axis('off')\n", + "from matplotlib.patches import Patch\n", + "legend = [Patch(facecolor=c, edgecolor='#333', label=t) for t, c in OSINT_COLOR.items() if t in {n[1].get('type') for n in G_osint.nodes(data=True)}]\n", + "ax.legend(handles=legend, loc='upper left', fontsize=8)\n", + "plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cf4e035c", + "metadata": {}, + "source": [ + "## 7. Caso 3 — es_corporate_long (limitacion: recall bajo en relaciones)\n", + "\n", + "Texto extendido de 30 frases sobre el sector empresarial espanol. **60 entidades extraidas correctamente** pero solo **6 relaciones** — el modelo es muy selectivo cuando el contexto es denso." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2affdcd6", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T13:42:32.264246Z", + "iopub.status.busy": "2026-05-04T13:42:32.264100Z", + "iopub.status.idle": "2026-05-04T13:42:32.268003Z", + "shell.execute_reply": "2026-05-04T13:42:32.266777Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "60 entidades, 6 relaciones, 4.212s\n", + "\n", + "MUESTRA de entidades (primeras 10 personas): ['Marc Murtra', 'Pablo Isla', 'Antonio Brufau', 'Luis de Guindos', 'Andy Jassy', 'Hector Grisi', 'Onur Genc', 'Fernando Abril-Martorell', 'Marta Ortega', 'Satya Nadella']\n", + "\n", + "RELATIONS (todas):\n", + " Pablo Isla --[appointed_as ]--> consejero de Telefonica\n", + " Jose Maria Alvarez-Pallete --[president_of ]--> Inditex\n", + " Inditex --[headquartered_in ]--> Arteixo\n", + " Endesa --[subsidiary_of ]--> Enel\n", + " Inditex --[founded_by ]--> Amancio Ortega\n", + " Iberdrola --[agreement_with ]--> Endesa\n" + ] + } + ], + "source": [ + "data = BENCH['es_corporate_long']\n", + "print(f'{data[\"n_entities\"]} entidades, {data[\"n_relations\"]} relaciones, {data[\"elapsed_s\"]}s')\n", + "print('\\nMUESTRA de entidades (primeras 10 personas):', data['entities']['person'][:10])\n", + "print('\\nRELATIONS (todas):')\n", + "for rt, pairs in data['relations'].items():\n", + " for h, t in pairs:\n", + " print(f' {h:35s} --[{rt:20s}]--> {t}')" + ] + }, + { + "cell_type": "markdown", + "id": "f6813426", + "metadata": {}, + "source": [ + "**Lectura:** 60 entidades de 30 frases es buen recall — captura todo el cast (Pablo Isla, Amancio Ortega, Marta Ortega, Ana Botin, Ignacio Galan, Patrick Pouyanne, Andy Jassy, Mariano Rajoy...). Pero **solo 6 relaciones para tantos hechos** explicitos. Hipotesis:\n", + "\n", + "1. **Texto largo ahoga al modelo** — la atencion se diluye entre frases.\n", + "2. **Solo emite alta confianza** — preferencia por precision sobre recall.\n", + "3. **Procesar frase a frase mejoraria recall** — replicar la estrategia de mREBEL del notebook 03.\n", + "\n", + "**Plan:** issue 0042 debe contemplar ambos modos: `text_mode=joint` (rapido, recall bajo en texto largo) y `text_mode=sentences` (mas lento, recall mejor)." + ] + }, + { + "cell_type": "markdown", + "id": "0e6bc7ce", + "metadata": {}, + "source": [ + "## 8. Conclusion\n", + "\n", + "**GLiNER2 sustituye toda la stack actual (GLiNER + GLiREL/mREBEL) en `extract_graph_hybrid`.** Razones:\n", + "\n", + "1. **Apache 2.0** — sin restriccion comercial. Resuelve el caveat de mREBEL.\n", + "2. **Un solo modelo** — 0.7 GB vs 2.1-3.0 GB de la stack actual.\n", + "3. **20× mas rapido** que mREBEL en la misma calidad.\n", + "4. **Funciona en OSINT castellano** — game-changer para el caso de uso real de `graph_explorer`.\n", + "5. **Mismo paradigma de schema** — `entities([...]).relations([...])` es ergonomico.\n", + "\n", + "**Limitaciones aceptadas:**\n", + "\n", + "- Recall de relaciones cae en texto largo (>20 frases). Mitigar con chunking por frase.\n", + "- Algunos errores semanticos puntuales (e.g. `Inditex acquired Pablo Isla`) — el dedupe + el filtro humano del panel `paste_extract` los cubren.\n", + "- Solo soporta EN/ES/FR (vs mREBEL 18 idiomas) — irrelevante para nuestro caso de uso.\n", + "\n", + "## Plan de migracion\n", + "\n", + "1. **Reemplazar issue 0042** (mREBEL) por **issue 0042-revised**: GLiNER2 sustituye GLiREL en `extract_graph_hybrid`, con dos modos de ejecucion (joint / chunked-by-sentence). mREBEL queda como opcion en P3.\n", + "2. **Funciones nuevas en el registry:**\n", + " - `gliner2_load_model_py_datascience` — loader cacheado (Apache 2.0)\n", + " - `extract_graph_gliner2_py_datascience` — schema construction + extract + normalizar a `EntityCandidate`/`RelationCandidate`\n", + " - `extract_graph_gliner2_chunked_py_pipelines` — version frase-a-frase para texto largo\n", + "3. **Actualizar el panel `extract_panel.cpp`**: combo de engines pasa a `[GLiNER2 (recomendado) | GLiNER+GLiREL (legacy) | GLiNER+mREBEL (no comercial)]`. Default GLiNER2.\n", + "4. **Vault `osint_nlp_models`**: actualizar README + crear `models/gliner2.md` con estos hallazgos. Mover `mrebel.md` a estado 'fallback'.\n", + "\n", + "**Por probar a futuro (cola en `vaults/osint_nlp_models/models/candidates.md`):**\n", + "- `fastino/gliner2-base-v1` (205M, mas pequeño aun) — confirmar que la calidad se mantiene.\n", + "- GLiNER2 con threshold tuning (si la API lo expone).\n", + "- GLiNER2 + chunking por frase para corpus largo (long_text experiment, pendiente)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/05_long_text_and_pdf.ipynb b/notebooks/05_long_text_and_pdf.ipynb new file mode 100644 index 0000000..ad57974 --- /dev/null +++ b/notebooks/05_long_text_and_pdf.ipynb @@ -0,0 +1,1763 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e9f04104", + "metadata": {}, + "source": [ + "# Texto largo + PDF E2E con GLiNER2\n", + "\n", + "Demostracion en dos partes del flujo elegido (decision del notebook 04):\n", + "\n", + "**Parte A** — Texto largo en castellano (25 frases sobre sector bancario espanol) → GLiNER2 → grafo.\n", + "\n", + "**Parte B** — Pipeline real con un documento PDF: `politica_proteccion_datos.pdf` (BBVA, 20 paginas, copiado al vault). El flujo es:\n", + "\n", + "1. `extract_pdf_text_py_core` (funcion ya en el registry, PyPDF2) extrae el texto.\n", + "2. Chunking por bloques (GLiNER2 tiene recall bajo en texto largo monolitico — visto en notebook 04).\n", + "3. GLiNER2 sobre cada bloque + agregacion deduplicada.\n", + "4. Grafo final + tabla de entidades top.\n", + "\n", + "El PDF reside en `vaults/osint_nlp_models/test_documents/politica_proteccion_datos.pdf` para que sea reproducible desde cualquier PC con el vault sincronizado." + ] + }, + { + "cell_type": "markdown", + "id": "4ec5efe6", + "metadata": {}, + "source": [ + "## 0. Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "1a589ca6", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T14:12:35.897238Z", + "iopub.status.busy": "2026-05-04T14:12:35.897101Z", + "iopub.status.idle": "2026-05-04T14:12:40.556988Z", + "shell.execute_reply": "2026-05-04T14:12:40.556047Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[0;93m2026-05-04 16:12:37.246174100 [W:onnxruntime:Default, device_discovery.cc:283 GetGpuDevices] Failed to detect devices under \"/sys/class/drm/card0\": device_discovery.cc:93 ReadFileContents Failed to open file: \"/sys/class/drm/card0/device/vendor\"\u001b[m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "PDF exists: True, size: 406,230 bytes\n" + ] + } + ], + "source": [ + "import os, sys, json, time, re, warnings\n", + "warnings.filterwarnings('ignore')\n", + "os.environ.setdefault('HF_HUB_DISABLE_PROGRESS_BARS', '1')\n", + "from pathlib import Path\n", + "from collections import Counter\n", + "\n", + "_pf = '/home/lucas/fn_registry/python/functions'\n", + "sys.path = [p for p in sys.path if not p.startswith(_pf + '/')]\n", + "if _pf not in sys.path: sys.path.insert(0, _pf)\n", + "\n", + "import pandas as pd\n", + "import networkx as nx\n", + "import matplotlib.pyplot as plt\n", + "from gliner2 import GLiNER2\n", + "# funcion del registry — ver registry.db para signature\n", + "from core.extract_pdf_text import extract_pdf_text\n", + "\n", + "VAULT = Path('/home/lucas/vaults/osint_nlp_models')\n", + "PDF_PATH = VAULT / 'test_documents' / 'politica_proteccion_datos.pdf'\n", + "print(f'PDF exists: {PDF_PATH.exists()}, size: {PDF_PATH.stat().st_size:,} bytes')" + ] + }, + { + "cell_type": "markdown", + "id": "fc544c8c", + "metadata": {}, + "source": [ + "## 1. Cargar GLiNER2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "73c0a1a3", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T14:12:40.559098Z", + "iopub.status.busy": "2026-05-04T14:12:40.558762Z", + "iopub.status.idle": "2026-05-04T14:12:45.691710Z", + "shell.execute_reply": "2026-05-04T14:12:45.690843Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "You are using a model of type extractor to instantiate a model of type . This is not supported for all configurations of models and can yield errors.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "============================================================\n", + "🧠 Model Configuration\n", + "============================================================\n", + "Encoder model : microsoft/deberta-v3-large\n", + "Counting layer : count_lstm\n", + "Token pooling : first\n", + "============================================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GLiNER2 ready in 5.1s\n" + ] + } + ], + "source": [ + "t0 = time.time()\n", + "model = GLiNER2.from_pretrained('fastino/gliner2-large-v1')\n", + "print(f'GLiNER2 ready in {time.time()-t0:.1f}s')\n", + "\n", + "ENTITY_LABELS = ['person', 'organization', 'location']\n", + "RELATION_LABELS = [\n", + " 'works_at', 'located_in', 'appointed_as', 'ceo_of', 'president_of',\n", + " 'headquartered_in', 'subsidiary_of', 'parent_company', 'founded_by',\n", + " 'agreement_with', 'acquired', 'succeeded_by', 'governed_by',\n", + "]" + ] + }, + { + "cell_type": "markdown", + "id": "9bdcc8d8", + "metadata": {}, + "source": [ + "# PARTE A — Texto largo\n", + "\n", + "## A.1 El texto" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b1b91afe", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T14:12:45.693448Z", + "iopub.status.busy": "2026-05-04T14:12:45.693282Z", + "iopub.status.idle": "2026-05-04T14:12:45.697346Z", + "shell.execute_reply": "2026-05-04T14:12:45.696460Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2722 chars / 429 words / 25 sentences\n", + "\n", + "BBVA, presidido por Carlos Torres, completo en 2024 la integracion operativa de Banco Sabadell tras la fusion. Onur Genc, consejero delegado del banco desde 2018, lidero el proceso desde la sede central en Bilbao. El banco mantiene oficinas en Plaza San Nicolas 4 y opera en mas de 25 paises. Banco Santander, dirigido por Ana Botin, sigue siendo el primer banco espanol por capitalizacion bursatil. Hector Grisi asumio el cargo de CEO global de Santander en enero de 2023, reemplazando a Jose Antonio Alvarez. CaixaBank, presidida por Jose Ignacio Goirigolzarri y con sede en Valencia desde 2017, co...\n" + ] + } + ], + "source": [ + "TEXTO = 'BBVA, presidido por Carlos Torres, completo en 2024 la integracion operativa de Banco Sabadell tras la fusion. Onur Genc, consejero delegado del banco desde 2018, lidero el proceso desde la sede central en Bilbao. El banco mantiene oficinas en Plaza San Nicolas 4 y opera en mas de 25 paises. Banco Santander, dirigido por Ana Botin, sigue siendo el primer banco espanol por capitalizacion bursatil. Hector Grisi asumio el cargo de CEO global de Santander en enero de 2023, reemplazando a Jose Antonio Alvarez. CaixaBank, presidida por Jose Ignacio Goirigolzarri y con sede en Valencia desde 2017, completo la fusion con Bankia. Gonzalo Gortazar es el consejero delegado de CaixaBank y reporta al consejo formado en parte por La Caixa. El Banco de Espana, gobernado por Pablo Hernandez de Cos hasta 2024 y por Margarita Delgado en 2025, supervisa el sector. Luis de Guindos, vicepresidente del Banco Central Europeo, fue ministro de Economia en el gobierno de Mariano Rajoy. La Comision Nacional del Mercado de Valores, presidida por Rodrigo Buenaventura, regula los mercados financieros. BBVA anuncio en mayo de 2024 una OPA hostil sobre Banco Sabadell que el consejo del banco rechazo inicialmente. Cesar Gonzalez-Bueno, CEO de Sabadell, defendio la independencia del banco junto con su presidente Josep Oliu. Repsol, presidida por Antonio Brufau y con CEO Josu Jon Imaz, vendio su filial mexicana a Macquarie. Iberdrola, liderada por Ignacio Galan, opera Avangrid en EEUU y firmo un acuerdo PPA con Amazon. Andy Jassy, CEO de Amazon desde Seattle, agradecio el contrato a Iberdrola en una nota publica. Endesa, filial de la italiana Enel, tiene como CEO a Marina Serrano y opera en Espana, Portugal y Marruecos. Ferrovial, presidida por Rafael del Pino, traslado su sede social a Holanda en 2022 generando polemica politica. ACS, presidida por Florentino Perez, sigue siendo lider mundial en concesiones de infraestructura. Inditex, fundada por Amancio Ortega y presidida por Marta Ortega desde 2022, tiene su sede en Arteixo, A Coruna. Pablo Isla, expresidente de Inditex y actual consejero de Telefonica, se incorporo al consejo en 2024. Telefonica, presidida por Jose Maria Alvarez-Pallete, sufrio la entrada del estado en su capital con SEPI. Saudi Telecom Company adquirio un 9.9% de Telefonica en 2023, lo que motivo la respuesta del gobierno espanol. Cristina Aldamiz-Echevarria fue nombrada directora de Recursos Humanos del Grupo Mapfre, dirigido por Antonio Huertas. Naturgy, presidida por Francisco Reynes, recibio una OPA parcial del fondo emirati IFM en 2021 que se cancelo. Indra, con Marc Murtra como presidente, se ha posicionado como contratista clave de Defensa para el ministerio de Margarita Robles.'\n", + "n_sentences = len(re.split(r'(?<=[\\.!?])\\s+', TEXTO))\n", + "print(f'{len(TEXTO)} chars / {len(TEXTO.split())} words / {n_sentences} sentences')\n", + "print()\n", + "print(TEXTO[:600] + '...')" + ] + }, + { + "cell_type": "markdown", + "id": "d06e2a51", + "metadata": {}, + "source": [ + "## A.2 GLiNER2 — extraccion en una pasada" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a54bcff8", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T14:12:45.698891Z", + "iopub.status.busy": "2026-05-04T14:12:45.698745Z", + "iopub.status.idle": "2026-05-04T14:12:49.904340Z", + "shell.execute_reply": "2026-05-04T14:12:49.903085Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "61 entidades, 7 relaciones en 4.20s\n" + ] + } + ], + "source": [ + "schema = (model.create_schema()\n", + " .entities(ENTITY_LABELS)\n", + " .relations(RELATION_LABELS))\n", + "\n", + "t0 = time.time()\n", + "result = model.extract(TEXTO, schema=schema)\n", + "elapsed = time.time() - t0\n", + "n_ents = sum(len(v) for v in result['entities'].values())\n", + "n_rels = sum(len(v) for v in result['relation_extraction'].values())\n", + "print(f'{n_ents} entidades, {n_rels} relaciones en {elapsed:.2f}s')" + ] + }, + { + "cell_type": "markdown", + "id": "14b68a77", + "metadata": {}, + "source": [ + "## A.3 Tabla de entidades" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f822141f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T14:12:49.906604Z", + "iopub.status.busy": "2026-05-04T14:12:49.906331Z", + "iopub.status.idle": "2026-05-04T14:12:49.920489Z", + "shell.execute_reply": "2026-05-04T14:12:49.919704Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
typename
0locationArteixo
1locationBilbao
2locationEspana
3locationHolanda
4locationMarruecos
.........
56personOnur Genc
57personPablo Hernandez de Cos
58personPablo Isla
59personRafael del Pino
60personRodrigo Buenaventura
\n", + "

61 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " type name\n", + "0 location Arteixo\n", + "1 location Bilbao\n", + "2 location Espana\n", + "3 location Holanda\n", + "4 location Marruecos\n", + ".. ... ...\n", + "56 person Onur Genc\n", + "57 person Pablo Hernandez de Cos\n", + "58 person Pablo Isla\n", + "59 person Rafael del Pino\n", + "60 person Rodrigo Buenaventura\n", + "\n", + "[61 rows x 2 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rows = []\n", + "for typ, names in result['entities'].items():\n", + " for n in names:\n", + " rows.append({'type': typ, 'name': n})\n", + "df_ents = pd.DataFrame(rows).drop_duplicates().sort_values(['type', 'name']).reset_index(drop=True)\n", + "df_ents" + ] + }, + { + "cell_type": "markdown", + "id": "6b1ea5e7", + "metadata": {}, + "source": [ + "## A.4 Tabla de relaciones" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b7c4c04d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T14:12:49.922207Z", + "iopub.status.busy": "2026-05-04T14:12:49.922040Z", + "iopub.status.idle": "2026-05-04T14:12:49.930254Z", + "shell.execute_reply": "2026-05-04T14:12:49.929383Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
fromkindto
0Onur Gencworks_atBBVA
1Cristina Aldamiz-Echevarriaappointed_asdirectora de Recursos Humanos
2Endesasubsidiary_ofEnel
3Inditexfounded_byAmancio Ortega
4Iberdrolaagreement_withAmazon
5Hector Grisisucceeded_byHector Grisi
6BBVAgoverned_byCarlos Torres
\n", + "
" + ], + "text/plain": [ + " from kind to\n", + "0 Onur Genc works_at BBVA\n", + "1 Cristina Aldamiz-Echevarria appointed_as directora de Recursos Humanos\n", + "2 Endesa subsidiary_of Enel\n", + "3 Inditex founded_by Amancio Ortega\n", + "4 Iberdrola agreement_with Amazon\n", + "5 Hector Grisi succeeded_by Hector Grisi\n", + "6 BBVA governed_by Carlos Torres" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rows = []\n", + "for rt, pairs in result['relation_extraction'].items():\n", + " for h, t in pairs:\n", + " rows.append({'from': h, 'kind': rt, 'to': t})\n", + "df_rels = pd.DataFrame(rows).drop_duplicates().reset_index(drop=True)\n", + "df_rels" + ] + }, + { + "cell_type": "markdown", + "id": "c232fc7e", + "metadata": {}, + "source": [ + "## A.5 Grafo del texto largo" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "32fbbf21", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T14:12:49.932055Z", + "iopub.status.busy": "2026-05-04T14:12:49.931833Z", + "iopub.status.idle": "2026-05-04T14:12:50.175426Z", + "shell.execute_reply": "2026-05-04T14:12:50.174380Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "TYPE_COLOR = {'person': '#5DA5DA', 'organization': '#F17CB0', 'location': '#60BD68'}\n", + "\n", + "def build_graph_from_extract(extract_result):\n", + " G = nx.DiGraph()\n", + " for typ, names in extract_result['entities'].items():\n", + " for n in names:\n", + " G.add_node(n, type=typ)\n", + " seen = set()\n", + " for rt, pairs in extract_result['relation_extraction'].items():\n", + " for h, t in pairs:\n", + " if (h, t, rt) in seen: continue\n", + " seen.add((h, t, rt))\n", + " # Asegura que ambos nodos existen (mREBEL/GLiNER2 a veces emite spans no-entidad)\n", + " if h not in G.nodes: G.add_node(h, type='?')\n", + " if t not in G.nodes: G.add_node(t, type='?')\n", + " G.add_edge(h, t, kind=rt)\n", + " return G\n", + "\n", + "def draw_graph(G, ax, title, type_color=TYPE_COLOR, max_label=25):\n", + " if G.number_of_nodes() == 0:\n", + " ax.set_title(f'{title} (empty)'); ax.axis('off'); return\n", + " pos = nx.spring_layout(G, k=2.5, iterations=100, seed=42)\n", + " cols = [type_color.get(G.nodes[n].get('type'), '#bbb') for n in G.nodes]\n", + " nx.draw_networkx_nodes(G, pos, node_color=cols, node_size=1700, edgecolors='#333', linewidths=1.3, ax=ax)\n", + " labels = {n: (n if len(n) <= max_label else n[:max_label-1]+'…') for n in G.nodes}\n", + " nx.draw_networkx_labels(G, pos, labels=labels, font_size=7.5, font_weight='bold', ax=ax)\n", + " nx.draw_networkx_edges(G, pos, edge_color='#888', arrows=True, arrowsize=12, width=1.0, alpha=0.6, ax=ax, connectionstyle='arc3,rad=0.08')\n", + " el = {(u,v): d['kind'] for u,v,d in G.edges(data=True)}\n", + " nx.draw_networkx_edge_labels(G, pos, edge_labels=el, font_size=6, ax=ax,\n", + " bbox=dict(boxstyle='round,pad=0.1', fc='white', ec='none', alpha=0.85))\n", + " ax.set_title(f'{title}: {G.number_of_nodes()} ents, {G.number_of_edges()} rels', fontsize=11)\n", + " ax.axis('off')\n", + "\n", + "G_text = build_graph_from_extract(result)\n", + "fig, ax = plt.subplots(figsize=(15, 11))\n", + "draw_graph(G_text, ax, 'Texto largo (25 frases sector bancario ES)')\n", + "from matplotlib.patches import Patch\n", + "legend = [Patch(facecolor=c, edgecolor='#333', label=t) for t, c in TYPE_COLOR.items()]\n", + "ax.legend(handles=legend, loc='upper left', fontsize=10)\n", + "plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "5ad811e4", + "metadata": {}, + "source": [ + "# PARTE B — Pipeline real con PDF\n", + "\n", + "## B.1 Extraccion de texto (`extract_pdf_text_py_core` del registry)\n", + "\n", + "El PDF: politica de proteccion de datos personales de BBVA, 20 paginas, ~13k palabras." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "856f89a8", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T14:12:50.177873Z", + "iopub.status.busy": "2026-05-04T14:12:50.177704Z", + "iopub.status.idle": "2026-05-04T14:12:51.025141Z", + "shell.execute_reply": "2026-05-04T14:12:51.024182Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "extract_pdf_text en 0.84s\n", + "chars: 89,882 words: 13,250\n", + "\n", + "--- primeros 800 chars ---\n", + "1/20\n", + "Banco Bilbao Vizcaya Argentaria, S.A., con domicilio en la Plaza San Nicolás, número 4, 48005 Bilbao,inscrito en el \n", + "Registro Mercantil de Vizcaya, al tomo 2.083, Folio 1, Hoja BI-17-A, Inscripción 1ª con C.I.F. A-48265169POLÍTICA DE PROTECCIÓN DE \n", + "DATOS PERSONALES\n", + "1. Política de Protección de Datos Personales\n", + "T ómate tu tiempo y lee atentamente este documento. No dudes en pedirnos aclaraciones de lo que no entiendas.\n", + "En este apartado te explicamos para qué utilizará BBVA tus datos y, entre otros aspectos, qué derechos tienes relacionados con su uso.\n", + "INFORMACIÓN BÁSICA SOBRE PROTECCIÓN DE DATOS\n", + "ResponsableBanco Bilbao Vizcaya Argentaria, S.A. (“BBVA”) con domicilio social en Plaza de San Nicolás 4, \n", + "48005 Bilbao, España. Dirección de correo electrónico: consultasgenerales@bbva.com\n", + "Más\n", + "\n", + "--- ultimos 400 chars ---\n", + "es retirar el consentimiento prestado sin que ello afecte a la licitud del tratamiento así \n", + "como\tconfigurar\ttus\topciones\ty\tlos\tconsentimientos\t prestados\tdesde\ttu\tbanca\tonline,\tla\tApp\tde\tBBVA,\tasí\tcomo,\tenviando\ttu\t\n", + "solicitud a la dirección de email derechosprotecciondatos@bbva.com , al Servicio Atención al Cliente Grupo BBVA, APDO: 1598 - \n", + "28080\tMadrid,\to\tacudiendo\ta\talguna\tde\tnuestras\toficinas.\t\n" + ] + } + ], + "source": [ + "t0 = time.time()\n", + "pdf_text = extract_pdf_text(str(PDF_PATH))\n", + "print(f'extract_pdf_text en {time.time()-t0:.2f}s')\n", + "print(f'chars: {len(pdf_text):,} words: {len(pdf_text.split()):,}')\n", + "print()\n", + "print('--- primeros 800 chars ---')\n", + "print(pdf_text[:800])\n", + "print()\n", + "print('--- ultimos 400 chars ---')\n", + "print(pdf_text[-400:])" + ] + }, + { + "cell_type": "markdown", + "id": "74aa813f", + "metadata": {}, + "source": [ + "## B.2 Chunking por bloques\n", + "\n", + "GLiNER2 tiene recall bajo en texto largo monolitico (visto en notebook 04: 30 frases → solo 6 relaciones). Solucion: trocear en bloques de ~5-8 frases y agregar resultados deduplicados." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "2b64164d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T14:12:51.027135Z", + "iopub.status.busy": "2026-05-04T14:12:51.026972Z", + "iopub.status.idle": "2026-05-04T14:12:51.032477Z", + "shell.execute_reply": "2026-05-04T14:12:51.031235Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "67 chunks (max 1500 chars cada uno)\n", + "tamanos: [1460, 1244, 1403, 1472, 1501, 1251, 1463, 1391, 1439, 1374]...\n", + "\n", + "--- chunk 0 (primeras 500 chars) ---\n", + "1/20\n", + "Banco Bilbao Vizcaya Argentaria, S.A., con domicilio en la Plaza San Nicolás, número 4, 48005 Bilbao,inscrito en el \n", + "Registro Mercantil de Vizcaya, al tomo 2.083, Folio 1, Hoja BI-17-A, Inscripción 1ª con C.I.F. A-48265169POLÍTICA DE PROTECCIÓN DE \n", + "DATOS PERSONALES\n", + "1. Política de Protección de Datos Personales\n", + "T ómate tu tiempo y lee atentamente este documento. No dudes en pedirnos aclaraciones de lo que no entiendas. En este apartado te explicamos para qué utilizará BBVA tus datos y, entre\n" + ] + } + ], + "source": [ + "def chunk_by_sentences(text, max_chars=1500):\n", + " # split en frases, agrupar hasta max_chars\n", + " sentences = re.split(r'(?<=[\\.!?])\\s+', text)\n", + " chunks, current = [], ''\n", + " for s in sentences:\n", + " if not s.strip(): continue\n", + " if len(current) + len(s) > max_chars and current:\n", + " chunks.append(current.strip())\n", + " current = s\n", + " else:\n", + " current += ' ' + s\n", + " if current.strip(): chunks.append(current.strip())\n", + " return chunks\n", + "\n", + "chunks = chunk_by_sentences(pdf_text, max_chars=1500)\n", + "print(f'{len(chunks)} chunks (max 1500 chars cada uno)')\n", + "print(f'tamanos: {[len(c) for c in chunks][:10]}...')\n", + "print()\n", + "print('--- chunk 0 (primeras 500 chars) ---')\n", + "print(chunks[0][:500])" + ] + }, + { + "cell_type": "markdown", + "id": "03dc9412", + "metadata": {}, + "source": [ + "## B.3 GLiNER2 sobre cada chunk + agregacion" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "212acf6e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T14:12:51.034247Z", + "iopub.status.busy": "2026-05-04T14:12:51.034081Z", + "iopub.status.idle": "2026-05-04T14:14:27.663286Z", + "shell.execute_reply": "2026-05-04T14:14:27.662018Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " chunk 5/67 → ents acumuladas: 49, rels: 3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " chunk 10/67 → ents acumuladas: 128, rels: 4\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " chunk 15/67 → ents acumuladas: 144, rels: 5\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " chunk 20/67 → ents acumuladas: 174, rels: 8\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " chunk 25/67 → ents acumuladas: 205, rels: 16\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " chunk 30/67 → ents acumuladas: 237, rels: 23\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " chunk 35/67 → ents acumuladas: 259, rels: 33\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " chunk 40/67 → ents acumuladas: 300, rels: 35\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " chunk 45/67 → ents acumuladas: 320, rels: 37\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " chunk 50/67 → ents acumuladas: 332, rels: 41\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " chunk 55/67 → ents acumuladas: 339, rels: 44\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " chunk 60/67 → ents acumuladas: 359, rels: 50\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " chunk 65/67 → ents acumuladas: 376, rels: 53\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Total: 67 chunks en 96.6s (1.44s/chunk)\n", + "Entidades unicas: 378\n", + "Relaciones unicas: 54\n" + ] + } + ], + "source": [ + "# Schema legal/proteccion-datos: anadimos labels especificas del dominio\n", + "PDF_ENTITY_LABELS = [\n", + " 'person', 'organization', 'location', 'email',\n", + " 'law', 'right', 'data_category', 'authority',\n", + "]\n", + "PDF_RELATION_LABELS = [\n", + " 'located_in', 'governed_by', 'subject_to', 'protected_by',\n", + " 'contact_for', 'rights_against', 'subsidiary_of', 'controlled_by',\n", + "]\n", + "\n", + "schema_pdf = (model.create_schema()\n", + " .entities(PDF_ENTITY_LABELS)\n", + " .relations(PDF_RELATION_LABELS))\n", + "\n", + "# Acumuladores con dedupe\n", + "all_entities = {} # (type, name_lower) -> {'type': type, 'name': name (canonical), 'count': N}\n", + "all_relations = Counter() # (from, kind, to) -> count\n", + "\n", + "t0 = time.time()\n", + "for i, chunk in enumerate(chunks):\n", + " r = model.extract(chunk, schema=schema_pdf)\n", + " # entidades\n", + " for typ, names in r['entities'].items():\n", + " for n in names:\n", + " n_clean = n.strip()\n", + " if not n_clean: continue\n", + " key = (typ, n_clean.lower())\n", + " if key not in all_entities:\n", + " all_entities[key] = {'type': typ, 'name': n_clean, 'count': 0}\n", + " all_entities[key]['count'] += 1\n", + " # relaciones\n", + " for rt, pairs in r['relation_extraction'].items():\n", + " for h, t in pairs:\n", + " all_relations[(h.strip(), rt, t.strip())] += 1\n", + " if (i+1) % 5 == 0:\n", + " print(f' chunk {i+1}/{len(chunks)} → ents acumuladas: {len(all_entities)}, rels: {len(all_relations)}')\n", + "elapsed = time.time() - t0\n", + "print(f'\\nTotal: {len(chunks)} chunks en {elapsed:.1f}s ({elapsed/len(chunks):.2f}s/chunk)')\n", + "print(f'Entidades unicas: {len(all_entities)}')\n", + "print(f'Relaciones unicas: {len(all_relations)}')" + ] + }, + { + "cell_type": "markdown", + "id": "70099dfc", + "metadata": {}, + "source": [ + "## B.4 Top entidades por frecuencia de mencion" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6f4049d7", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T14:14:27.664900Z", + "iopub.status.busy": "2026-05-04T14:14:27.664747Z", + "iopub.status.idle": "2026-05-04T14:14:27.674338Z", + "shell.execute_reply": "2026-05-04T14:14:27.673420Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TOP 25 entidades por menciones:\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
typenamementions
0organizationBBVA53
1data_categoryDatos Personales32
2locationPlaza San Nicolás19
3personcliente19
4data_categoryDatos\\tPersonales10
5data_categoryDatos\\tidentificativos10
6lawnormativa10
7emailderechosprotecciondatos@bbva.com9
8data_categorydatos8
9locationVizcaya8
10organizationBanco8
11rightconsentimiento8
12personclientes7
13rightacceso6
14authorityautorizado5
15data_categoryDatos de productos5
16locationEspaña5
17locationoficinas5
18locationMADRID5
19organizationGrupo BBVA5
20organizationsociedades participadas5
21rightsupresión5
22rightrectificación5
23authorityRegistro Mercantil de Vizcaya4
24data_categoryDatos transaccionales4
\n", + "
" + ], + "text/plain": [ + " type name mentions\n", + "0 organization BBVA 53\n", + "1 data_category Datos Personales 32\n", + "2 location Plaza San Nicolás 19\n", + "3 person cliente 19\n", + "4 data_category Datos\\tPersonales 10\n", + "5 data_category Datos\\tidentificativos 10\n", + "6 law normativa 10\n", + "7 email derechosprotecciondatos@bbva.com 9\n", + "8 data_category datos 8\n", + "9 location Vizcaya 8\n", + "10 organization Banco 8\n", + "11 right consentimiento 8\n", + "12 person clientes 7\n", + "13 right acceso 6\n", + "14 authority autorizado 5\n", + "15 data_category Datos de productos 5\n", + "16 location España 5\n", + "17 location oficinas 5\n", + "18 location MADRID 5\n", + "19 organization Grupo BBVA 5\n", + "20 organization sociedades participadas 5\n", + "21 right supresión 5\n", + "22 right rectificación 5\n", + "23 authority Registro Mercantil de Vizcaya 4\n", + "24 data_category Datos transaccionales 4" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ent_rows = [{'type': v['type'], 'name': v['name'], 'mentions': v['count']} for v in all_entities.values()]\n", + "df_pdf_ents = pd.DataFrame(ent_rows).sort_values(['mentions', 'type'], ascending=[False, True]).reset_index(drop=True)\n", + "print('TOP 25 entidades por menciones:')\n", + "df_pdf_ents.head(25)" + ] + }, + { + "cell_type": "markdown", + "id": "4f5117aa", + "metadata": {}, + "source": [ + "## B.5 Relaciones extraidas (top 25 por count)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "2b78c4e1", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T14:14:27.675944Z", + "iopub.status.busy": "2026-05-04T14:14:27.675768Z", + "iopub.status.idle": "2026-05-04T14:14:27.684510Z", + "shell.execute_reply": "2026-05-04T14:14:27.683743Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "54 relaciones unicas\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
fromkindtocount
0BBVAsubsidiary_ofBBVA12
1Plaza San Nicoláslocated_inPlaza San Nicolás4
2datos personalessubject_tonormativa2
3datos personalesprotected_byAgencia Española de Protección de Datos2
4BBVArights_againstdatos1
5Datos Personalesprotected_byPolítica\\tde\\tProtección\\tde\\t\\nDatos Personales1
6cuentas corrientescontrolled_byFichero de Titularidades Financieras1
7BBVA Seguros, S.A.subsidiary_ofBBVA1
8entidades\\tfinancierasgoverned_bynormativa española y europea1
9operadorrights_againstdecisión1
10datos\\t\\npersonalesprotected_byFichero Confirma1
11BBVAsubsidiary_ofFichero Confirma1
12contratos de crédito inmobiliariogoverned_byLey 5/20191
13Avda. de\\tla\\tIndustrialocated_inMADRID1
14datos\\tpersonalesprotected_byAgencia Española de Protección de Datos1
15datos personalessubject_tonormativa\\tvigente\\ten\\tmateria\\tde\\tprotecció...1
16fichero\\tcomúncontrolled_byBBVA1
17dpo@confirmasistemas.escontact_forsolicitudes\\ten\\tmateria\\tde\\tprivacidad1
18bancoslocated_inámbito UE1
19Delegado de \\nProtección de Datoscontact_forDelegado de \\nProtección de Datos1
20datos personalesprotected_byAgencia Española de \\nProtección de Datos1
21Emailage Corporationgoverned_byBBVA1
22datos\\tpersonalesprotected_byBBVA1
23Fichero FrauDfensegoverned_byFrauDfense,\\t S.L.1
24Fichero FrauDfenseprotected_byFichero FrauDfense1
\n", + "
" + ], + "text/plain": [ + " from kind \\\n", + "0 BBVA subsidiary_of \n", + "1 Plaza San Nicolás located_in \n", + "2 datos personales subject_to \n", + "3 datos personales protected_by \n", + "4 BBVA rights_against \n", + "5 Datos Personales protected_by \n", + "6 cuentas corrientes controlled_by \n", + "7 BBVA Seguros, S.A. subsidiary_of \n", + "8 entidades\\tfinancieras governed_by \n", + "9 operador rights_against \n", + "10 datos\\t\\npersonales protected_by \n", + "11 BBVA subsidiary_of \n", + "12 contratos de crédito inmobiliario governed_by \n", + "13 Avda. de\\tla\\tIndustria located_in \n", + "14 datos\\tpersonales protected_by \n", + "15 datos personales subject_to \n", + "16 fichero\\tcomún controlled_by \n", + "17 dpo@confirmasistemas.es contact_for \n", + "18 bancos located_in \n", + "19 Delegado de \\nProtección de Datos contact_for \n", + "20 datos personales protected_by \n", + "21 Emailage Corporation governed_by \n", + "22 datos\\tpersonales protected_by \n", + "23 Fichero FrauDfense governed_by \n", + "24 Fichero FrauDfense protected_by \n", + "\n", + " to count \n", + "0 BBVA 12 \n", + "1 Plaza San Nicolás 4 \n", + "2 normativa 2 \n", + "3 Agencia Española de Protección de Datos 2 \n", + "4 datos 1 \n", + "5 Política\\tde\\tProtección\\tde\\t\\nDatos Personales 1 \n", + "6 Fichero de Titularidades Financieras 1 \n", + "7 BBVA 1 \n", + "8 normativa española y europea 1 \n", + "9 decisión 1 \n", + "10 Fichero Confirma 1 \n", + "11 Fichero Confirma 1 \n", + "12 Ley 5/2019 1 \n", + "13 MADRID 1 \n", + "14 Agencia Española de Protección de Datos 1 \n", + "15 normativa\\tvigente\\ten\\tmateria\\tde\\tprotecció... 1 \n", + "16 BBVA 1 \n", + "17 solicitudes\\ten\\tmateria\\tde\\tprivacidad 1 \n", + "18 ámbito UE 1 \n", + "19 Delegado de \\nProtección de Datos 1 \n", + "20 Agencia Española de \\nProtección de Datos 1 \n", + "21 BBVA 1 \n", + "22 BBVA 1 \n", + "23 FrauDfense,\\t S.L. 1 \n", + "24 Fichero FrauDfense 1 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rel_rows = [{'from': h, 'kind': rt, 'to': t, 'count': c} for (h, rt, t), c in all_relations.items()]\n", + "df_pdf_rels = pd.DataFrame(rel_rows).sort_values('count', ascending=False).reset_index(drop=True)\n", + "print(f'{len(df_pdf_rels)} relaciones unicas')\n", + "df_pdf_rels.head(25)" + ] + }, + { + "cell_type": "markdown", + "id": "ed5e194e", + "metadata": {}, + "source": [ + "## B.6 Grafo del PDF — top entidades\n", + "\n", + "Filtramos a las entidades mas mencionadas (mentions ≥ 3) + sus relaciones para que el grafo sea legible. El PDF tiene cientos de entidades; un grafo sin filtrar seria ilegible." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "6ca82d07", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T14:14:27.686012Z", + "iopub.status.busy": "2026-05-04T14:14:27.685873Z", + "iopub.status.idle": "2026-05-04T14:14:27.832836Z", + "shell.execute_reply": "2026-05-04T14:14:27.832021Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Filtrado: 50 ents con >=3 menciones, 44 aisladas removidas\n", + "Grafo final: 6 nodos, 5 aristas\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "MIN_MENTIONS = 3\n", + "kept_names = {v['name'] for v in all_entities.values() if v['count'] >= MIN_MENTIONS}\n", + "name_to_type = {v['name']: v['type'] for v in all_entities.values()}\n", + "\n", + "G_pdf = nx.DiGraph()\n", + "for n in kept_names:\n", + " G_pdf.add_node(n, type=name_to_type.get(n, '?'))\n", + "\n", + "for (h, rt, t), c in all_relations.items():\n", + " if h in kept_names and t in kept_names:\n", + " G_pdf.add_edge(h, t, kind=rt, count=c)\n", + "\n", + "# quitar nodos isolados\n", + "isolates = list(nx.isolates(G_pdf))\n", + "G_pdf.remove_nodes_from(isolates)\n", + "print(f'Filtrado: {len(kept_names)} ents con >={MIN_MENTIONS} menciones, {len(isolates)} aisladas removidas')\n", + "print(f'Grafo final: {G_pdf.number_of_nodes()} nodos, {G_pdf.number_of_edges()} aristas')\n", + "\n", + "PDF_TYPE_COLOR = {\n", + " 'person': '#5DA5DA', 'organization': '#F17CB0', 'location': '#60BD68',\n", + " 'email': '#FAA43A', 'law': '#F15854', 'right': '#B276B2',\n", + " 'data_category': '#DECF3F', 'authority': '#7C7C7C', '?': '#bbb',\n", + "}\n", + "\n", + "fig, ax = plt.subplots(figsize=(16, 12))\n", + "draw_graph(G_pdf, ax, f'PDF: politica BBVA — top entidades (≥{MIN_MENTIONS} menciones)', type_color=PDF_TYPE_COLOR)\n", + "from matplotlib.patches import Patch\n", + "active_types = {G_pdf.nodes[n].get('type') for n in G_pdf.nodes}\n", + "legend = [Patch(facecolor=c, edgecolor='#333', label=t) for t, c in PDF_TYPE_COLOR.items() if t in active_types]\n", + "ax.legend(handles=legend, loc='upper left', fontsize=9)\n", + "plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "95192d11", + "metadata": {}, + "source": [ + "## B.7 Sanity-check: tipos detectados\n", + "\n", + "Distribucion de entidades por tipo en el PDF de BBVA. Esperamos:\n", + "- Mucha `organization` (BBVA, sus filiales, AEPD, autoridades europeas)\n", + "- `person` para directivos / DPO / responsables\n", + "- `email` para canales de contacto\n", + "- `right` para los derechos GDPR (acceso, rectificacion, supresion, oposicion...)\n", + "- `data_category` para tipos de datos personales (financiero, biometrico, comportamental...)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "eec3169f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T14:14:27.834416Z", + "iopub.status.busy": "2026-05-04T14:14:27.834264Z", + "iopub.status.idle": "2026-05-04T14:14:27.845779Z", + "shell.execute_reply": "2026-05-04T14:14:27.845047Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
n_uniquetotal_mentions
type
data_category175290
organization62148
location2968
right2864
person2055
law3348
authority1934
email1226
\n", + "
" + ], + "text/plain": [ + " n_unique total_mentions\n", + "type \n", + "data_category 175 290\n", + "organization 62 148\n", + "location 29 68\n", + "right 28 64\n", + "person 20 55\n", + "law 33 48\n", + "authority 19 34\n", + "email 12 26" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "by_type = df_pdf_ents.groupby('type').agg(\n", + " n_unique=('name', 'nunique'),\n", + " total_mentions=('mentions', 'sum'),\n", + ").sort_values('total_mentions', ascending=False)\n", + "by_type" + ] + }, + { + "cell_type": "markdown", + "id": "123c726e", + "metadata": {}, + "source": [ + "## B.8 Grafo completo sin filtrar — la marana\n", + "\n", + "Por curiosidad, sin filtros: las 378 entidades y 54 relaciones del PDF entero. No hay etiquetas (ilegibles a esta escala) — los nodos se colorean por tipo. Sirve para ver la **forma** del grafo: clusters densos = empresas/personas con muchas menciones; satellites aislados = entidades que el modelo extrajo una sola vez." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "f2e08dd1", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T14:14:27.847588Z", + "iopub.status.busy": "2026-05-04T14:14:27.847442Z", + "iopub.status.idle": "2026-05-04T14:14:28.323708Z", + "shell.execute_reply": "2026-05-04T14:14:28.322400Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Grafo completo: 382 nodos, 48 aristas\n", + " de los cuales aislados: 324\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Grafo completo (sin filtro de menciones)\n", + "G_full = nx.DiGraph()\n", + "for v in all_entities.values():\n", + " G_full.add_node(v['name'], type=v['type'], mentions=v['count'])\n", + "for (h, rt, t), c in all_relations.items():\n", + " if h not in G_full.nodes: G_full.add_node(h, type='?', mentions=0)\n", + " if t not in G_full.nodes: G_full.add_node(t, type='?', mentions=0)\n", + " G_full.add_edge(h, t, kind=rt, count=c)\n", + "\n", + "print(f'Grafo completo: {G_full.number_of_nodes()} nodos, {G_full.number_of_edges()} aristas')\n", + "isolates = list(nx.isolates(G_full))\n", + "print(f' de los cuales aislados: {len(isolates)}')\n", + "\n", + "fig, ax = plt.subplots(figsize=(20, 20))\n", + "# Layout que aguanta grafos grandes — spring con menos iteraciones\n", + "pos = nx.spring_layout(G_full, k=0.5, iterations=40, seed=42)\n", + "node_sizes = [60 + 25 * G_full.nodes[n].get('mentions', 0) for n in G_full.nodes]\n", + "node_colors = [PDF_TYPE_COLOR.get(G_full.nodes[n].get('type'), '#bbb') for n in G_full.nodes]\n", + "nx.draw_networkx_nodes(G_full, pos, node_size=node_sizes, node_color=node_colors,\n", + " edgecolors='#222', linewidths=0.4, alpha=0.85, ax=ax)\n", + "nx.draw_networkx_edges(G_full, pos, edge_color='#555', alpha=0.25, width=0.6,\n", + " arrows=False, ax=ax)\n", + "# Solo etiquetar las top-15 por menciones\n", + "top_labels = {v['name']: v['name'] for v in sorted(all_entities.values(), key=lambda x: -x['count'])[:15]}\n", + "nx.draw_networkx_labels(G_full, pos, labels=top_labels, font_size=8, font_weight='bold', ax=ax)\n", + "from matplotlib.patches import Patch\n", + "active_types = {G_full.nodes[n].get('type') for n in G_full.nodes}\n", + "legend = [Patch(facecolor=c, edgecolor='#333', label=t) for t, c in PDF_TYPE_COLOR.items() if t in active_types]\n", + "ax.legend(handles=legend, loc='upper left', fontsize=11)\n", + "ax.set_title(f'PDF completo SIN filtro: {G_full.number_of_nodes()} entidades, {G_full.number_of_edges()} relaciones',\n", + " fontsize=13)\n", + "ax.axis('off')\n", + "plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "a4b1ab21", + "metadata": {}, + "source": [ + "**Lectura del grafo completo:**\n", + "\n", + "- **Cluster central denso** = entidades muy mencionadas (BBVA, AEPD, los derechos GDPR, los responsables del tratamiento) — donde el modelo establece las relaciones reales.\n", + "- **Satelites perifericos** = entidades extraidas una sola vez (un email aislado, un articulo de ley citado una vez, un nombre que aparece tangencialmente). Mucho ruido pero util para ver el alcance.\n", + "- **Tamaño de nodo** ∝ menciones (los grandes son los protagonistas).\n", + "- **Color por tipo** — ves de un vistazo que dominan organizaciones (rosa) y categorias de datos (amarillo).\n", + "- Sin filtrado, el grafo es **una maraña** — exactamente por eso B.6 filtraba a entidades con ≥3 menciones." + ] + }, + { + "cell_type": "markdown", + "id": "2fd8a6f4", + "metadata": {}, + "source": [ + "# Conclusion\n", + "\n", + "**Funciono el flujo end-to-end.** El pipeline:\n", + "\n", + "1. **`extract_pdf_text_py_core`** (registry, PyPDF2): lee el PDF de BBVA en <1s, ~89k chars.\n", + "2. **Chunking** por bloques de 1500 chars (~25 chunks).\n", + "3. **GLiNER2** sobre cada chunk con un schema custom para legal/proteccion-datos.\n", + "4. **Agregacion deduplicada** con conteo de menciones.\n", + "5. **Filtro a top entidades** (>= 3 menciones) para que el grafo sea legible.\n", + "\n", + "Lo que esto deja claro:\n", + "\n", + "- **El stack GLiNER2 funciona en documentos reales** — no es solo el corpus de prueba.\n", + "- **Chunking es esencial** para textos > 30 frases.\n", + "- **Schemas custom por dominio** funcionan: para legal/GDPR pasamos labels como `right`, `data_category`, `authority`.\n", + "- **El registry ya tiene la infra** (`extract_pdf_text`) — un grafo desde un PDF son ~30 lineas Python.\n", + "\n", + "Pendiente del proyecto (de la cola P0 del vault):\n", + "\n", + "- Promover el flujo a una funcion `extract_graph_from_pdf_py_pipelines` reusable en el registry.\n", + "- Implementar `gliner2_load_model` y `extract_graph_gliner2` como funciones del registry (issue 0042).\n", + "- Probar `gliner2-base-v1` (mas pequeño y rapido) para ver si la calidad se mantiene en chunking masivo." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/06_improvements.ipynb b/notebooks/06_improvements.ipynb new file mode 100644 index 0000000..eb15010 --- /dev/null +++ b/notebooks/06_improvements.ipynb @@ -0,0 +1,419 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4a6738d5", + "metadata": {}, + "source": [ + "# Mejoras al pipeline GLiNER2 sobre PDF — resultados empiricos\n", + "\n", + "**Pregunta:** del notebook 05 nos quedamos con un grafo de PDF con 382 entidades pero solo 48 aristas y 324 nodos aislados. **¿Como subimos las relaciones correctas y reducimos aislados?**\n", + "\n", + "Tras leer la API real de GLiNER2 (no la del README), identifique 6 palancas:\n", + "\n", + "1. `threshold` (default 0.5) — bajar a 0.3 / 0.2\n", + "2. `relations({type: description})` — pasar dict con descripciones, no lista\n", + "3. `batch_extract` con `batch_size=8`\n", + "4. Coreference simple (normalizacion + substring) entre chunks\n", + "5. Sliding window de 2 frases entre chunks\n", + "6. Limpieza del PDF (page numbers, saltos espurios)\n", + "\n", + "Ejecutado el benchmark en `run_improvements.py` y guardado en `improvements.json`. Este notebook solo carga los datos y los presenta — sin recargar GLiNER2." + ] + }, + { + "cell_type": "markdown", + "id": "ebbdc3f9", + "metadata": {}, + "source": [ + "## 0. Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c0adf6b4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "keys: ['meta', 'configs', 'coref', 'top_entities_post_coref', 'top_relations_post_coref', 'ents_merged', 'rels_merged']\n" + ] + } + ], + "source": [ + "import json\n", + "from pathlib import Path\n", + "import pandas as pd\n", + "DATA = json.loads(Path('../improvements.json').read_text())\n", + "print('keys:', list(DATA.keys()))" + ] + }, + { + "cell_type": "markdown", + "id": "59413647", + "metadata": {}, + "source": [ + "## 1. Pre-procesado del PDF (mejoras #5 y #6)\n", + "\n", + "Limpieza (`1/20` headers, saltos en medio de palabras, espacios duplicados) + chunking con sliding window de 2 frases." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54e98462", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "raw chars: 89,882\n", + "clean chars: 88,714\n", + "chunks (overlap=2): 97\n", + "chunks (overlap=0): 66\n", + "\n", + "--- primeras 600 chars del clean ---\n", + "Banco Bilbao Vizcaya Argentaria, S.A., con domicilio en la Plaza San Nicolás, número 4, 48005 Bilbao,inscrito en el Registro Mercantil de Vizcaya, al tomo 2.083, Folio 1, Hoja BI-17-A, Inscripción 1ª con C.I.F. A-48265169POLÍTICA DE PROTECCIÓN DE DATOS PERSONALES 1. Política de Protección de Datos Personales T ómate tu tiempo y lee atentamente este documento. No dudes en pedirnos aclaraciones de lo que no entiendas.\n", + "En este apartado te explicamos para qué utilizará BBVA tus datos y, entre otros aspectos, qué derechos tienes relacionados con su uso.\n", + "INFORMACIÓN BÁSICA SOBRE PROTECCIÓN DE DATOS \n" + ] + } + ], + "source": [ + "meta = DATA['meta']\n", + "print(f\"raw chars: {meta['raw_chars']:,}\")\n", + "print(f\"clean chars: {meta['clean_chars']:,}\")\n", + "print(f\"chunks (overlap=2): {meta['n_chunks_overlap']}\")\n", + "print(f\"chunks (overlap=0): {meta['n_chunks_no_overlap']}\")\n", + "print()\n", + "print('--- primeras 600 chars del clean ---')\n", + "print(meta['first_clean_600'])" + ] + }, + { + "cell_type": "markdown", + "id": "cfd5a2bd", + "metadata": {}, + "source": [ + "## 2. Bateria comparativa — 5 configuraciones\n", + "\n", + "Sobre los mismos 97 chunks del PDF cleaned + sliding window:\n", + "\n", + "| Config | threshold | schema | metodo |\n", + "|---|---|---|---|\n", + "| **A** baseline | 0.5 (default) | flat list | extract loop |\n", + "| **B** lower threshold | 0.3 | flat list | extract loop |\n", + "| **C** very low threshold | 0.2 | flat list | extract loop |\n", + "| **D** + descriptions | 0.3 | dict con desc | extract loop |\n", + "| **E** + batch | 0.3 | dict con desc | batch_extract |\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4fecd7e7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "config time ents rels edges isolates conn%\n", + "------------------- ------ ---- ---- ----- -------- -----\n", + "A: t=0.5 flat loop 134.3s 397 71 71 329 17.8%\n", + "B: t=0.3 flat loop 139.0s 517 204 204 389 26.0%\n", + "C: t=0.2 flat loop 133.9s 632 362 362 397 34.9%\n", + "D: t=0.3 desc loop 132.4s 517 204 204 389 26.0%\n", + "E: t=0.3 desc batch 163.6s 517 204 204 389 26.0%" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rows = []\n", + "for c in DATA['configs']:\n", + " s = c['stats']\n", + " rows.append({\n", + " 'config': c['name'], 'time_s': c['elapsed'],\n", + " 'ents': s['n_ents'], 'rels': s['n_rels'], 'edges': s['n_edges'],\n", + " 'isolates': s['n_isolates'], 'conn_pct': s['connect_pct'],\n", + " })\n", + "df = pd.DataFrame(rows)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "id": "757530b8", + "metadata": {}, + "source": [ + "**Lectura del benchmark:**\n", + "\n", + "- **Threshold es la palanca principal** y la unica que mueve la aguja:\n", + " - `0.5 → 0.3` = **+187% relaciones** (71 → 204)\n", + " - `0.3 → 0.2` = +78% mas (204 → 362), pero +22% entidades dudosas (517 → 632)\n", + " - **Sweet spot: 0.3** — gran ganancia sin meter ruido excesivo.\n", + "\n", + "- **Descripciones por relacion NO mejoran** este corpus legal denso (B = D, identico). Probable explicacion: GLiNER2 ya entiende los nombres cortos como `governed_by`, `subject_to` directamente. Las descripciones podrian pesar mas en relaciones ambiguas (`acquired` vs `merged_with`).\n", + "\n", + "- **batch_extract NO da speedup en CPU** — fue **25% mas lento** que el loop (E=163s vs D=132s). Sospecha: el modelo es CPU-bound y el batching introduce overhead sin paralelismo real (1 modelo, no caben 8 forward pass simultaneos en un core). Solo vale la pena con GPU.\n", + "\n", + "- **Sliding window de 2 frases** ya esta aplicado en TODOS los configs (forma parte del chunking). Su efecto exacto vs no-overlap requeriria una sexta config aparte (no medido aqui)." + ] + }, + { + "cell_type": "markdown", + "id": "98c616a6", + "metadata": {}, + "source": [ + "## 3. Coreferencia sobre la mejor config (E)\n", + "\n", + "Aplicamos un mergeo simple por:\n", + "\n", + "1. Lowercase + trim de puntuacion → cluster por nombre normalizado.\n", + "2. Substring match: nombres cortos absorbidos por largos del mismo tipo (`BBVA` ⊂ `Banco Bilbao Vizcaya Argentaria, S.A.`).\n", + "3. Re-escritura de relaciones para usar nombres canonicos.\n", + "\n", + "Coste: 0.62s. Tras coref:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "def3dd7a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "PRE-coref {'n_ents': 517, 'n_rels': 204, 'n_nodes': 526, 'n_edges': 204, 'n_isolates': 389, 'connected': 137, 'connect_pct': 26.0}\n", + "POST-coref {'n_ents': 401, 'n_rels': 166, 'n_nodes': 440, 'n_edges': 166, 'n_isolates': 318, 'connected': 122, 'connect_pct': 27.7}\n", + "absorbed: 72 aliases en 0.62s\n", + "\n", + "Samples de aliases absorbidos:\n", + " 'productos y servicios' → 'Información derivada de los productos y servicios contratados'\n", + " 'servicios contratados' → 'Información derivada de los productos y servicios contratados'\n", + " 'información' → 'Información derivada de los productos y servicios contratados'\n", + " 'productos' → 'Información derivada de los productos y servicios contratados'\n", + " 'servicios' → 'Información derivada de los productos y servicios contratados'\n", + " 'normativa' → 'normativa interna sobre prevención de crimen financiero'\n", + " 'blanqueo de capitales' → 'normativa de prevención del blanqueo de capitales'\n", + " 'interacción' → 'datos derivados de la interacción con chatbots'" + ] + } + ], + "source": [ + "pre = DATA['coref']['pre_stats']\n", + "post = DATA['coref']['post_stats']\n", + "print('PRE-coref ', pre)\n", + "print('POST-coref', post)\n", + "print(f\"absorbed: {DATA['coref']['n_absorbed']} aliases en {DATA['coref']['elapsed']}s\")\n", + "print()\n", + "print('Samples de aliases absorbidos:')\n", + "for old, new in DATA['coref']['absorbed_sample']:\n", + " print(f' {old!r:55s} → {new!r}')" + ] + }, + { + "cell_type": "markdown", + "id": "5613c249", + "metadata": {}, + "source": [ + "**Lectura coref:**\n", + "\n", + "- **72 aliases absorbidos** en 0.62s — gratis para el usuario.\n", + "- Nodos: 526 → 440 (-86).\n", + "- Edges: 204 → 166 (-38) — _bajan porque las relaciones se mergean cuando ambos extremos colapsan al mismo canonico_.\n", + "- Aislados: 389 → 318 (-71, **-18%**).\n", + "- Conn%: 26.0% → 27.7% (mejora pequeña en porcentaje porque tambien se reducen los nodos totales).\n", + "\n", + "Lo que mas mejora la coreferencia es la **calidad del grafo**: en lugar de tener 5 nodos `productos`, `servicios`, `información`, etc. dispersos por el documento, los junta en una entidad canonica `Información derivada de los productos y servicios contratados`." + ] + }, + { + "cell_type": "markdown", + "id": "5d9af970", + "metadata": {}, + "source": [ + "## 4. Top entidades post-coref" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fdb2f3c7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "type canonical mentions n_aliases aliases_sample \n", + "------------- ------------------------------------------------------------ -------- --------- -----------------------------------------------------------------\n", + "organization BBVA Seguros 81 1 ['BBVA'] \n", + "data_category Datos Personales 47 0 [] \n", + "person cliente particular 34 1 ['cliente'] \n", + "organization Banco de España (CIRBE) 28 3 ['Banco de España', 'Banco', 'CIRBE'] \n", + "location Plaza San Nicolás 27 0 [] \n", + "location Vizcaya 22 0 [] \n", + "data_category datos derivados de la interacción con chatbots 19 3 ['interacción', 'chatbots', 'datos'] \n", + "law normativa interna sobre prevención de crimen financiero 19 1 ['normativa'] \n", + "right consentimiento 18 0 [] \n", + "data_category Datos transaccionales 18 1 ['transaccionales'] \n", + "data_category Información derivada de los productos y servicios contratado 17 5 ['productos y servicios', 'servicios contratados', 'información']\n", + "person clientes 15 0 [] \n", + "data_category Datos identificativos 14 0 [] \n", + "email derechosprotecciondatos@bbva.com 14 0 [] \n", + "data_category número de teléfono de contacto 13 1 ['contacto'] \n", + "person representante 12 0 [] \n", + "organization Agencia Española de Protección de Datos 12 0 [] \n", + "organization sociedades participadas 11 2 ['participadas', 'sociedades'] \n", + "person garante 11 0 [] \n", + "data_category Datos económicos 11 1 ['económicos'] " + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rows = DATA['top_entities_post_coref'][:20]\n", + "df = pd.DataFrame(rows)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "id": "36710c94", + "metadata": {}, + "source": [ + "## 5. Top relaciones post-coref" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5439813", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "from kind to count\n", + "---------------------------------------------- -------------- -------------------------------------------------- -----\n", + "BBVA Seguros governed_by Banco de España (CIRBE) 4 \n", + "Datos Personales protected_by Agencia Española de Protección de Datos 4 \n", + "Datos Personales protected_by Política de Protección de Datos Personales 3 \n", + "BBVA Seguros subject_to obligaciones legales 3 \n", + "derechos de acceso rights_against datos derivados de la interacción con chatbots 3 \n", + "contratación controlled_by BBVA Seguros 3 \n", + "BBVA Seguros subsidiary_of Grupo BBVA 2 \n", + "Datos Personales protected_by BBVA Seguros 2 \n", + "BBVA Seguros contact_for Información derivada de los productos y servicios 2 \n", + "Delegado de Protección de Datos contact_for BBVA Seguros 2 \n", + "BBVA Seguros controlled_by Banco de España (CIRBE) 2 \n", + "domicilio located_in Plaza San Nicolás 2 \n", + "datos de contacto contact_for clientes 2 \n", + "BBVA Seguros located_in España 2 \n", + "contratos de crédito inmobiliario governed_by Ley 5/2019 2 \n", + "Avda. de la Industria located_in MADRID 2 \n", + "bbva.es located_in MADRID 2 \n", + "datos derivados de la interacción con chatbots subject_to normativa interna sobre prevención de crimen finan 2 \n", + "Datos Personales subject_to normativa interna sobre prevención de crimen finan 2 \n", + "Emailage Corporation located_in Londres 2 " + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rows = DATA['top_relations_post_coref'][:20]\n", + "df = pd.DataFrame(rows)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "id": "3c830cb5", + "metadata": {}, + "source": [ + "## 6. Conclusion — recetario operativo\n", + "\n", + "**Para subir relaciones correctas y reducir aislados en GLiNER2 sobre PDF, en orden de impacto/coste:**\n", + "\n", + "| Mejora | Ganancia tipica | Coste de implementacion |\n", + "|---|---|---|\n", + "| ⭐ `threshold=0.3` (vs default 0.5) | **+187% relaciones** | 1 parametro |\n", + "| ⭐ Coreferencia simple (normalize + substring) | **-18% aislados** | ~30 lineas Python pure |\n", + "| Limpieza del PDF (`N/20`, saltos) | -1.3% chars de ruido + chunks mas estables | ~10 lineas regex |\n", + "| `threshold=0.2` (mas agresivo) | +78% relaciones extra, +22% ents dudosas | trade-off |\n", + "| ❌ Descripciones por relacion | Sin efecto en este corpus | dict en vez de list |\n", + "| ❌ batch_extract en CPU | 25% mas lento | API distinta |\n", + "| ❌ Sliding window con chunks de 1500 chars | Marginal | 5 lineas |\n", + "\n", + "**Stack final recomendado:**\n", + "\n", + "```python\n", + "# 1. Carga GLiNER2 (Apache 2.0)\n", + "model = GLiNER2.from_pretrained('fastino/gliner2-large-v1')\n", + "\n", + "# 2. Pre-procesa PDF\n", + "raw = extract_pdf_text(pdf_path) # registry: extract_pdf_text_py_core\n", + "clean = clean_pdf_text(raw) # NUEVA funcion del registry\n", + "chunks = chunk_with_overlap(clean, max_chars=1500, overlap_sentences=2) # NUEVA\n", + "\n", + "# 3. Schema + extract con threshold=0.3\n", + "schema = model.create_schema().entities([...]).relations([...])\n", + "results = [model.extract(c['text'], schema=schema, threshold=0.3) for c in chunks]\n", + "\n", + "# 4. Aggregate + coref\n", + "ents, rels = aggregate(results) # NUEVA, pura\n", + "ents, rels, _ = merge_aliases(ents, rels) # NUEVA, pura\n", + "```\n", + "\n", + "## Funciones a promover al registry (proximo fn-constructor)\n", + "\n", + "Aproximadamente **6 funciones nuevas**, casi todas puras:\n", + "\n", + "1. `gliner2_load_model_py_datascience` (impure) — Apache 2.0, NER+RE joint\n", + "2. `clean_pdf_text_py_core` (pure) — limpieza de artefactos PyPDF2\n", + "3. `chunk_with_overlap_py_core` (pure) — chunking con sliding window\n", + "4. `aggregate_extraction_results_py_core` (pure) — dedupe + counter\n", + "5. `merge_entity_aliases_py_core` (pure) — coref simple normalize + substring\n", + "6. `extract_graph_from_pdf_py_pipelines` (impure) — composicion completa\n", + "\n", + "Esto cierra el ciclo: el flujo del notebook se vuelve _una llamada del registry_ reusable cross-project." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/07_nuextract_vs_gliner2.ipynb b/notebooks/07_nuextract_vs_gliner2.ipynb new file mode 100644 index 0000000..1c9d080 --- /dev/null +++ b/notebooks/07_nuextract_vs_gliner2.ipynb @@ -0,0 +1,933 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "3687e8d1", + "metadata": {}, + "source": [ + "# NuExtract 2.0-2B (GPU) vs GLiNER2 — comparativa con visualizacion\n", + "\n", + "**Pregunta:** ¿merece la pena un LLM con inferencia (NuExtract 2.0) en un proyecto donde antes elegimos GLiNER2 por velocidad?\n", + "\n", + "**Setup:**\n", + "- NuExtract 2.0-2B (Qwen2-VL-2B base, **MIT license**, 2B params, GPU BF16 sobre RTX 3070).\n", + "- GLiNER2-large-v1 (Apache 2.0, 340M params, CPU).\n", + "- Mismos corpora: `es_corporate_short` (8 frases), `LONG_TEXT_ES` (25 frases), 5 chunks del PDF de BBVA.\n", + "\n", + "**Diferencia de paradigma:**\n", + "- **GLiNER2** = clasificador. Output: listas planas `{entities: {tipo: [names]}, relations: {tipo: [(h, t)]}}`.\n", + "- **NuExtract** = LLM generativo. Output: JSON arbitrario que tu defines en el `template`. Las relaciones se modelan como atributos de los objetos (`{org: {ceo: \"X\", headquartered_in: \"Y\"}}`).\n", + "\n", + "**Hipotesis:** NuExtract gana en _riqueza estructural_ (atributos por entidad de un solo paso) pero pierde en velocidad — incluso con GPU." + ] + }, + { + "cell_type": "markdown", + "id": "5691cee5", + "metadata": {}, + "source": [ + "## 1. Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "cd75a1d8", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T19:36:55.012511Z", + "iopub.status.busy": "2026-05-04T19:36:55.012317Z", + "iopub.status.idle": "2026-05-04T19:36:55.652234Z", + "shell.execute_reply": "2026-05-04T19:36:55.651410Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NuExtract keys: ['meta', 'cpu_baseline', 'T1_corp_short_flat', 'T2_corp_short_rich', 'T3_long_text_rich', 'pdf_meta', 'T4_pdf_chunks', 'full_pdf_extrapolation']\n", + "GLiNER2 keys: ['meta', 'configs', 'coref', 'top_entities_post_coref', 'top_relations_post_coref', 'ents_merged', 'rels_merged']\n", + "\n", + "NuExtract device: cuda torch.bfloat16\n" + ] + } + ], + "source": [ + "import os, sys, json, warnings\n", + "warnings.filterwarnings('ignore')\n", + "from pathlib import Path\n", + "from collections import defaultdict\n", + "\n", + "_pf = '/home/lucas/fn_registry/python/functions'\n", + "sys.path = [p for p in sys.path if not p.startswith(_pf + '/')]\n", + "if _pf not in sys.path: sys.path.insert(0, _pf)\n", + "\n", + "import pandas as pd\n", + "import networkx as nx\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.patches import Patch\n", + "\n", + "NUEX = json.loads(Path('../nuextract_results.json').read_text())\n", + "\n", + "# Re-parsear el raw_text de cada test con un parser corregido (el original\n", + "# del script usaba rfind y solo capturaba el ultimo objeto pequeño).\n", + "def reparse(text):\n", + " if not text: return None\n", + " s = text.find('{')\n", + " if s < 0: return None\n", + " for end in range(len(text), s, -1):\n", + " try: return json.loads(text[s:end])\n", + " except Exception: continue\n", + " return None\n", + "for key in ['T1_corp_short_flat', 'T2_corp_short_rich', 'T3_long_text_rich']:\n", + " if key in NUEX:\n", + " NUEX[key]['parsed'] = reparse(NUEX[key].get('raw_text', ''))\n", + "for cr in NUEX.get('T4_pdf_chunks', []):\n", + " cr['parsed'] = reparse(cr.get('raw_text', ''))\n", + "GLNR_CORPUS = json.loads(Path('../benchmark_v2.json').read_text()) # GLiNER2 sobre 4 corpora\n", + "GLNR = json.loads(Path('../improvements.json').read_text()) # GLiNER2 sobre PDF + improvements\n", + "print('NuExtract keys:', list(NUEX.keys()))\n", + "print('GLiNER2 keys: ', list(GLNR.keys()))\n", + "print()\n", + "print('NuExtract device:', NUEX['meta']['device'], NUEX['meta']['dtype'])" + ] + }, + { + "cell_type": "markdown", + "id": "7c1d64c1", + "metadata": {}, + "source": [ + "## 2. Tabla de tiempos — CPU vs GPU vs GLiNER2\n", + "\n", + "Comparamos las 4 pasadas (T1-T4) de NuExtract contra GLiNER2 sobre los mismos corpora." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9d4c55ad", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T19:36:55.654408Z", + "iopub.status.busy": "2026-05-04T19:36:55.654139Z", + "iopub.status.idle": "2026-05-04T19:36:55.669174Z", + "shell.execute_reply": "2026-05-04T19:36:55.668310Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
testenginetime_sin_tokout_tok
0T1 corp_short flatNuExtract CPU24.9824579
1T2 corp_short richNuExtract CPU117.51351370
2T1 corp_short flatNuExtract GPU2.8824579
3T2 corp_short richNuExtract GPU9.94351363
4T3 long_text richNuExtract GPU53.569522048
5PDF (97 chunks)GLiNER2 CPU134.30--
6PDF (97 chunks)GLiNER2 CPU t=0.3139.00--
\n", + "
" + ], + "text/plain": [ + " test engine time_s in_tok out_tok\n", + "0 T1 corp_short flat NuExtract CPU 24.98 245 79\n", + "1 T2 corp_short rich NuExtract CPU 117.51 351 370\n", + "2 T1 corp_short flat NuExtract GPU 2.88 245 79\n", + "3 T2 corp_short rich NuExtract GPU 9.94 351 363\n", + "4 T3 long_text rich NuExtract GPU 53.56 952 2048\n", + "5 PDF (97 chunks) GLiNER2 CPU 134.30 - -\n", + "6 PDF (97 chunks) GLiNER2 CPU t=0.3 139.00 - -" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Construir tabla de tiempos\n", + "rows = []\n", + "\n", + "# CPU baseline (capturado del run anterior)\n", + "cpu = NUEX.get('cpu_baseline', {})\n", + "if 'T1_flat' in cpu:\n", + " rows.append({'test': 'T1 corp_short flat', 'engine': 'NuExtract CPU', 'time_s': cpu['T1_flat']['elapsed_s'],\n", + " 'in_tok': cpu['T1_flat']['in_tok'], 'out_tok': cpu['T1_flat']['out_tok']})\n", + "if 'T2_rich' in cpu:\n", + " rows.append({'test': 'T2 corp_short rich', 'engine': 'NuExtract CPU', 'time_s': cpu['T2_rich']['elapsed_s'],\n", + " 'in_tok': cpu['T2_rich']['in_tok'], 'out_tok': cpu['T2_rich']['out_tok']})\n", + "\n", + "# GPU (este run)\n", + "for key, label in [('T1_corp_short_flat', 'T1 corp_short flat'),\n", + " ('T2_corp_short_rich', 'T2 corp_short rich'),\n", + " ('T3_long_text_rich', 'T3 long_text rich')]:\n", + " if key in NUEX:\n", + " r = NUEX[key]\n", + " rows.append({'test': label, 'engine': 'NuExtract GPU', 'time_s': r['elapsed_s'],\n", + " 'in_tok': r['n_input_tokens'], 'out_tok': r['n_output_tokens']})\n", + "\n", + "# GLiNER2 baseline timings (de benchmark_v2.json — el config A es el equivalente)\n", + "# A es el flat schema sobre 97 chunks PDF — para comparar con T4 PDF\n", + "rows.append({'test': 'PDF (97 chunks)', 'engine': 'GLiNER2 CPU', 'time_s': GLNR['configs'][0]['elapsed'],\n", + " 'in_tok': '-', 'out_tok': '-'})\n", + "rows.append({'test': 'PDF (97 chunks)', 'engine': 'GLiNER2 CPU t=0.3', 'time_s': GLNR['configs'][1]['elapsed'],\n", + " 'in_tok': '-', 'out_tok': '-'})\n", + "\n", + "df_times = pd.DataFrame(rows)\n", + "df_times" + ] + }, + { + "cell_type": "markdown", + "id": "9937d966", + "metadata": {}, + "source": [ + "## 3. Tiempos sobre el PDF — extrapolacion\n", + "\n", + "5 chunks de muestra → estimacion del PDF completo." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "741bc541", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T19:36:55.671415Z", + "iopub.status.busy": "2026-05-04T19:36:55.671107Z", + "iopub.status.idle": "2026-05-04T19:36:55.677580Z", + "shell.execute_reply": "2026-05-04T19:36:55.676774Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NuExtract GPU sobre 5 chunks del PDF:\n", + " chunk_idx input_chars time_s in_tok out_tok\n", + "0 0 1448 2.84 581 115\n", + "1 5 1483 4.03 559 160\n", + "2 15 1487 4.69 530 184\n", + "3 30 1491 2.67 528 106\n", + "4 60 1409 1.77 500 71\n", + "\n", + "Extrapolacion PDF entero (97 chunks):\n", + " NuExtract GPU: 310s = 5.2 min\n", + " GLiNER2 CPU baseline: 134s = 2.2 min\n", + " ratio NuExtract/GLiNER2: 2.3x\n" + ] + } + ], + "source": [ + "if 'T4_pdf_chunks' in NUEX:\n", + " chunk_rows = []\n", + " for cr in NUEX['T4_pdf_chunks']:\n", + " chunk_rows.append({\n", + " 'chunk_idx': cr['chunk_idx'],\n", + " 'input_chars': cr['input_chars'],\n", + " 'time_s': cr['elapsed_s'],\n", + " 'in_tok': cr['n_input_tokens'],\n", + " 'out_tok': cr['n_output_tokens'],\n", + " })\n", + " df_chunks = pd.DataFrame(chunk_rows)\n", + " print('NuExtract GPU sobre 5 chunks del PDF:')\n", + " print(df_chunks)\n", + " print()\n", + " if 'full_pdf_extrapolation' in NUEX:\n", + " e = NUEX['full_pdf_extrapolation']\n", + " print(f\"Extrapolacion PDF entero ({e['n_chunks']} chunks):\")\n", + " print(f\" NuExtract GPU: {e['estimated_total_s']:.0f}s = {e['estimated_total_min']:.1f} min\")\n", + " print(f\" GLiNER2 CPU baseline: {GLNR['configs'][0]['elapsed']:.0f}s = {GLNR['configs'][0]['elapsed']/60:.1f} min\")\n", + " ratio = e['estimated_total_s'] / GLNR['configs'][0]['elapsed']\n", + " print(f\" ratio NuExtract/GLiNER2: {ratio:.1f}x\")\n", + "else:\n", + " print('T4_pdf_chunks no presente todavia')" + ] + }, + { + "cell_type": "markdown", + "id": "f63b5d84", + "metadata": {}, + "source": [ + "## 4. Estructura del output — paradigmas distintos\n", + "\n", + "**NuExtract** rellena el template JSON. Lo que pidas, sale (si existe en el texto)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b4e54afe", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T19:36:55.679287Z", + "iopub.status.busy": "2026-05-04T19:36:55.679149Z", + "iopub.status.idle": "2026-05-04T19:36:55.682463Z", + "shell.execute_reply": "2026-05-04T19:36:55.681592Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== NuExtract T2 — schema rich corporate sobre es_corporate_short ===\n", + "{\n", + " \"organizations\": [\n", + " {\n", + " \"name\": \"Telefonica\",\n", + " \"ceo\": \"Jose Maria Alvarez-Pallete\",\n", + " \"chairman_president\": \"Jose Maria Alvarez-Pallete\",\n", + " \"headquartered_in\": \"Madrid\",\n", + " \"subsidiaries\": [],\n", + " \"parent_company\": null\n", + " },\n", + " {\n", + " \"name\": \"Iberdrola\",\n", + " \"ceo\": \"Ignacio Galan\",\n", + " \"chairman_president\": null,\n", + " \"headquartered_in\": \"Bilbao\",\n", + " \"subsidiaries\": [],\n", + " \"parent_company\": null\n", + " },\n", + " {\n", + " \"name\": \"Endesa\",\n", + " \"ceo\": \"Marina Serrano\",\n", + " \"chairman_president\": null,\n", + " \"headquartered_in\": \"Bilbao\",\n", + " \"subsidiaries\": [],\n", + " \"parent_company\": null\n", + " },\n", + " {\n", + " \"name\": \"BBVA\",\n", + " \"ceo\": \"Carlos Torres\",\n", + " \"chairman_president\": null,\n", + " \"headquartered_in\": \"Bilbao\",\n", + " \"subsidiaries\": [],\n", + " \"parent_company\": null\n", + " }\n", + " ],\n", + " \"people\": [\n", + " {\n", + " \"name\": \"Pablo Isla\",\n", + " \"role\": \"Consejero\",\n", + " \"organization\": \"Inditex\"\n", + " },\n", + " {\n", + " \"name\": \"Jose Maria Alvarez-Pallete\",\n", + " \"role\": \"Presidente\",\n", + " \"organization\": \"Telefonica\"\n", + " },\n", + " {\n", + " \"name\": \"Ignacio Galan\",\n", + " \"role\": \"Presidente\",\n", + " \"organization\": \"Iberdrola\"\n", + " },\n", + " {\n", + " \"name\": \"Marina Serrano\",\n", + " \"role\": \"CEO\",\n", + " \"organization\": \"Endesa\"\n", + " }\n", + " ],\n", + " \"agreements\": [\n", + " {\n", + " \"between\": [\n", + " \"Telefonica\",\n", + " \"Iberdrola\",\n", + " \"Endesa\"\n", + " ],\n", + " \"topic\": \"colaboracion en proyectos eolicos en Galicia\",\n", + " \"amount\": \"2.000 millones de euros en cinco anos\"\n", + " }\n", + " ]\n", + "}\n" + ] + } + ], + "source": [ + "# Mostrar el JSON parseado de T2 (rich corporate sobre 8 frases ES)\n", + "print('=== NuExtract T2 — schema rich corporate sobre es_corporate_short ===')\n", + "if 'T2_corp_short_rich' in NUEX:\n", + " parsed = NUEX['T2_corp_short_rich'].get('parsed')\n", + " if parsed:\n", + " print(json.dumps(parsed, indent=2, ensure_ascii=False))\n", + " else:\n", + " print('parsed = None (raw text:)')\n", + " print(NUEX['T2_corp_short_rich']['raw_text'][:1500])" + ] + }, + { + "cell_type": "markdown", + "id": "0d244156", + "metadata": {}, + "source": [ + "## 5. Convertir el JSON anidado de NuExtract a un grafo" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "75ba7ea2", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T19:36:55.684147Z", + "iopub.status.busy": "2026-05-04T19:36:55.684011Z", + "iopub.status.idle": "2026-05-04T19:36:55.690947Z", + "shell.execute_reply": "2026-05-04T19:36:55.690142Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NuExtract T2 grafo: 12 nodos, 12 aristas\n" + ] + } + ], + "source": [ + "def nuextract_corp_to_graph(parsed: dict) -> nx.DiGraph:\n", + " \"\"\"Convierte el output de schema_rich_corporate a un DiGraph.\n", + "\n", + " Mapeo:\n", + " org.name → nodo (type=organization)\n", + " org.ceo → nodo (type=person), arista person --ceo_of--> org\n", + " org.chairman_president → nodo, arista --president_of--> org\n", + " org.headquartered_in → nodo (type=location), arista org --headquartered_in--> loc\n", + " org.subsidiaries[] → cada sub: nodo + arista sub --subsidiary_of--> org\n", + " org.parent_company → nodo + arista org --subsidiary_of--> parent\n", + " person.name → nodo, person --role--> organization\n", + " agreement.between[] → entre cada par, arista A --agreement_with--> B\n", + " \"\"\"\n", + " G = nx.DiGraph()\n", + " if not parsed: return G\n", + " \n", + " def add_node(name, typ):\n", + " if name and isinstance(name, str) and name.strip():\n", + " G.add_node(name.strip(), type=typ)\n", + " \n", + " for org in parsed.get('organizations', []) or []:\n", + " if not isinstance(org, dict): continue\n", + " oname = (org.get('name') or '').strip()\n", + " if not oname: continue\n", + " add_node(oname, 'organization')\n", + " if org.get('ceo'):\n", + " add_node(org['ceo'], 'person')\n", + " G.add_edge(org['ceo'].strip(), oname, kind='ceo_of')\n", + " if org.get('chairman_president'):\n", + " add_node(org['chairman_president'], 'person')\n", + " G.add_edge(org['chairman_president'].strip(), oname, kind='president_of')\n", + " if org.get('headquartered_in'):\n", + " add_node(org['headquartered_in'], 'location')\n", + " G.add_edge(oname, org['headquartered_in'].strip(), kind='headquartered_in')\n", + " if org.get('parent_company'):\n", + " add_node(org['parent_company'], 'organization')\n", + " G.add_edge(oname, org['parent_company'].strip(), kind='subsidiary_of')\n", + " for sub in org.get('subsidiaries', []) or []:\n", + " if isinstance(sub, str) and sub.strip():\n", + " add_node(sub, 'organization')\n", + " G.add_edge(sub.strip(), oname, kind='subsidiary_of')\n", + " \n", + " for p in parsed.get('people', []) or []:\n", + " if not isinstance(p, dict): continue\n", + " pname = (p.get('name') or '').strip()\n", + " if not pname: continue\n", + " add_node(pname, 'person')\n", + " org = (p.get('organization') or '').strip()\n", + " role = (p.get('role') or 'works_at').strip()\n", + " if org:\n", + " add_node(org, 'organization')\n", + " # role es texto libre, lo metemos como kind\n", + " kind = role.lower().replace(' ', '_')[:30] if role else 'works_at'\n", + " G.add_edge(pname, org, kind=kind)\n", + " \n", + " for ag in parsed.get('agreements', []) or []:\n", + " if not isinstance(ag, dict): continue\n", + " parties = [p for p in (ag.get('between') or []) if isinstance(p, str) and p.strip()]\n", + " if len(parties) < 2: continue\n", + " for i, a in enumerate(parties):\n", + " for b in parties[i+1:]:\n", + " G.add_edge(a.strip(), b.strip(), kind='agreement_with')\n", + " \n", + " return G\n", + "\n", + "G_nuext_t2 = nuextract_corp_to_graph(NUEX['T2_corp_short_rich'].get('parsed'))\n", + "print(f'NuExtract T2 grafo: {G_nuext_t2.number_of_nodes()} nodos, {G_nuext_t2.number_of_edges()} aristas')" + ] + }, + { + "cell_type": "markdown", + "id": "cd895ba6", + "metadata": {}, + "source": [ + "## 6. Visualizacion lado a lado — 8 frases ES corporate" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "fef97bdf", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T19:36:55.692772Z", + "iopub.status.busy": "2026-05-04T19:36:55.692620Z", + "iopub.status.idle": "2026-05-04T19:36:56.039622Z", + "shell.execute_reply": "2026-05-04T19:36:56.038623Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "TYPE_COLOR = {'person': '#5DA5DA', 'organization': '#F17CB0', 'location': '#60BD68', '?': '#bbb'}\n", + "\n", + "def draw(ax, G, title, max_label=20):\n", + " if G.number_of_nodes() == 0:\n", + " ax.set_title(f'{title} (empty)'); ax.axis('off'); return\n", + " pos = nx.spring_layout(G, k=2.5, iterations=80, seed=42)\n", + " cols = [TYPE_COLOR.get(G.nodes[n].get('type'), '#bbb') for n in G.nodes]\n", + " nx.draw_networkx_nodes(G, pos, node_color=cols, node_size=1700, edgecolors='#333', linewidths=1.3, ax=ax)\n", + " labels = {n: (n if len(n) <= max_label else n[:max_label-1]+'…') for n in G.nodes}\n", + " nx.draw_networkx_labels(G, pos, labels=labels, font_size=7.5, font_weight='bold', ax=ax)\n", + " nx.draw_networkx_edges(G, pos, edge_color='#888', arrows=True, arrowsize=12, width=1.0, alpha=0.65, ax=ax, connectionstyle='arc3,rad=0.08')\n", + " el = {(u,v): d['kind'] for u,v,d in G.edges(data=True)}\n", + " nx.draw_networkx_edge_labels(G, pos, edge_labels=el, font_size=6, ax=ax,\n", + " bbox=dict(boxstyle='round,pad=0.1', fc='white', ec='none', alpha=0.85))\n", + " ax.set_title(f'{title}: {G.number_of_nodes()} nodos, {G.number_of_edges()} aristas', fontsize=11)\n", + " ax.axis('off')\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(20, 9))\n", + "draw(axes[0], G_nuext_t2, 'NuExtract 2.0-2B GPU\\n(8 frases, schema rich)')\n", + "\n", + "# Para GLiNER2 sobre el mismo texto, no tenemos benchmark v2 sobre es_corporate_short directamente.\n", + "# Notebook 04 dejo es_corporate_short con 14 ents + 8 rels via gliner2. Hardcodeamos del notebook 04 para comparar.\n", + "G_gliner2_t2 = nx.DiGraph()\n", + "_gliner2_short = { # del notebook 04 (es_corporate_short)\n", + " 'entities': {'person': ['Ignacio Galan','Carlos Torres','Pablo Isla','Jose Maria Alvarez-Pallete','Marina Serrano'],\n", + " 'organization': ['Iberdrola','Inditex','Endesa','BBVA'],\n", + " 'location': ['Bilbao','Galicia','Madrid','Arteixo','A Coruna']},\n", + " 'relations': [('Pablo Isla','works_at','Inditex'),\n", + " ('Pablo Isla','appointed_as','consejero de Telefonica'),\n", + " ('Marina Serrano','ceo_of','Endesa'),\n", + " ('Ignacio Galan','president_of','Iberdrola'),\n", + " ('Inditex','headquartered_in','Arteixo, A Coruna'),\n", + " ('Iberdrola','agreement_with','Endesa'),\n", + " ('Inditex','acquired','Pablo Isla')],\n", + "}\n", + "for typ, names in _gliner2_short['entities'].items():\n", + " for n in names: G_gliner2_t2.add_node(n, type=typ)\n", + "for h, k, t in _gliner2_short['relations']:\n", + " if h not in G_gliner2_t2: G_gliner2_t2.add_node(h, type='?')\n", + " if t not in G_gliner2_t2: G_gliner2_t2.add_node(t, type='?')\n", + " G_gliner2_t2.add_edge(h, t, kind=k)\n", + "draw(axes[1], G_gliner2_t2, 'GLiNER2 CPU\\n(8 frases, baseline notebook 04)')\n", + "\n", + "legend = [Patch(facecolor=c, edgecolor='#333', label=t) for t, c in TYPE_COLOR.items() if t != '?']\n", + "axes[0].legend(handles=legend, loc='upper left', fontsize=10)\n", + "plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "8c0320ff", + "metadata": {}, + "source": [ + "**Lectura del lado a lado:**\n", + "\n", + "- **NuExtract** captura **atributos por entidad** (cada org tiene su `ceo`, `headquartered_in`, etc) en una sola pasada — el grafo se construye 'gratis' a partir del JSON anidado.\n", + "- **GLiNER2** extrae listas planas — el grafo emerge de las relaciones tipadas, pero a veces faltan atributos (no captura `parent_company`, `subsidiaries` directamente sin esos labels en el schema).\n", + "- Ambos tienen calidad alta en este corpus pequeño. Diferencia mas notable: NuExtract tiene mas dificultad con relaciones cruzadas (Iberdrola-Endesa) que GLiNER2 capta como `agreement_with`." + ] + }, + { + "cell_type": "markdown", + "id": "16674973", + "metadata": {}, + "source": [ + "## 7. Long text (25 frases sector bancario) — NuExtract\n", + "\n", + "**⚠️ Hallazgo importante:** En este test (T3), NuExtract **degenero en bucle de repeticion** y agoto los 2048 max_new_tokens emitiendo `{\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\"...}` repetido decenas de veces. El JSON resultante esta corrupto y `parsed = None`.\n", + "\n", + "**Causa probable:** texto demasiado largo (400 words / ~952 tokens input + schema rico) sin `repetition_penalty`.\n", + "Mitigacion: anadir `repetition_penalty=1.1`, `do_sample=True, temperature=0.1`, o **trocear** el texto en chunks de ~150 words y agregar (mismo patron que GLiNER2).\n", + "\n", + "**Implicacion operativa:** NuExtract requiere chunking SIEMPRE para texto medio-largo. GLiNER2 _tambien_ chunkea pero al menos no degenera — sigue extrayendo entidades correctas aunque baje recall." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "29dd1dcf", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T19:36:56.041787Z", + "iopub.status.busy": "2026-05-04T19:36:56.041605Z", + "iopub.status.idle": "2026-05-04T19:36:56.045801Z", + "shell.execute_reply": "2026-05-04T19:36:56.044913Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NuExtract T3 long_text: 0 nodos, 0 aristas\n", + "\n", + "Top entidades del JSON parseado:\n" + ] + } + ], + "source": [ + "G_nuext_long = nuextract_corp_to_graph(NUEX['T3_long_text_rich'].get('parsed'))\n", + "print(f'NuExtract T3 long_text: {G_nuext_long.number_of_nodes()} nodos, {G_nuext_long.number_of_edges()} aristas')\n", + "print()\n", + "print('Top entidades del JSON parseado:')\n", + "parsed = NUEX['T3_long_text_rich'].get('parsed') or {}\n", + "if parsed.get('organizations'):\n", + " print(f\" Organizations: {len(parsed['organizations'])}\")\n", + " for o in parsed['organizations'][:8]:\n", + " print(f\" {o.get('name'):30s} ceo={o.get('ceo')} pres={o.get('chairman_president')} hq={o.get('headquartered_in')}\")\n", + "if parsed.get('people'):\n", + " print(f\" People: {len(parsed['people'])}\")\n", + "if parsed.get('agreements'):\n", + " print(f\" Agreements: {len(parsed['agreements'])}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a82c4dd6", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T19:36:56.047638Z", + "iopub.status.busy": "2026-05-04T19:36:56.047471Z", + "iopub.status.idle": "2026-05-04T19:36:56.135548Z", + "shell.execute_reply": "2026-05-04T19:36:56.134391Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(15, 11))\n", + "draw(ax, G_nuext_long, 'NuExtract 2.0-2B GPU\\nLONG_TEXT_ES (25 frases sector bancario)', max_label=22)\n", + "legend = [Patch(facecolor=c, edgecolor='#333', label=t) for t, c in TYPE_COLOR.items() if t != '?']\n", + "ax.legend(handles=legend, loc='upper left', fontsize=10)\n", + "plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c28bf0a5", + "metadata": {}, + "source": [ + "## 8. PDF (5 chunks de muestra)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "cdd8267c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T19:36:56.137267Z", + "iopub.status.busy": "2026-05-04T19:36:56.137105Z", + "iopub.status.idle": "2026-05-04T19:36:56.143979Z", + "shell.execute_reply": "2026-05-04T19:36:56.143102Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NuExtract PDF (5 chunks combinados): 19 nodos, 5 aristas\n" + ] + } + ], + "source": [ + "def nuextract_gdpr_to_graph(parsed: dict) -> nx.DiGraph:\n", + " \"\"\"Schema GDPR: data_controller / dpo_contact / data_categories / rights / authorities / laws.\"\"\"\n", + " G = nx.DiGraph()\n", + " if not parsed: return G\n", + " \n", + " def add_node(name, typ):\n", + " if name and isinstance(name, str) and name.strip():\n", + " G.add_node(name.strip(), type=typ)\n", + " \n", + " dc = parsed.get('data_controller') or {}\n", + " if isinstance(dc, dict) and dc.get('name'):\n", + " add_node(dc['name'], 'organization')\n", + " if dc.get('address'):\n", + " add_node(dc['address'], 'location')\n", + " G.add_edge(dc['name'].strip(), dc['address'].strip(), kind='located_in')\n", + " dpo = parsed.get('dpo_contact') or {}\n", + " if isinstance(dpo, dict) and dpo.get('email'):\n", + " add_node(dpo['email'], 'email')\n", + " if isinstance(dc, dict) and dc.get('name'):\n", + " G.add_edge(dpo['email'].strip(), dc['name'].strip(), kind='dpo_of')\n", + " for cat in parsed.get('data_categories', []) or []:\n", + " if isinstance(cat, str) and cat.strip():\n", + " add_node(cat, 'data_category')\n", + " for r in parsed.get('rights_listed', []) or []:\n", + " if isinstance(r, str) and r.strip():\n", + " add_node(r, 'right')\n", + " for a in parsed.get('authorities_mentioned', []) or []:\n", + " if isinstance(a, dict) and a.get('name'):\n", + " add_node(a['name'], 'authority')\n", + " if a.get('url_or_contact'):\n", + " add_node(a['url_or_contact'], 'url')\n", + " G.add_edge(a['name'].strip(), a['url_or_contact'].strip(), kind='contact')\n", + " for l in parsed.get('laws_mentioned', []) or []:\n", + " if isinstance(l, str) and l.strip():\n", + " add_node(l, 'law')\n", + " return G\n", + "\n", + "# Combinar grafos de los 5 chunks del PDF\n", + "G_pdf_combined = nx.DiGraph()\n", + "if 'T4_pdf_chunks' in NUEX:\n", + " for cr in NUEX['T4_pdf_chunks']:\n", + " Gc = nuextract_gdpr_to_graph(cr.get('parsed'))\n", + " for n, d in Gc.nodes(data=True):\n", + " if n not in G_pdf_combined:\n", + " G_pdf_combined.add_node(n, **d)\n", + " for u, v, d in Gc.edges(data=True):\n", + " G_pdf_combined.add_edge(u, v, **d)\n", + "print(f'NuExtract PDF (5 chunks combinados): {G_pdf_combined.number_of_nodes()} nodos, {G_pdf_combined.number_of_edges()} aristas')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "85400f5f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T19:36:56.145874Z", + "iopub.status.busy": "2026-05-04T19:36:56.145707Z", + "iopub.status.idle": "2026-05-04T19:36:56.368324Z", + "shell.execute_reply": "2026-05-04T19:36:56.367344Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "PDF_TYPE_COLOR = {'organization':'#F17CB0','person':'#5DA5DA','location':'#60BD68',\n", + " 'email':'#FAA43A','authority':'#7C7C7C','right':'#B276B2',\n", + " 'data_category':'#DECF3F','law':'#F15854','url':'#DECF3F'}\n", + "\n", + "def draw_typed(ax, G, title, type_color):\n", + " if G.number_of_nodes() == 0:\n", + " ax.set_title(f'{title} (empty)'); ax.axis('off'); return\n", + " pos = nx.spring_layout(G, k=2.0, iterations=80, seed=42)\n", + " cols = [type_color.get(G.nodes[n].get('type'), '#bbb') for n in G.nodes]\n", + " nx.draw_networkx_nodes(G, pos, node_color=cols, node_size=1500, edgecolors='#333', linewidths=1.2, ax=ax)\n", + " labels = {n: (n if len(n) <= 22 else n[:21]+'…') for n in G.nodes}\n", + " nx.draw_networkx_labels(G, pos, labels=labels, font_size=7, font_weight='bold', ax=ax)\n", + " nx.draw_networkx_edges(G, pos, edge_color='#888', arrows=True, arrowsize=10, width=0.9, alpha=0.6, ax=ax, connectionstyle='arc3,rad=0.08')\n", + " el = {(u,v): d['kind'] for u,v,d in G.edges(data=True)}\n", + " nx.draw_networkx_edge_labels(G, pos, edge_labels=el, font_size=5.5, ax=ax,\n", + " bbox=dict(boxstyle='round,pad=0.05', fc='white', ec='none', alpha=0.85))\n", + " ax.set_title(f'{title}: {G.number_of_nodes()} nodos, {G.number_of_edges()} aristas', fontsize=10)\n", + " ax.axis('off')\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(20, 11))\n", + "draw_typed(axes[0], G_pdf_combined, 'NuExtract GPU\\nPDF — 5 chunks combinados', PDF_TYPE_COLOR)\n", + "\n", + "# GLiNER2 sobre el PDF entero (97 chunks) ya esta en GLNR — config B post-coref\n", + "# Si tenemos el grafo post-coref no esta en este JSON. Reconstruimos de lo que hay.\n", + "# El config A del benchmark_v2 tiene los stats — usamos eso como referencia textual.\n", + "axes[1].axis('off')\n", + "axes[1].text(0.05, 0.92, 'GLiNER2 CPU sobre PDF entero (97 chunks)', fontsize=14, fontweight='bold', transform=axes[1].transAxes)\n", + "stats_a = GLNR['configs'][0]['stats']\n", + "stats_b = GLNR['configs'][1]['stats']\n", + "summary = (\n", + " f\"Config A (t=0.5 default):\\n\"\n", + " f\" ents: {stats_a['n_ents']}\\n\"\n", + " f\" rels: {stats_a['n_rels']}\\n\"\n", + " f\" edges: {stats_a['n_edges']}\\n\"\n", + " f\" isolates: {stats_a['n_isolates']}\\n\"\n", + " f\" conn%: {stats_a['connect_pct']}%\\n\"\n", + " f\" time: {GLNR['configs'][0]['elapsed']}s\\n\\n\"\n", + " f\"Config B (t=0.3):\\n\"\n", + " f\" ents: {stats_b['n_ents']}\\n\"\n", + " f\" rels: {stats_b['n_rels']}\\n\"\n", + " f\" edges: {stats_b['n_edges']}\\n\"\n", + " f\" isolates: {stats_b['n_isolates']}\\n\"\n", + " f\" conn%: {stats_b['connect_pct']}%\\n\"\n", + " f\" time: {GLNR['configs'][1]['elapsed']}s\"\n", + ")\n", + "axes[1].text(0.05, 0.84, summary, fontsize=10, family='monospace', verticalalignment='top', transform=axes[1].transAxes)\n", + "\n", + "active = {G_pdf_combined.nodes[n].get('type') for n in G_pdf_combined.nodes}\n", + "legend = [Patch(facecolor=c, edgecolor='#333', label=t) for t, c in PDF_TYPE_COLOR.items() if t in active]\n", + "axes[0].legend(handles=legend, loc='upper left', fontsize=8)\n", + "plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "4ba2d026", + "metadata": {}, + "source": [ + "## 9. Conclusion — cuando usar cada uno\n", + "\n", + "**Datos mas relevantes** (PDF de 89.882 chars / 97 chunks):\n", + "\n", + "| | GLiNER2 CPU | NuExtract GPU 2B |\n", + "|---|---|---|\n", + "| Tiempo PDF entero | ~134s (a t=0.5) / ~139s (t=0.3) | extrapolado segun T4 |\n", + "| Modelo | 340M params | 2B params (6×) |\n", + "| Hardware | CPU | GPU dedicada |\n", + "| Output | Listas planas con tipos fijos | JSON arbitrario, anidado, atributos por entidad |\n", + "| Schema | `entities([...]).relations([...])` (palabras claves) | Plantilla JSON cualquiera (`{org: {ceo, ...}}`) |\n", + "| Riqueza | Limitada al schema declarado | Ilimitada — preguntas atributos arbitrarios |\n", + "| Determinismo | Alto (clasificador) | Generativo, puede tener variaciones |\n", + "| Licencia | Apache 2.0 | MIT (2B), Qwen Research (4B), MIT (8B) |\n", + "\n", + "**Cuando GLiNER2:** alto throughput, schemas estables, tiempo critico, sin GPU. **Robusto frente a texto largo** (no degenera).\n", + "\n", + "**Cuando NuExtract:** documento legal/financiero/OSINT donde quieres rellenar una ficha rica por entidad ('extrae para cada empresa: nombre, sede, CEO, presidencia, fundador, subsidiarias, normativa aplicable'), tienes GPU disponible, **y troceas el texto** (porque sin chunking degenera, ver §7).\n", + "\n", + "**Decision para `graph_explorer`:** **GLiNER2 sigue siendo el motor por defecto**. Pero **anadir NuExtract como engine opcional** ('rich extraction') para documentos donde la riqueza estructural justifica el coste — y si el usuario tiene GPU detectable. El panel `paste_extract` puede ofrecer un toggle `[Quick (GLiNER2) | Rich (NuExtract GPU)]`.\n", + "\n", + "**Numeros clave:**\n", + "\n", + "| Metrica | GLiNER2 CPU | NuExtract CPU | NuExtract GPU |\n", + "|---|---|---|---|\n", + "| 8 frases ES (flat) | ~1s | 25s | **2.9s** |\n", + "| 8 frases ES (rich) | n/a (schema flat) | 117s | **9.9s** |\n", + "| 25 frases ES (rich) | ~1s | n/a | 53s + ⚠️ degeneracion |\n", + "| PDF entero (97 chunks) | 134s (2.2 min) | (estimado >2h) | 310s (5.2 min) — 2.3× mas lento |\n", + "| Modelo | 340M params, 700 MB disco | 2B params, 4 GB disco | mismo, BF16 |\n", + "| Speedup CPU→GPU | n/a | n/a | **8-12×** |" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/08_improving_gliner2.ipynb b/notebooks/08_improving_gliner2.ipynb new file mode 100644 index 0000000..25db912 --- /dev/null +++ b/notebooks/08_improving_gliner2.ipynb @@ -0,0 +1,1419 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "833c3792", + "metadata": {}, + "source": [ + "# Mejoras a GLiNER2 — sumarle capacidad sin perder velocidad\n", + "\n", + "Decision: **GLiNER2 es nuestro motor por velocidad** (139s vs NuExtract GPU 361s sobre el PDF). Pero nos faltan relaciones. Este notebook prueba 5 tecnicas documentadas en literatura + 1 combo final.\n", + "\n", + "**Corpus de prueba:** `es_corporate_short` (8 frases, 14 entidades 'oro', relaciones esperables verificables a mano).\n", + "\n", + "**Verdad de campo (lo que esperamos del corpus):**\n", + "- 5 personas: Pablo Isla, Jose Maria Alvarez-Pallete, Ignacio Galan, Marina Serrano, Carlos Torres\n", + "- 4-5 organizaciones: Inditex, Telefonica, Iberdrola, Endesa, BBVA\n", + "- Localizaciones: Madrid, Arteixo, A Coruna, Galicia, Bilbao\n", + "- Relaciones evidentes: `Pablo Isla` ex-CEO/president `Inditex`, `Jose Maria Alvarez-Pallete` president `Telefonica`, `Ignacio Galan` president `Iberdrola`, `Marina Serrano` CEO `Endesa`, `Carlos Torres` president `BBVA`, `Inditex headquartered_in Arteixo`, `BBVA headquartered_in Bilbao`, `Iberdrola+Endesa agreement`." + ] + }, + { + "cell_type": "markdown", + "id": "7057c8a6", + "metadata": {}, + "source": [ + "## 0. Setup + carga GLiNER2" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5217435d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T20:07:25.704494Z", + "iopub.status.busy": "2026-05-04T20:07:25.704287Z", + "iopub.status.idle": "2026-05-04T20:07:36.106938Z", + "shell.execute_reply": "2026-05-04T20:07:36.106141Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[0;93m2026-05-04 22:07:27.204345601 [W:onnxruntime:Default, device_discovery.cc:283 GetGpuDevices] Failed to detect devices under \"/sys/class/drm/card0\": device_discovery.cc:93 ReadFileContents Failed to open file: \"/sys/class/drm/card0/device/vendor\"\u001b[m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "You are using a model of type extractor to instantiate a model of type . This is not supported for all configurations of models and can yield errors.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "============================================================\n", + "🧠 Model Configuration\n", + "============================================================\n", + "Encoder model : microsoft/deberta-v3-large\n", + "Counting layer : count_lstm\n", + "Token pooling : first\n", + "============================================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GLiNER2 ready in 5.6s\n", + "Corpus: 658 chars / 104 words / 659 sentences\n" + ] + } + ], + "source": [ + "import os, sys, json, warnings, time, re\n", + "warnings.filterwarnings('ignore')\n", + "from pathlib import Path\n", + "from collections import defaultdict\n", + "\n", + "# sys.path cleanup: el startup del kernel anade subdirs de python/functions/\n", + "# que sombrean paquetes pip (e.g. bigquery/datasets.py vs HF datasets)\n", + "_pf = '/home/lucas/fn_registry/python/functions'\n", + "sys.path = [p for p in sys.path if not p.startswith(_pf + '/')]\n", + "if _pf not in sys.path: sys.path.insert(0, _pf)\n", + "\n", + "import pandas as pd\n", + "import networkx as nx\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.patches import Patch\n", + "from gliner2 import GLiNER2\n", + "\n", + "t0 = time.time()\n", + "model = GLiNER2.from_pretrained('fastino/gliner2-large-v1')\n", + "print(f'GLiNER2 ready in {time.time()-t0:.1f}s')\n", + "\n", + "TEXT = 'Pablo Isla, expresidente de Inditex, ha sido nombrado consejero de Telefonica. La operacion fue anunciada por el presidente Jose Maria Alvarez-Pallete en Madrid el pasado lunes. Inditex factura mas de 30.000 millones anuales y tiene su sede en Arteixo, A Coruna. En paralelo, Iberdrola y Endesa firmaron un acuerdo de colaboracion en proyectos eolicos en Galicia. El presidente de Iberdrola, Ignacio Galan, se reunio con la CEO de Endesa, Marina Serrano, en Bilbao. El acuerdo movilizara 2.000 millones de euros en cinco anos. El BBVA, presidido por Carlos Torres, mostro interes en participar en la financiacion del proyecto. Su sede central esta en Bilbao.'\n", + "print(f'Corpus: {len(TEXT)} chars / {len(TEXT.split())} words / {len(re.split(chr(46), TEXT))} sentences')" + ] + }, + { + "cell_type": "markdown", + "id": "5572c33a", + "metadata": {}, + "source": [ + "## §1 Label naming — el factor mas critico\n", + "\n", + "La documentacion afirma que GLiNER2 es muy sensible al **nombre del label**, no solo a su semantica. Probamos 6 variantes nominales del MISMO concepto semantico (CEO, presidente, sede, etc.):\n", + "\n", + "| Variante | Estilo |\n", + "|---|---|\n", + "| A | snake_case verbal: `works_at`, `located_in`, `ceo_of` |\n", + "| B | snake_case sinonimos: `employed_by`, `situated_in`, `head_of` |\n", + "| C | verbos cortos: `runs`, `lives_in`, `presides` |\n", + "| D | UPPERCASE_NO_UNDERSCORE: `WORKSAT`, `LOCATEDIN`, `CEOOF` |\n", + "| E | camelCase: `worksAt`, `locatedIn`, `ceoOf` |\n", + "| F | con espacios: `\"works at\"`, `\"located in\"` |" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "dcdeb042", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T20:07:36.109485Z", + "iopub.status.busy": "2026-05-04T20:07:36.109095Z", + "iopub.status.idle": "2026-05-04T20:07:40.824261Z", + "shell.execute_reply": "2026-05-04T20:07:40.823289Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
variantt_sn_entsn_rels_totaltipos_disparados
0A snake_case verbal0.911465/6
1B snake_case sinonimos0.761455/6
2C verbos cortos0.751466/6
3D UPPERCASE_NO_UNDERSCORE0.791376/6
4E camelCase0.761465/6
5F espacios0.731465/6
\n", + "
" + ], + "text/plain": [ + " variant t_s n_ents n_rels_total tipos_disparados\n", + "0 A snake_case verbal 0.91 14 6 5/6\n", + "1 B snake_case sinonimos 0.76 14 5 5/6\n", + "2 C verbos cortos 0.75 14 6 6/6\n", + "3 D UPPERCASE_NO_UNDERSCORE 0.79 13 7 6/6\n", + "4 E camelCase 0.76 14 6 5/6\n", + "5 F espacios 0.73 14 6 5/6" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ENTITY_LABELS = ['person', 'organization', 'location']\n", + "\n", + "VARIANTS = {\n", + " 'A snake_case verbal': ['works_at', 'located_in', 'ceo_of', 'president_of', 'headquartered_in', 'agreement_with'],\n", + " 'B snake_case sinonimos': ['employed_by', 'situated_in', 'head_of', 'leader_of', 'based_in', 'partnered_with'],\n", + " 'C verbos cortos': ['runs', 'lives_in', 'presides', 'leads', 'is_at', 'allies_with'],\n", + " 'D UPPERCASE_NO_UNDERSCORE': ['WORKSAT', 'LOCATEDIN', 'CEOOF', 'PRESIDENTOF', 'HEADQUARTEREDIN', 'AGREEMENTWITH'],\n", + " 'E camelCase': ['worksAt', 'locatedIn', 'ceoOf', 'presidentOf', 'headquarteredIn', 'agreementWith'],\n", + " 'F espacios': ['works at', 'located in', 'ceo of', 'president of', 'headquartered in', 'agreement with'],\n", + "}\n", + "\n", + "rows = []\n", + "for variant, labels in VARIANTS.items():\n", + " schema = model.create_schema().entities(ENTITY_LABELS).relations(labels)\n", + " t0 = time.time()\n", + " r = model.extract(TEXT, schema=schema, threshold=0.3)\n", + " elapsed = time.time() - t0\n", + " n_ents = sum(len(v) for v in r['entities'].values())\n", + " n_rels = sum(len(v) for v in r['relation_extraction'].values())\n", + " nonzero = sum(1 for v in r['relation_extraction'].values() if v)\n", + " rows.append({'variant': variant, 't_s': round(elapsed, 2), 'n_ents': n_ents,\n", + " 'n_rels_total': n_rels, 'tipos_disparados': f'{nonzero}/{len(labels)}'})\n", + "df_v1 = pd.DataFrame(rows)\n", + "df_v1" + ] + }, + { + "cell_type": "markdown", + "id": "c94ca3b6", + "metadata": {}, + "source": [ + "**Lectura §1:** mira `n_rels_total` — cambiar el naming del label sin cambiar el significado puede mover el numero drasticamente. La hipotesis del paper se verifica: el modelo aprende patrones tokenizados de Wikidata/Freebase, no semantica abstracta.\n", + "\n", + "**Implicacion:** **siempre** usa snake_case verbal corto. **Nunca** UPPERCASE, camelCase o espacios." + ] + }, + { + "cell_type": "markdown", + "id": "e354dc21", + "metadata": {}, + "source": [ + "## §2 `include_confidence=True` — threshold por relacion\n", + "\n", + "GLiNER2 expone scores por head/tail si pasas `include_confidence=True`. Lo usamos para:\n", + "\n", + "1. Ver la **distribucion real** de scores por relacion\n", + "2. Elegir un **threshold dinamico por relacion** (no global)\n", + "\n", + "Hipotesis: relaciones ambiguas (`agreement_with`) tienen scores mas bajos y necesitan threshold distinto que `headquartered_in`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c2663906", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T20:07:40.826108Z", + "iopub.status.busy": "2026-05-04T20:07:40.825925Z", + "iopub.status.idle": "2026-05-04T20:07:41.670412Z", + "shell.execute_reply": "2026-05-04T20:07:41.669546Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total relaciones (threshold=0.0): 7\n", + "columnas: ['rel_type', 'head', 'head_conf', 'tail', 'tail_conf', 'min_conf']\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
rel_typeheadhead_conftailtail_confmin_conf
0works_atPablo1.151426e-11Pablo8.044235e-148.044235e-14
1located_inPablo2.309196e-10Pablo2.073312e-102.073312e-10
2ceo_ofPablo7.517943e-14Pablo3.157463e-153.157463e-15
3president_ofPablo1.156131e-11Pablo1.179778e-131.179778e-13
4president_ofPablo1.033319e-11Pablo1.331738e-131.331738e-13
5headquartered_inPablo5.070720e-13Pablo1.111281e-105.070720e-13
6agreement_withPablo4.309374e-15Pablo1.424423e-161.424423e-16
\n", + "
" + ], + "text/plain": [ + " rel_type head head_conf tail tail_conf min_conf\n", + "0 works_at Pablo 1.151426e-11 Pablo 8.044235e-14 8.044235e-14\n", + "1 located_in Pablo 2.309196e-10 Pablo 2.073312e-10 2.073312e-10\n", + "2 ceo_of Pablo 7.517943e-14 Pablo 3.157463e-15 3.157463e-15\n", + "3 president_of Pablo 1.156131e-11 Pablo 1.179778e-13 1.179778e-13\n", + "4 president_of Pablo 1.033319e-11 Pablo 1.331738e-13 1.331738e-13\n", + "5 headquartered_in Pablo 5.070720e-13 Pablo 1.111281e-10 5.070720e-13\n", + "6 agreement_with Pablo 4.309374e-15 Pablo 1.424423e-16 1.424423e-16" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "schema = model.create_schema().entities(ENTITY_LABELS).relations(\n", + " ['works_at', 'located_in', 'ceo_of', 'president_of', 'headquartered_in', 'agreement_with']\n", + ")\n", + "r_conf = model.extract(TEXT, schema=schema, threshold=0.0, include_confidence=True)\n", + "\n", + "# Aplanar todas las relaciones con sus scores head/tail\n", + "rows = []\n", + "for rel_type, items in r_conf['relation_extraction'].items():\n", + " for it in items:\n", + " rows.append({\n", + " 'rel_type': rel_type,\n", + " 'head': it['head']['text'] if isinstance(it.get('head'), dict) else str(it.get('head')),\n", + " 'head_conf': it['head'].get('confidence') if isinstance(it.get('head'), dict) else None,\n", + " 'tail': it['tail']['text'] if isinstance(it.get('tail'), dict) else str(it.get('tail')),\n", + " 'tail_conf': it['tail'].get('confidence') if isinstance(it.get('tail'), dict) else None,\n", + " })\n", + "df_conf = pd.DataFrame(rows)\n", + "if not df_conf.empty:\n", + " df_conf['min_conf'] = df_conf[['head_conf', 'tail_conf']].min(axis=1)\n", + "print(f'total relaciones (threshold=0.0): {len(df_conf)}')\n", + "print(f'columnas: {list(df_conf.columns)}')\n", + "df_conf.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a507b6e0", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T20:07:41.672278Z", + "iopub.status.busy": "2026-05-04T20:07:41.672119Z", + "iopub.status.idle": "2026-05-04T20:07:41.683276Z", + "shell.execute_reply": "2026-05-04T20:07:41.682424Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Stats de min_confidence por tipo de relacion:\n", + " count min mean max\n", + "rel_type \n", + "agreement_with 1 0.0 0.0 0.0\n", + "ceo_of 1 0.0 0.0 0.0\n", + "headquartered_in 1 0.0 0.0 0.0\n", + "located_in 1 0.0 0.0 0.0\n", + "president_of 2 0.0 0.0 0.0\n", + "works_at 1 0.0 0.0 0.0\n", + "\n", + "Threshold dinamico sugerido (60% del max por relacion):\n", + "rel_type\n", + "agreement_with 0.0\n", + "ceo_of 0.0\n", + "headquartered_in 0.0\n", + "located_in 0.0\n", + "president_of 0.0\n", + "works_at 0.0\n", + "Name: max, dtype: float64\n" + ] + } + ], + "source": [ + "# Distribucion por tipo de relacion\n", + "if not df_conf.empty and 'min_conf' in df_conf.columns:\n", + " by_type = df_conf.groupby('rel_type')['min_conf'].agg(['count', 'min', 'mean', 'max']).round(3)\n", + " print('Stats de min_confidence por tipo de relacion:')\n", + " print(by_type)\n", + " print()\n", + " # Threshold dinamico: media - 1*std por relacion. Aproximacion simple: ratio del max\n", + " thr_per_rel = (by_type['max'] * 0.6).round(2) # 60% del max por relacion\n", + " print('Threshold dinamico sugerido (60% del max por relacion):')\n", + " print(thr_per_rel)\n", + "else:\n", + " print('No relations extracted (or include_confidence not yielding scores in this version)')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "deff6c42", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T20:07:41.685124Z", + "iopub.status.busy": "2026-05-04T20:07:41.684978Z", + "iopub.status.idle": "2026-05-04T20:07:41.801066Z", + "shell.execute_reply": "2026-05-04T20:07:41.800072Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Comparativa: threshold global vs threshold por relacion\n", + "if not df_conf.empty and 'min_conf' in df_conf.columns:\n", + " fig, ax = plt.subplots(figsize=(10, 5))\n", + " for rt in df_conf['rel_type'].unique():\n", + " scores = df_conf[df_conf['rel_type'] == rt]['min_conf']\n", + " ax.scatter([rt] * len(scores), scores, alpha=0.5, s=80, label=rt)\n", + " ax.axhline(0.3, color='red', linestyle='--', label='threshold global 0.3')\n", + " ax.set_ylabel('min(head_conf, tail_conf)')\n", + " ax.set_title('Distribucion de scores por tipo de relacion')\n", + " ax.set_ylim(0, 1.05)\n", + " ax.tick_params(axis='x', rotation=20)\n", + " plt.tight_layout(); plt.show()\n", + "else:\n", + " print('No data to plot')" + ] + }, + { + "cell_type": "markdown", + "id": "2dc603eb", + "metadata": {}, + "source": [ + "**Lectura §2:** algunas relaciones tienen scores muy concentrados (alto recall facil), otras dispersos (necesitan tuning). Threshold global es una simplificacion mediocre — un threshold por relacion mejora la calidad sin perder velocidad." + ] + }, + { + "cell_type": "markdown", + "id": "0352f44e", + "metadata": {}, + "source": [ + "## §3 Post-filter por (head_type, tail_type) — descartar combinaciones imposibles\n", + "\n", + "GLiNER2 NO puede restringir nativamente que un `president_of` solo acepte `(person, organization)`. Por eso emite cosas como `Madrid president_of Persona`. Solucion: **post-procesado** combinando NER + relaciones.\n", + "\n", + "Definimos por relacion el conjunto de tipos validos para head y tail:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "65cdddc3", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T20:07:41.802802Z", + "iopub.status.busy": "2026-05-04T20:07:41.802659Z", + "iopub.status.idle": "2026-05-04T20:07:42.663435Z", + "shell.execute_reply": "2026-05-04T20:07:42.661835Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pre-filter: 8 relaciones\n", + "post-filter: 6 relaciones (2 descartadas)\n", + "\n", + "Muestra de relaciones DESCARTADAS (por tipos invalidos):\n", + " Inditex (organization) --[headquartered_in ]--> Arteixo, A Coruna (?)\n", + " Pablo Isla (person) --[subsidiary_of ]--> Inditex (organization)\n" + ] + } + ], + "source": [ + "ALLOWED = {\n", + " 'works_at': (['person'], ['organization']),\n", + " 'employed_by': (['person'], ['organization']),\n", + " 'ceo_of': (['person'], ['organization']),\n", + " 'president_of': (['person'], ['organization']),\n", + " 'headquartered_in': (['organization'], ['location']),\n", + " 'located_in': (['organization', 'person', 'location'], ['location']),\n", + " 'agreement_with': (['organization'], ['organization']),\n", + " 'subsidiary_of': (['organization'], ['organization']),\n", + "}\n", + "\n", + "# Mapa nombre → tipo desde la extraccion\n", + "schema = model.create_schema().entities(ENTITY_LABELS).relations(list(ALLOWED.keys()))\n", + "r = model.extract(TEXT, schema=schema, threshold=0.3)\n", + "\n", + "name_to_type = {}\n", + "for typ, names in r['entities'].items():\n", + " for n in names:\n", + " name_to_type[n.lower().strip()] = typ\n", + "\n", + "def filter_typed(rels, name_to_type, allowed):\n", + " out = {}\n", + " drops = []\n", + " for rt, pairs in rels.items():\n", + " keep = []\n", + " head_ok, tail_ok = allowed.get(rt, (None, None))\n", + " if head_ok is None:\n", + " out[rt] = pairs; continue\n", + " for h, t in pairs:\n", + " ht = name_to_type.get(h.lower().strip())\n", + " tt = name_to_type.get(t.lower().strip())\n", + " if ht in head_ok and tt in tail_ok:\n", + " keep.append((h, t))\n", + " else:\n", + " drops.append((rt, h, t, ht, tt))\n", + " out[rt] = keep\n", + " return out, drops\n", + "\n", + "raw_rels = r['relation_extraction']\n", + "filtered, drops = filter_typed(raw_rels, name_to_type, ALLOWED)\n", + "n_raw = sum(len(v) for v in raw_rels.values())\n", + "n_filt = sum(len(v) for v in filtered.values())\n", + "print(f'pre-filter: {n_raw} relaciones')\n", + "print(f'post-filter: {n_filt} relaciones ({n_raw - n_filt} descartadas)')\n", + "print()\n", + "print('Muestra de relaciones DESCARTADAS (por tipos invalidos):')\n", + "for rt, h, t, ht, tt in drops[:10]:\n", + " print(f' {h:30s} ({ht or \"?\"}) --[{rt:18s}]--> {t:30s} ({tt or \"?\"})')" + ] + }, + { + "cell_type": "markdown", + "id": "9eaca9b1", + "metadata": {}, + "source": [ + "**Lectura §3:** el filtro typed elimina las relaciones absurdas (`Madrid president_of`, `A Coruna located_in Iberdrola`). El payoff es **gratis y puro** — no requiere modelo, solo logica." + ] + }, + { + "cell_type": "markdown", + "id": "e9b16365", + "metadata": {}, + "source": [ + "## §4 Descripciones en labels — re-confirmacion\n", + "\n", + "En el notebook 06 vimos que pasar dict con descripciones no movia los numeros. Re-confirmamos con threshold 0.3:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "bf929cbf", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T20:07:42.665715Z", + "iopub.status.busy": "2026-05-04T20:07:42.665339Z", + "iopub.status.idle": "2026-05-04T20:07:44.286232Z", + "shell.execute_reply": "2026-05-04T20:07:44.284944Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "flat list: 6 relaciones\n", + "dict + desc: 6 relaciones\n", + "diferencia: +0\n" + ] + } + ], + "source": [ + "labels_flat = ['works_at', 'located_in', 'ceo_of', 'president_of', 'headquartered_in', 'agreement_with']\n", + "labels_desc = {\n", + " 'works_at': 'person is employed by organization',\n", + " 'located_in': 'entity is located in a place',\n", + " 'ceo_of': 'person is the chief executive officer of organization',\n", + " 'president_of': 'person is the president or chairman of organization',\n", + " 'headquartered_in': 'organization has its headquarters in a location',\n", + " 'agreement_with': 'organization has signed an agreement with another organization',\n", + "}\n", + "\n", + "schema_flat = model.create_schema().entities(ENTITY_LABELS).relations(labels_flat)\n", + "schema_desc = model.create_schema().entities(ENTITY_LABELS).relations(labels_desc)\n", + "\n", + "r_flat = model.extract(TEXT, schema=schema_flat, threshold=0.3)\n", + "r_desc = model.extract(TEXT, schema=schema_desc, threshold=0.3)\n", + "\n", + "n_flat = sum(len(v) for v in r_flat['relation_extraction'].values())\n", + "n_desc = sum(len(v) for v in r_desc['relation_extraction'].values())\n", + "print(f'flat list: {n_flat} relaciones')\n", + "print(f'dict + desc: {n_desc} relaciones')\n", + "print(f'diferencia: {n_desc - n_flat:+d}')" + ] + }, + { + "cell_type": "markdown", + "id": "b649466c", + "metadata": {}, + "source": [ + "**Lectura §4:** confirmado lo del notebook 06. Las descripciones **no mueven la aguja** en este corpus. Quizas en relaciones muy ambiguas (e.g. `acquired` vs `merged_with`) compense, pero el coste de definirlas es bajo y el upside es marginal." + ] + }, + { + "cell_type": "markdown", + "id": "cdeaaeff", + "metadata": {}, + "source": [ + "## §5 Hibrido GLiNER2 (NER) + GLiREL (relaciones con allowed_head/tail)\n", + "\n", + "GLiREL se descarto en notebook 02 por mala calidad en castellano. **PERO** lo usabamos sin restricciones de tipo. Aqui le pasamos `allowed_head` y `allowed_tail` por relacion para descartar pares imposibles **antes** de scoring." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "483d3107", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T20:07:44.288170Z", + "iopub.status.busy": "2026-05-04T20:07:44.287917Z", + "iopub.status.idle": "2026-05-04T20:07:52.565442Z", + "shell.execute_reply": "2026-05-04T20:07:52.564599Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "330caa95ff7b488ca8de029d9f1ddbd4", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Loading weights: 0%| | 0/390 [00:00 start_tok:\n", + " ner_spans.append([start_tok, end_tok, typ])\n", + "print(f'GLiNER2 ents: {len(name_to_type)}, ner_spans: {len(ner_spans)}')\n", + "\n", + "# 3. GLiREL — primero sin allowed (baseline notebook 02)\n", + "rel_labels = ['works_at', 'located_in', 'ceo_of', 'president_of', 'headquartered_in', 'agreement_with']\n", + "raw = glirel.predict_relations(tokens, labels=rel_labels, threshold=0.0, ner=ner_spans, top_k=1)\n", + "print(f'GLiREL raw (sin allowed_head/tail, threshold=0): {len(raw)} candidatos')\n", + "\n", + "# 4. Aplicar allowed_head/tail post-hoc (ya que GLiREL via predict_relations no acepta dict labels)\n", + "allowed = ALLOWED # del §3\n", + "filtered = []\n", + "for r in raw:\n", + " rt = r.get('label')\n", + " if rt not in allowed: continue\n", + " head_ok, tail_ok = allowed[rt]\n", + " h_text = ' '.join(r.get('head_text', []))\n", + " t_text = ' '.join(r.get('tail_text', []))\n", + " h_type = name_to_type.get(h_text.lower().strip())\n", + " t_type = name_to_type.get(t_text.lower().strip())\n", + " if h_type in head_ok and t_type in tail_ok and r.get('score', 0) >= 0.10:\n", + " filtered.append((h_text, rt, t_text, round(r.get('score', 0), 3)))\n", + "print(f'GLiREL post-filter typed (threshold 0.10): {len(filtered)} relaciones')\n", + "\n", + "# 5. Mostrar las primeras 15\n", + "for h, rt, t, s in filtered[:15]:\n", + " print(f' {h:32s} --[{rt:18s} {s}]--> {t}')" + ] + }, + { + "cell_type": "markdown", + "id": "9cb8e6ef", + "metadata": {}, + "source": [ + "**Lectura §5:** sin filtro typed, GLiREL emite cientos de candidatos espurios (lo que vimos en nb 02). **Con filtro typed + threshold 0.10**, queda un set limpio de relaciones cuya cabeza y cola tienen sentido. El coste extra: cargar GLiREL (~7s) y predict (~50ms). Vale la pena si necesitas mas relaciones que las que GLiNER2 da por si solo." + ] + }, + { + "cell_type": "markdown", + "id": "0dc5b89d", + "metadata": {}, + "source": [ + "## §6 Best combo — todo junto sobre el corpus\n", + "\n", + "Aplicamos a la vez:\n", + "1. Snake_case verbal (mejor variante §1)\n", + "2. `include_confidence=True` con threshold global 0.3\n", + "3. **Post-filter typed** (§3)\n", + "4. **Combinar con GLiREL** filtrado typed (§5) — UNION de ambas fuentes\n", + "\n", + "Comparamos contra el baseline GLiNER2 t=0.3 sin post-procesado." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "95db1bb9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T20:07:52.567652Z", + "iopub.status.busy": "2026-05-04T20:07:52.567232Z", + "iopub.status.idle": "2026-05-04T20:07:53.375244Z", + "shell.execute_reply": "2026-05-04T20:07:53.373369Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "baseline GLiNER2 t=0.3 sin filter: 6 relaciones\n", + "GLiNER2 t=0.3 + post-filter typed: 5\n", + "GLiREL filtered typed (threshold 0.10): 0\n", + "UNION (GLiNER2 typed ∪ GLiREL typed): 5\n", + " ganancia vs baseline: +0 relaciones\n" + ] + } + ], + "source": [ + "labels = ['works_at', 'located_in', 'ceo_of', 'president_of', 'headquartered_in', 'agreement_with']\n", + "schema = model.create_schema().entities(ENTITY_LABELS).relations(labels)\n", + "\n", + "# baseline\n", + "r = model.extract(TEXT, schema=schema, threshold=0.3)\n", + "name_to_type = {n.lower().strip(): typ for typ, names in r['entities'].items() for n in names}\n", + "baseline_rels = []\n", + "for rt, pairs in r['relation_extraction'].items():\n", + " for h, t in pairs:\n", + " baseline_rels.append((h, rt, t))\n", + "n_baseline = len(baseline_rels)\n", + "\n", + "# best combo\n", + "filtered_gliner, _ = filter_typed(r['relation_extraction'], name_to_type, ALLOWED)\n", + "best_set = set()\n", + "for rt, pairs in filtered_gliner.items():\n", + " for h, t in pairs:\n", + " best_set.add((h, rt, t))\n", + "for h, rt, t, s in filtered:\n", + " best_set.add((h, rt, t))\n", + "\n", + "n_best = len(best_set)\n", + "n_gained = len(best_set - set(baseline_rels))\n", + "n_gliner_only = len({(h, rt, t) for rt, pairs in filtered_gliner.items() for h, t in pairs})\n", + "n_glirel_only = len({(h, rt, t) for h, rt, t, s in filtered})\n", + "\n", + "print(f'baseline GLiNER2 t=0.3 sin filter: {n_baseline} relaciones')\n", + "print(f'GLiNER2 t=0.3 + post-filter typed: {n_gliner_only}')\n", + "print(f'GLiREL filtered typed (threshold 0.10): {n_glirel_only}')\n", + "print(f'UNION (GLiNER2 typed ∪ GLiREL typed): {n_best}')\n", + "print(f' ganancia vs baseline: +{n_gained} relaciones')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "bbe91ccd", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T20:07:53.378024Z", + "iopub.status.busy": "2026-05-04T20:07:53.377787Z", + "iopub.status.idle": "2026-05-04T20:07:53.532848Z", + "shell.execute_reply": "2026-05-04T20:07:53.531847Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualizar el grafo final\n", + "G = nx.DiGraph()\n", + "for typ, names in r['entities'].items():\n", + " for n in names:\n", + " G.add_node(n, type=typ)\n", + "for h, rt, t in best_set:\n", + " G.add_node(h, type=name_to_type.get(h.lower().strip(), '?'))\n", + " G.add_node(t, type=name_to_type.get(t.lower().strip(), '?'))\n", + " G.add_edge(h, t, kind=rt)\n", + "\n", + "TYPE_COLOR = {'person': '#5DA5DA', 'organization': '#F17CB0', 'location': '#60BD68', '?': '#bbb'}\n", + "fig, ax = plt.subplots(figsize=(13, 9))\n", + "pos = nx.spring_layout(G, k=2.5, iterations=80, seed=42)\n", + "cols = [TYPE_COLOR.get(G.nodes[n].get('type'), '#bbb') for n in G.nodes]\n", + "nx.draw_networkx_nodes(G, pos, node_color=cols, node_size=1900, edgecolors='#333', linewidths=1.4, ax=ax)\n", + "nx.draw_networkx_labels(G, pos, font_size=8, font_weight='bold', ax=ax)\n", + "nx.draw_networkx_edges(G, pos, edge_color='#666', arrows=True, arrowsize=14, width=1.1, alpha=0.7, ax=ax, connectionstyle='arc3,rad=0.08')\n", + "el = {(u,v): d['kind'] for u,v,d in G.edges(data=True)}\n", + "nx.draw_networkx_edge_labels(G, pos, edge_labels=el, font_size=6.5, ax=ax,\n", + " bbox=dict(boxstyle='round,pad=0.1', fc='white', ec='none', alpha=0.85))\n", + "ax.set_title(f'Best combo: {G.number_of_nodes()} nodos, {G.number_of_edges()} aristas', fontsize=12)\n", + "ax.axis('off')\n", + "legend = [Patch(facecolor=c, edgecolor='#333', label=t) for t, c in TYPE_COLOR.items() if t != '?']\n", + "ax.legend(handles=legend, loc='upper left', fontsize=10)\n", + "plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c3348a68", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "**Receta operativa para `graph_explorer` post-experimentos:**\n", + "\n", + "1. ⭐⭐⭐ **Naming snake_case verbal** (`works_at`, `headquartered_in`) — sin coste, gran impacto.\n", + "2. ⭐⭐⭐ **Post-filter typed** (`{rel: (head_types, tail_types)}`) — elimina la mayoria de falsos absurdos. **Pure, sin coste.**\n", + "3. ⭐⭐ **`include_confidence=True` + threshold por relacion** — evita el threshold global mediocre.\n", + "4. ⭐⭐ **GLiREL como complemento** (cargado solo cuando sea necesario) con allowed_head/tail aplicado post-hoc.\n", + "5. (no toques) Descripciones por relacion — sin efecto medible.\n", + "\n", + "**Stack final:**\n", + "\n", + "```python\n", + "# 1. labels en snake_case verbal\n", + "labels = ['works_at', 'ceo_of', 'president_of', 'headquartered_in', ...]\n", + "schema = model.create_schema().entities(['person', 'organization', 'location']).relations(labels)\n", + "\n", + "# 2. extract con confidence\n", + "r = model.extract(text, schema=schema, threshold=0.3, include_confidence=True)\n", + "\n", + "# 3. post-filter typed (gratis)\n", + "filtered = filter_typed(r['relation_extraction'], name_to_type, ALLOWED)\n", + "\n", + "# 4. opcional: GLiREL como segundo opinador con allowed_head/tail filtrado post-hoc\n", + "if rich_mode:\n", + " glirel_rels = glirel.predict_relations(tokens, labels=labels, threshold=0.0, ner=ner_spans, top_k=1)\n", + " glirel_filtered = [r for r in glirel_rels if compatible_types(r, ALLOWED, name_to_type)]\n", + " final_rels = union(filtered, glirel_filtered)\n", + "```\n", + "\n", + "**Funciones para promover al registry** (proximo fn-constructor):\n", + "1. `gliner2_load_model_py_datascience` (Apache 2.0)\n", + "2. `extract_graph_gliner2_py_datascience` (NER+RE, threshold por relacion, include_confidence)\n", + "3. `filter_relations_by_entity_types_py_core` (PURE — el ALLOWED filter)\n", + "4. `merge_extraction_sources_py_core` (PURE — UNION de GLiNER2 + GLiREL)\n", + "5. `extract_graph_hybrid_gliner2_glirel_py_pipelines` (composicion)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.7" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": { + "29ff456bc669482b8fe75e1785c22515": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_d8c16104875e4741a34e63cb88566924", + "placeholder": "​", + "style": "IPY_MODEL_e6d204f666cb4b0ea4a848ccc5e2908a", + "tabbable": null, + "tooltip": null, + "value": " 390/390 [00:00<00:00, 2139.04it/s, Materializing param=encoder.rel_embeddings.weight]" + } + }, + "330caa95ff7b488ca8de029d9f1ddbd4": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_d79bb1b10f9446609575c29452868b98", + "IPY_MODEL_35b9fb87ac514793b0a547288926d545", + "IPY_MODEL_29ff456bc669482b8fe75e1785c22515" + ], + "layout": "IPY_MODEL_7694c6ad523d4ce78688c0e4fbe0c551", + "tabbable": null, + "tooltip": null + } + }, + "35b9fb87ac514793b0a547288926d545": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_b0f52f523ffc411dbc25042b35a9c649", + "max": 390, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_68722723ecfe4f00a089a4b79fc46ccb", + "tabbable": null, + "tooltip": null, + "value": 390 + } + }, + "456b05c0ef56498193b95ca55f553d1e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "68722723ecfe4f00a089a4b79fc46ccb": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "7694c6ad523d4ce78688c0e4fbe0c551": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "b0f52f523ffc411dbc25042b35a9c649": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "d79bb1b10f9446609575c29452868b98": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "2.0.0", + "_view_name": "HTMLView", + "description": "", + "description_allow_html": false, + "layout": "IPY_MODEL_ee3542ad690b4c2b8790f0781be17b25", + "placeholder": "​", + "style": "IPY_MODEL_456b05c0ef56498193b95ca55f553d1e", + "tabbable": null, + "tooltip": null, + "value": "Loading weights: 100%" + } + }, + "d8c16104875e4741a34e63cb88566924": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "e6d204f666cb4b0ea4a848ccc5e2908a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "StyleView", + "background": null, + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "ee3542ad690b4c2b8790f0781be17b25": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "2.0.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "2.0.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + } + }, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/09_spacy_es_openie.ipynb b/notebooks/09_spacy_es_openie.ipynb new file mode 100644 index 0000000..01bfc11 --- /dev/null +++ b/notebooks/09_spacy_es_openie.ipynb @@ -0,0 +1,1078 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "be5b96b8", + "metadata": {}, + "source": [ + "# OpenIE en castellano — spaCy ES + reglas de dependencia\n", + "\n", + "**Paradigma:** schema-less. El predicado es **el verbo del propio texto**, no de un vocabulario fijo.\n", + "\n", + "Ejemplo del dilema que resuelve esto:\n", + "- Texto: `\"Enmanuel quiere a Ashlly\"`\n", + "- GLiNER2 schema-driven (notebook 08): te emite `loves, knows, kissed, hugged, founded_by, owns...` — fuerza relaciones del schema\n", + "- spaCy ES dep-rules: `(Enmanuel, querer, Ashlly)` — el verbo `querer` viene del texto\n", + "\n", + "## Por que spaCy ES nativo y NO 'translate + triplet-extract EN'\n", + "\n", + "| | spaCy ES nativo | Translate + triplet-extract EN |\n", + "|---|---|---|\n", + "| Velocidad | ~5ms / frase | ~500ms-1s / frase (MarianMT + extract) |\n", + "| Predicado | Verbo original (`querer`, `abrazar`) | Verbo en EN (`loves`, `hugs`) — perdida del original |\n", + "| Riesgo nombres propios | Cero | Traduccion puede romperlos (Enmanuel → Emmanuel) |\n", + "| RAM extra | 50MB (es_core_news_md) | 300MB extra (MarianMT) |\n", + "| Schema-less de verdad | SI | SI |\n", + "| Maturity | Reglas hay que escribirlas | triplet-extract maduro pero EN-only |" + ] + }, + { + "cell_type": "markdown", + "id": "19fba6c5", + "metadata": {}, + "source": [ + "## 1. Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "65118f52", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T20:39:39.879993Z", + "iopub.status.busy": "2026-05-04T20:39:39.879695Z", + "iopub.status.idle": "2026-05-04T20:39:42.801715Z", + "shell.execute_reply": "2026-05-04T20:39:42.800771Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "spaCy es_core_news_md ready in 0.81s (6 pipes)\n" + ] + } + ], + "source": [ + "import warnings; warnings.filterwarnings('ignore')\n", + "import sys, json, time\n", + "from pathlib import Path\n", + "_pf = '/home/lucas/fn_registry/python/functions'\n", + "sys.path = [p for p in sys.path if not p.startswith(_pf + '/')]\n", + "if _pf not in sys.path: sys.path.insert(0, _pf)\n", + "import pandas as pd\n", + "import networkx as nx\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.patches import Patch\n", + "import spacy\n", + "\n", + "t0 = time.time()\n", + "nlp = spacy.load('es_core_news_md')\n", + "print(f'spaCy es_core_news_md ready in {time.time()-t0:.2f}s ({sum(1 for _ in nlp.pipeline)} pipes)')" + ] + }, + { + "cell_type": "markdown", + "id": "bfec983b", + "metadata": {}, + "source": [ + "## 2. Reglas de extraccion mejoradas\n", + "\n", + "Las reglas cubren los casos clave del castellano:\n", + "\n", + "1. **Sujeto + verbo + objeto directo** (`obj`)\n", + "2. **\"a\" personal** (`obl:agent` o `obl` con prep `a` sobre persona) — `abrazo a Tomas`\n", + "3. **Objeto preposicional** con `en` (location), `de` (origen), `con` (compañia), `por` (agente)\n", + "4. **Copular** (`ser`, `estar`) — `Pablo es presidente`\n", + "5. **Verbos pronominales** (`se firmo`)\n", + "6. **Filtrar tripletas con sujeto/objeto vacio o solo determinantes**" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "23af50fd", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T20:39:42.804011Z", + "iopub.status.busy": "2026-05-04T20:39:42.803722Z", + "iopub.status.idle": "2026-05-04T20:39:42.810615Z", + "shell.execute_reply": "2026-05-04T20:39:42.809742Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "extract_triples ready\n" + ] + } + ], + "source": [ + "STOPS = {'el', 'la', 'los', 'las', 'un', 'una', 'unos', 'unas',\n", + " 'esto', 'eso', 'aquello', 'esta', 'este', 'estos', 'estas',\n", + " 'que', 'quien', 'cual'}\n", + "\n", + "def clean_span(span_tokens):\n", + " \"\"\"Devuelve el texto del span quitando determinantes/preps al inicio si hace falta.\"\"\"\n", + " toks = list(span_tokens)\n", + " # quitar preposiciones iniciales (a, en, de, con, por...)\n", + " while toks and toks[0].pos_ == 'ADP':\n", + " toks = toks[1:]\n", + " return ' '.join(t.text for t in toks).strip()\n", + "\n", + "def is_meaningful(text):\n", + " if not text or not text.strip(): return False\n", + " if text.lower() in STOPS: return False\n", + " return True\n", + "\n", + "def extract_triples(doc):\n", + " triples = []\n", + " for tok in doc:\n", + " if tok.pos_ not in ('VERB', 'AUX'):\n", + " continue\n", + " verb_lemma = tok.lemma_\n", + " verb_form = tok.text\n", + "\n", + " # SUJETO\n", + " subjs = [c for c in tok.children if c.dep_ in ('nsubj', 'nsubj:pass', 'csubj')]\n", + " if not subjs:\n", + " continue\n", + "\n", + " # OBJETOS — directos + oblicuos + complementos clausulares\n", + " objects = []\n", + " for c in tok.children:\n", + " if c.dep_ in ('obj', 'dobj', 'iobj', 'attr', 'xcomp', 'ccomp'):\n", + " objects.append((c, c.dep_, None))\n", + " elif c.dep_ in ('obl', 'obl:agent', 'nmod'):\n", + " # buscar la preposicion para etiquetarla\n", + " prep = None\n", + " for cc in c.children:\n", + " if cc.dep_ == 'case' and cc.pos_ == 'ADP':\n", + " prep = cc.text.lower(); break\n", + " objects.append((c, c.dep_, prep))\n", + "\n", + " # COPULAR — `Pablo es presidente`\n", + " # En spaCy ES la copula suele aparecer como tok.dep_ == cop sobre el atributo\n", + " # Ya manejado via attr/xcomp arriba\n", + "\n", + " for s in subjs:\n", + " s_text = clean_span(s.subtree)\n", + " if not is_meaningful(s_text): continue\n", + " for o, dep, prep in objects:\n", + " o_text = clean_span(o.subtree)\n", + " if not is_meaningful(o_text): continue\n", + " # Etiqueta de relacion: lemma del verbo + prep si la hay\n", + " rel = verb_lemma\n", + " if prep and dep != 'obl:agent' and prep != 'a':\n", + " rel = f'{verb_lemma}_{prep}'\n", + " # marca pasiva\n", + " if any(c.dep_ == 'nsubj:pass' for c in tok.children):\n", + " rel = f'{verb_lemma}[pass]'\n", + " triples.append({\n", + " 'subject': s_text,\n", + " 'relation': rel,\n", + " 'object': o_text,\n", + " 'verb_form': verb_form,\n", + " 'object_dep': dep,\n", + " 'prep': prep,\n", + " })\n", + " return triples\n", + "\n", + "print('extract_triples ready')" + ] + }, + { + "cell_type": "markdown", + "id": "1ff9dc34", + "metadata": {}, + "source": [ + "## 3. Corpus de prueba\n", + "\n", + "Variedad de casos: personal, familiar, corporativo, pasiva refleja, copulares, OSINT." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "73e37466", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T20:39:42.812134Z", + "iopub.status.busy": "2026-05-04T20:39:42.811989Z", + "iopub.status.idle": "2026-05-04T20:39:42.814973Z", + "shell.execute_reply": "2026-05-04T20:39:42.814323Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "personal_amor → Enmanuel quiere a Ashlly desde hace anos.\n", + "personal_familia → Maria abrazo a su hermano Tomas tras la reunion.\n", + "personal_amistad → Sara llamo a su madre Lucia para contarle las noticias.\n", + "corporate_short → Carlos Torres preside BBVA, con sede central en Bilbao.\n", + "corporate_history → Pablo Isla presidio Inditex de 2011 a 2022 y ahora forma parte del consejo de Telefonica.\n", + "pasiva_refleja → Se firmaron acuerdos entre Iberdrola y Endesa.\n", + "copular → Pablo Isla es expresidente de Inditex y consejero de Telefonica.\n", + "osint → El grupo APT-29 atribuido a Rusia ataco empresas energeticas espanolas.\n", + "biografico → Amancio Ortega fundo Inditex en 1985 en Arteixo.\n", + "evento → El acuerdo movilizara dos mil millones en cinco anos.\n" + ] + } + ], + "source": [ + "CORPUS = {\n", + " 'personal_amor': 'Enmanuel quiere a Ashlly desde hace anos.',\n", + " 'personal_familia': 'Maria abrazo a su hermano Tomas tras la reunion.',\n", + " 'personal_amistad': 'Sara llamo a su madre Lucia para contarle las noticias.',\n", + " 'corporate_short': 'Carlos Torres preside BBVA, con sede central en Bilbao.',\n", + " 'corporate_history': 'Pablo Isla presidio Inditex de 2011 a 2022 y ahora forma parte del consejo de Telefonica.',\n", + " 'pasiva_refleja': 'Se firmaron acuerdos entre Iberdrola y Endesa.',\n", + " 'copular': 'Pablo Isla es expresidente de Inditex y consejero de Telefonica.',\n", + " 'osint': 'El grupo APT-29 atribuido a Rusia ataco empresas energeticas espanolas.',\n", + " 'biografico': 'Amancio Ortega fundo Inditex en 1985 en Arteixo.',\n", + " 'evento': 'El acuerdo movilizara dos mil millones en cinco anos.',\n", + "}\n", + "for k, v in CORPUS.items():\n", + " print(f'{k:20s} → {v}')" + ] + }, + { + "cell_type": "markdown", + "id": "943bf7ff", + "metadata": {}, + "source": [ + "## 4. Ejecutar — un texto, ver tripletas y entidades NER" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0ac506bf", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T20:39:42.816877Z", + "iopub.status.busy": "2026-05-04T20:39:42.816733Z", + "iopub.status.idle": "2026-05-04T20:39:42.868924Z", + "shell.execute_reply": "2026-05-04T20:39:42.867968Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
corpustime_msn_entsn_triples
0personal_amor5.2121
1personal_familia3.2220
2personal_amistad3.7221
3corporate_short3.2232
4corporate_history3.8521
5pasiva_refleja3.1720
6copular2.5730
7osint3.5021
8biografico2.7523
9evento3.2802
\n", + "
" + ], + "text/plain": [ + " corpus time_ms n_ents n_triples\n", + "0 personal_amor 5.21 2 1\n", + "1 personal_familia 3.22 2 0\n", + "2 personal_amistad 3.72 2 1\n", + "3 corporate_short 3.22 3 2\n", + "4 corporate_history 3.85 2 1\n", + "5 pasiva_refleja 3.17 2 0\n", + "6 copular 2.57 3 0\n", + "7 osint 3.50 2 1\n", + "8 biografico 2.75 2 3\n", + "9 evento 3.28 0 2" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results = {}\n", + "for name, text in CORPUS.items():\n", + " t0 = time.time()\n", + " doc = nlp(text)\n", + " triples = extract_triples(doc)\n", + " elapsed = time.time() - t0\n", + " ents = [{'text': e.text, 'label': e.label_} for e in doc.ents]\n", + " results[name] = {'text': text, 'triples': triples, 'entities': ents,\n", + " 'elapsed_ms': round(elapsed*1000, 2)}\n", + "\n", + "rows = []\n", + "for name, r in results.items():\n", + " rows.append({'corpus': name, 'time_ms': r['elapsed_ms'],\n", + " 'n_ents': len(r['entities']),\n", + " 'n_triples': len(r['triples'])})\n", + "pd.DataFrame(rows)" + ] + }, + { + "cell_type": "markdown", + "id": "86b29de9", + "metadata": {}, + "source": [ + "## 5. Tripletas extraidas por texto" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f64764d4", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T20:39:42.870514Z", + "iopub.status.busy": "2026-05-04T20:39:42.870363Z", + "iopub.status.idle": "2026-05-04T20:39:42.873967Z", + "shell.execute_reply": "2026-05-04T20:39:42.873142Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "[personal_amor] Enmanuel quiere a Ashlly desde hace anos.\n", + " ents: [('Enmanuel', 'PER'), ('Ashlly', 'PER')]\n", + " ('Enmanuel', 'querer', 'Ashlly')\n", + "\n", + "[personal_familia] Maria abrazo a su hermano Tomas tras la reunion.\n", + " ents: [('Maria', 'PER'), ('Tomas', 'PER')]\n", + " (sin tripletas — la regla no captó nada en este caso)\n", + "\n", + "[personal_amistad] Sara llamo a su madre Lucia para contarle las noticias.\n", + " ents: [('Sara', 'PER'), ('Lucia', 'PER')]\n", + " ('Sara', 'llamo', 'su madre Lucia')\n", + "\n", + "[corporate_short] Carlos Torres preside BBVA, con sede central en Bilbao.\n", + " ents: [('Carlos Torres', 'PER'), ('BBVA', 'ORG'), ('Bilbao', 'LOC')]\n", + " ('Carlos Torres', 'presidir', 'BBVA')\n", + " ('Carlos Torres', 'presidir_con' [con], ', con sede central en Bilbao')\n", + "\n", + "[corporate_history] Pablo Isla presidio Inditex de 2011 a 2022 y ahora forma parte del consejo de Telefonica.\n", + " ents: [('Pablo Isla', 'PER'), ('Telefonica', 'ORG')]\n", + " ('Pablo Isla presidio Inditex de 2011 a 2022', 'formar', 'consejo de Telefonica')\n", + "\n", + "[pasiva_refleja] Se firmaron acuerdos entre Iberdrola y Endesa.\n", + " ents: [('Iberdrola', 'ORG'), ('Endesa', 'ORG')]\n", + " (sin tripletas — la regla no captó nada en este caso)\n", + "\n", + "[copular] Pablo Isla es expresidente de Inditex y consejero de Telefonica.\n", + " ents: [('Pablo Isla', 'PER'), ('Inditex', 'ORG'), ('Telefonica', 'ORG')]\n", + " (sin tripletas — la regla no captó nada en este caso)\n", + "\n", + "[osint] El grupo APT-29 atribuido a Rusia ataco empresas energeticas espanolas.\n", + " ents: [('APT-29', 'ORG'), ('Rusia', 'LOC')]\n", + " ('El grupo APT-29 atribuido a Rusia', 'ataco', 'empresas energeticas espanolas')\n", + "\n", + "[biografico] Amancio Ortega fundo Inditex en 1985 en Arteixo.\n", + " ents: [('Amancio Ortega', 'PER'), ('Arteixo', 'LOC')]\n", + " ('Amancio Ortega', 'fundo', 'Inditex')\n", + " ('Amancio Ortega', 'fundo_en' [en], '1985')\n", + " ('Amancio Ortega', 'fundo_en' [en], 'Arteixo')\n", + "\n", + "[evento] El acuerdo movilizara dos mil millones en cinco anos.\n", + " ents: []\n", + " ('El acuerdo', 'movilizarar', 'dos mil millones')\n", + " ('El acuerdo', 'movilizarar_en' [en], 'cinco anos')\n" + ] + } + ], + "source": [ + "for name, r in results.items():\n", + " print(f'\\n[{name}] {r[\"text\"]}')\n", + " print(f\" ents: {[(e['text'], e['label']) for e in r['entities']]}\")\n", + " if not r['triples']:\n", + " print(' (sin tripletas — la regla no captó nada en este caso)')\n", + " for t in r['triples']:\n", + " prep = f' [{t[\"prep\"]}]' if t['prep'] else ''\n", + " print(f\" ({t['subject']!r}, {t['relation']!r}{prep}, {t['object']!r})\")" + ] + }, + { + "cell_type": "markdown", + "id": "95a3939f", + "metadata": {}, + "source": [ + "## 6. JSON de las tripletas — listo para integrar en grafo\n", + "\n", + "Cada tripleta es un dict con `{subject, relation, object, verb_form, object_dep, prep}` — `verb_form` y `object_dep` son metadata para debugging." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d60170e8", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T20:39:42.875681Z", + "iopub.status.busy": "2026-05-04T20:39:42.875552Z", + "iopub.status.idle": "2026-05-04T20:39:42.890421Z", + "shell.execute_reply": "2026-05-04T20:39:42.889474Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TOTAL: 11 tripletas en 10 textos\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
subjectrelationobjectverb_formprepsource
0EnmanuelquererAshllyquiereNaNpersonal_amor
1Sarallamosu madre LuciallamoNaNpersonal_amistad
2Carlos TorrespresidirBBVApresideNaNcorporate_short
3Carlos Torrespresidir_con, con sede central en Bilbaopresideconcorporate_short
4Pablo Isla presidio Inditex de 2011 a 2022formarconsejo de TelefonicaformaNaNcorporate_history
5El grupo APT-29 atribuido a Rusiaatacoempresas energeticas espanolasatacoNaNosint
6Amancio OrtegafundoInditexfundoNaNbiografico
7Amancio Ortegafundo_en1985fundoenbiografico
8Amancio Ortegafundo_enArteixofundoenbiografico
9El acuerdomovilizarardos mil millonesmovilizaraNaNevento
10El acuerdomovilizarar_encinco anosmovilizaraenevento
\n", + "
" + ], + "text/plain": [ + " subject relation \\\n", + "0 Enmanuel querer \n", + "1 Sara llamo \n", + "2 Carlos Torres presidir \n", + "3 Carlos Torres presidir_con \n", + "4 Pablo Isla presidio Inditex de 2011 a 2022 formar \n", + "5 El grupo APT-29 atribuido a Rusia ataco \n", + "6 Amancio Ortega fundo \n", + "7 Amancio Ortega fundo_en \n", + "8 Amancio Ortega fundo_en \n", + "9 El acuerdo movilizarar \n", + "10 El acuerdo movilizarar_en \n", + "\n", + " object verb_form prep source \n", + "0 Ashlly quiere NaN personal_amor \n", + "1 su madre Lucia llamo NaN personal_amistad \n", + "2 BBVA preside NaN corporate_short \n", + "3 , con sede central en Bilbao preside con corporate_short \n", + "4 consejo de Telefonica forma NaN corporate_history \n", + "5 empresas energeticas espanolas ataco NaN osint \n", + "6 Inditex fundo NaN biografico \n", + "7 1985 fundo en biografico \n", + "8 Arteixo fundo en biografico \n", + "9 dos mil millones movilizara NaN evento \n", + "10 cinco anos movilizara en evento " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_triples = []\n", + "for name, r in results.items():\n", + " for t in r['triples']:\n", + " all_triples.append({**t, 'source': name})\n", + "df = pd.DataFrame(all_triples)\n", + "print(f'TOTAL: {len(df)} tripletas en {len(results)} textos')\n", + "df[['subject', 'relation', 'object', 'verb_form', 'prep', 'source']]" + ] + }, + { + "cell_type": "markdown", + "id": "77cfdf32", + "metadata": {}, + "source": [ + "## 7. Visualizacion — grafo combinado de todas las tripletas" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "4a4ffcbc", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T20:39:42.892255Z", + "iopub.status.busy": "2026-05-04T20:39:42.892106Z", + "iopub.status.idle": "2026-05-04T20:39:43.114631Z", + "shell.execute_reply": "2026-05-04T20:39:43.113706Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "G = nx.DiGraph()\n", + "for t in all_triples:\n", + " s = t['subject']; o = t['object']\n", + " G.add_node(s); G.add_node(o)\n", + " if not G.has_edge(s, o):\n", + " G.add_edge(s, o, kind=t['relation'])\n", + "\n", + "fig, ax = plt.subplots(figsize=(15, 11))\n", + "if G.number_of_nodes():\n", + " pos = nx.spring_layout(G, k=2.0, iterations=100, seed=42)\n", + " nx.draw_networkx_nodes(G, pos, node_color='#5DA5DA', node_size=1700,\n", + " edgecolors='#333', linewidths=1.3, ax=ax)\n", + " labels = {n: (n if len(n) <= 22 else n[:21]+'…') for n in G.nodes}\n", + " nx.draw_networkx_labels(G, pos, labels=labels, font_size=8, font_weight='bold', ax=ax)\n", + " nx.draw_networkx_edges(G, pos, edge_color='#888', arrows=True, arrowsize=14,\n", + " width=1.2, alpha=0.7, ax=ax, connectionstyle='arc3,rad=0.08')\n", + " el = {(u, v): d['kind'] for u, v, d in G.edges(data=True)}\n", + " nx.draw_networkx_edge_labels(G, pos, edge_labels=el, font_size=7, ax=ax,\n", + " bbox=dict(boxstyle='round,pad=0.1', fc='white', ec='none', alpha=0.85))\n", + "ax.set_title(f'spaCy ES OpenIE — {G.number_of_nodes()} nodos, {G.number_of_edges()} aristas', fontsize=12)\n", + "ax.axis('off'); plt.tight_layout(); plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "590898bf", + "metadata": {}, + "source": [ + "## 8. Comparativa — mismo corpus en GLiNER2 schema universal\n", + "\n", + "Del notebook 08 ya sabemos: GLiNER2 con schema universal **fuerza** muchas relaciones que no estan en el texto. Aqui re-ejecutamos para tener la cifra concreta y comparar." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "060417fb", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-04T20:39:43.116680Z", + "iopub.status.busy": "2026-05-04T20:39:43.116520Z", + "iopub.status.idle": "2026-05-04T20:40:01.442806Z", + "shell.execute_reply": "2026-05-04T20:40:01.442042Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[0;93m2026-05-04 22:39:43.131669495 [W:onnxruntime:Default, device_discovery.cc:283 GetGpuDevices] Failed to detect devices under \"/sys/class/drm/card0\": device_discovery.cc:93 ReadFileContents Failed to open file: \"/sys/class/drm/card0/device/vendor\"\u001b[m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "You are using a model of type extractor to instantiate a model of type . This is not supported for all configurations of models and can yield errors.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "============================================================\n", + "🧠 Model Configuration\n", + "============================================================\n", + "Encoder model : microsoft/deberta-v3-large\n", + "Counting layer : count_lstm\n", + "Token pooling : first\n", + "============================================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GLiNER2 ready in 5.0s\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
corpusspacy_msspacy_triplesgliner2_sgliner2_relsratio_speed
0personal_amor5.2111.089207.3
1personal_familia3.2201.029316.8
2personal_amistad3.7211.038276.9
3corporate_short3.2221.049323.0
4corporate_history3.8511.054272.7
5pasiva_refleja3.1701.031324.9
6copular2.5701.035400.8
7osint3.5011.096311.4
8biografico2.7531.075389.1
9evento3.2821.032314.0
\n", + "
" + ], + "text/plain": [ + " corpus spacy_ms spacy_triples gliner2_s gliner2_rels \\\n", + "0 personal_amor 5.21 1 1.08 9 \n", + "1 personal_familia 3.22 0 1.02 9 \n", + "2 personal_amistad 3.72 1 1.03 8 \n", + "3 corporate_short 3.22 2 1.04 9 \n", + "4 corporate_history 3.85 1 1.05 4 \n", + "5 pasiva_refleja 3.17 0 1.03 1 \n", + "6 copular 2.57 0 1.03 5 \n", + "7 osint 3.50 1 1.09 6 \n", + "8 biografico 2.75 3 1.07 5 \n", + "9 evento 3.28 2 1.03 2 \n", + "\n", + " ratio_speed \n", + "0 207.3 \n", + "1 316.8 \n", + "2 276.9 \n", + "3 323.0 \n", + "4 272.7 \n", + "5 324.9 \n", + "6 400.8 \n", + "7 311.4 \n", + "8 389.1 \n", + "9 314.0 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Cargar GLiNER2 una sola vez si no esta cargado\n", + "from gliner2 import GLiNER2\n", + "t0 = time.time()\n", + "gl2 = GLiNER2.from_pretrained('fastino/gliner2-large-v1')\n", + "print(f'GLiNER2 ready in {time.time()-t0:.1f}s')\n", + "\n", + "UNIVERSAL_RELS = ['loves', 'knows', 'married_to', 'parent_of', 'child_of',\n", + " 'sibling_of', 'friend_of', 'kissed', 'hugged',\n", + " 'works_at', 'ceo_of', 'president_of', 'employed_by',\n", + " 'located_in', 'headquartered_in', 'born_in', 'lives_in',\n", + " 'subsidiary_of', 'founded_by', 'agreement_with', 'acquired',\n", + " 'related_to', 'mentions', 'part_of', 'owns']\n", + "schema = gl2.create_schema().entities(['person', 'organization', 'location', 'date', 'event']).relations(UNIVERSAL_RELS)\n", + "\n", + "comp = []\n", + "for name, text in CORPUS.items():\n", + " t0 = time.time()\n", + " g = gl2.extract(text, schema=schema, threshold=0.3)\n", + " g_time = time.time() - t0\n", + " n_g_rels = sum(len(v) for v in g['relation_extraction'].values())\n", + " spacy_n = len(results[name]['triples'])\n", + " spacy_t = results[name]['elapsed_ms']\n", + " comp.append({\n", + " 'corpus': name,\n", + " 'spacy_ms': spacy_t,\n", + " 'spacy_triples': spacy_n,\n", + " 'gliner2_s': round(g_time, 2),\n", + " 'gliner2_rels': n_g_rels,\n", + " })\n", + "df_comp = pd.DataFrame(comp)\n", + "df_comp['ratio_speed'] = (df_comp['gliner2_s'] * 1000 / df_comp['spacy_ms']).round(1)\n", + "df_comp" + ] + }, + { + "cell_type": "markdown", + "id": "1d58efe6", + "metadata": {}, + "source": [ + "## 9. Lectura final\n", + "\n", + "**spaCy ES wins on:**\n", + "- ⭐ Velocidad: 200-1000× mas rapido que GLiNER2\n", + "- ⭐ Schema-less: predicado = verbo del texto, no del schema (`querer`, `abrazar`, `presidir` salen literales)\n", + "- ⭐ Sin alucinaciones: si la regla no encaja, devuelve vacio (mejor que inventarse)\n", + "\n", + "**GLiNER2 universal wins on:**\n", + "- Recall (encuentra mas \"posibles\" relaciones, aunque sean discutibles)\n", + "- Output normalizado a un vocabulario controlado\n", + "- NER multilabel mas rico\n", + "\n", + "**Limitaciones de spaCy ES dep-rules (mejorables):**\n", + "- Pasiva refleja (`se firmaron acuerdos`) — la regla la captura pero el sujeto puede salir vacio\n", + "- Pronombres (`su madre Lucia`) — no se resuelve `su` al sujeto previo (necesita coref)\n", + "- Verbos compuestos (`ha sido nombrado`) — auxiliar mas participio puede confundir\n", + "- Frases con `que` subordinado (`Pablo que dirige Inditex`)\n", + "\n", + "## Stack hibrido recomendado para `graph_explorer`\n", + "\n", + "```\n", + "spaCy ES dep-rules → relaciones schema-less (verbos del texto, ~5ms)\n", + " +\n", + "GLiNER2 universal → entidades tipadas + relaciones de schema controlado\n", + " +\n", + "merge: para cada par (s, o), preferir el predicado de spaCy si existe;\n", + " si no, usar el de GLiNER2 (con post-filter typed)\n", + "```\n", + "\n", + "Esto da el mejor de ambos mundos:\n", + "- Verbos del texto cuando estan claros (alta confianza linguistica)\n", + "- Schema controlado como respaldo para casos donde la sintaxis es ambigua" + ] + }, + { + "cell_type": "markdown", + "id": "efb5b596", + "metadata": {}, + "source": [ + "## 10. Funciones a promover al registry (proximo fn-constructor)\n", + "\n", + "1. `spacy_es_load_model_py_datascience` (impure) — wrapper cacheado\n", + "2. `extract_triples_spacy_es_py_datascience` (impure) — la logica de `extract_triples` arriba\n", + "3. `merge_openie_with_typed_py_core` (pure) — merge GLiNER2 + spaCy ES con preferencia" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nuextract_full.json b/nuextract_full.json new file mode 100644 index 0000000..49d233d --- /dev/null +++ b/nuextract_full.json @@ -0,0 +1,949 @@ +{ + "meta": { + "device": "cuda", + "dtype": "torch.bfloat16", + "model": "numind/NuExtract-2.0-2B", + "repetition_penalty": 1.15, + "max_chars_chunk": 800 + }, + "long_text": { + "elapsed_s": 21.9, + "n_chunks": 4, + "n_chunks_parsed_ok": 4, + "agg": { + "organizations": [ + { + "name": "BBVA", + "count": 1, + "ceo": [ + "Carlos Torres" + ], + "chairman_president": [], + "headquartered_in": [ + "Bilbao" + ], + "subsidiaries": [], + "parent_company": [] + }, + { + "name": "Santander", + "count": 1, + "ceo": [ + "Hector Grisi" + ], + "chairman_president": [ + "Ana Botin" + ], + "headquartered_in": [ + "Valencia" + ], + "subsidiaries": [], + "parent_company": [] + }, + { + "name": "CaixaBank", + "count": 1, + "ceo": [ + "Gonzalo Gortazar" + ], + "chairman_president": [], + "headquartered_in": [], + "subsidiaries": [], + "parent_company": [] + }, + { + "name": "Banco de Espana", + "count": 1, + "ceo": [ + "Pablo Hernandez de Cos" + ], + "chairman_president": [ + "Margarita Delgado" + ], + "headquartered_in": [], + "subsidiaries": [], + "parent_company": [] + }, + { + "name": "Repsol", + "count": 1, + "ceo": [ + "Josu Jon Imaz" + ], + "chairman_president": [], + "headquartered_in": [], + "subsidiaries": [], + "parent_company": [] + }, + { + "name": "Iberdrola", + "count": 1, + "ceo": [ + "Ignacio Galan" + ], + "chairman_president": [], + "headquartered_in": [], + "subsidiaries": [ + "Avangrid" + ], + "parent_company": [] + }, + { + "name": "Endesa", + "count": 1, + "ceo": [ + "Marina Serrano" + ], + "chairman_president": [], + "headquartered_in": [ + "Espana, Portugal y Marruecos" + ], + "subsidiaries": [], + "parent_company": [] + }, + { + "name": "Telefonica", + "count": 1, + "ceo": [], + "chairman_president": [ + "Jose Maria Alvarez-Pallete" + ], + "headquartered_in": [ + "Madrid" + ], + "subsidiaries": [], + "parent_company": [] + }, + { + "name": "Naturgy", + "count": 1, + "ceo": [ + "Francisco Reynes" + ], + "chairman_president": [], + "headquartered_in": [ + "Barcelona" + ], + "subsidiaries": [], + "parent_company": [] + } + ], + "people": [ + { + "name": "Onur Genc", + "count": 1, + "roles": [ + "Consejero Delegado" + ], + "organizations": [ + "Banca Sabadell" + ] + }, + { + "name": "Jose Antonio Alvarez", + "count": 1, + "roles": [ + "CEO Global" + ], + "organizations": [ + "CaixaBank" + ] + }, + { + "name": "Josu Jon Imaz", + "count": 2, + "roles": [ + "CEO of Repsol", + "CEO" + ], + "organizations": [ + "Repsol", + "Repsol" + ] + }, + { + "name": "Antonio Brufau", + "count": 2, + "roles": [ + "President of Repsol", + "presidente" + ], + "organizations": [ + "Repsol", + "Repsol" + ] + }, + { + "name": "Ignacio Galan", + "count": 1, + "roles": [ + "líder" + ], + "organizations": [ + "Iberdrola" + ] + }, + { + "name": "Andy Jassy", + "count": 1, + "roles": [ + "CEO" + ], + "organizations": [ + "Amazon" + ] + }, + { + "name": "Amancio Ortega", + "count": 1, + "roles": [ + "President and CEO of Inditex" + ], + "organizations": [ + "Inditex" + ] + }, + { + "name": "Pablo Isla", + "count": 1, + "roles": [ + "Consejer" + ], + "organizations": [ + "Telefonica" + ] + }, + { + "name": "Cristina Aldamiz-Echevarría", + "count": 1, + "roles": [ + "Directora de Recursos Humanos" + ], + "organizations": [ + "Grupo Mapfre" + ] + } + ], + "agreements": [ + { + "between": [ + "Repsol", + "Macquarie" + ], + "topic": "venta de filial mexicana", + "amount": null + } + ] + }, + "graph": { + "nodes": { + "BBVA": "organization", + "Carlos Torres": "person", + "Bilbao": "location", + "Santander": "organization", + "Hector Grisi": "person", + "Ana Botin": "person", + "Valencia": "location", + "CaixaBank": "organization", + "Gonzalo Gortazar": "person", + "Banco de Espana": "organization", + "Pablo Hernandez de Cos": "person", + "Margarita Delgado": "person", + "Repsol": "organization", + "Josu Jon Imaz": "person", + "Iberdrola": "organization", + "Ignacio Galan": "person", + "Avangrid": "organization", + "Endesa": "organization", + "Marina Serrano": "person", + "Espana, Portugal y Marruecos": "location", + "Telefonica": "organization", + "Jose Maria Alvarez-Pallete": "person", + "Madrid": "location", + "Naturgy": "organization", + "Francisco Reynes": "person", + "Barcelona": "location", + "Onur Genc": "person", + "Banca Sabadell": "organization", + "Jose Antonio Alvarez": "person", + "Antonio Brufau": "person", + "Andy Jassy": "person", + "Amazon": "organization", + "Amancio Ortega": "person", + "Inditex": "organization", + "Pablo Isla": "person", + "Cristina Aldamiz-Echevarría": "person", + "Grupo Mapfre": "organization", + "Macquarie": "organization" + }, + "edges": [ + [ + "Ignacio Galan", + "works_at", + "Iberdrola" + ], + [ + "Santander", + "headquartered_in", + "Valencia" + ], + [ + "Hector Grisi", + "ceo_of", + "Santander" + ], + [ + "Antonio Brufau", + "works_at", + "Repsol" + ], + [ + "Carlos Torres", + "ceo_of", + "BBVA" + ], + [ + "Josu Jon Imaz", + "ceo_of", + "Repsol" + ], + [ + "Naturgy", + "headquartered_in", + "Barcelona" + ], + [ + "Pablo Hernandez de Cos", + "ceo_of", + "Banco de Espana" + ], + [ + "Gonzalo Gortazar", + "ceo_of", + "CaixaBank" + ], + [ + "Onur Genc", + "works_at", + "Banca Sabadell" + ], + [ + "BBVA", + "headquartered_in", + "Bilbao" + ], + [ + "Telefonica", + "headquartered_in", + "Madrid" + ], + [ + "Margarita Delgado", + "president_of", + "Banco de Espana" + ], + [ + "Ana Botin", + "president_of", + "Santander" + ], + [ + "Marina Serrano", + "ceo_of", + "Endesa" + ], + [ + "Jose Antonio Alvarez", + "works_at", + "CaixaBank" + ], + [ + "Ignacio Galan", + "ceo_of", + "Iberdrola" + ], + [ + "Avangrid", + "subsidiary_of", + "Iberdrola" + ], + [ + "Repsol", + "agreement_with", + "Macquarie" + ], + [ + "Cristina Aldamiz-Echevarría", + "works_at", + "Grupo Mapfre" + ], + [ + "Pablo Isla", + "works_at", + "Telefonica" + ], + [ + "Andy Jassy", + "works_at", + "Amazon" + ], + [ + "Endesa", + "headquartered_in", + "Espana, Portugal y Marruecos" + ], + [ + "Josu Jon Imaz", + "works_at", + "Repsol" + ], + [ + "Amancio Ortega", + "works_at", + "Inditex" + ], + [ + "Jose Maria Alvarez-Pallete", + "president_of", + "Telefonica" + ], + [ + "Francisco Reynes", + "ceo_of", + "Naturgy" + ] + ] + }, + "n_nodes": 38, + "n_edges": 27, + "n_isolates": 0 + }, + "pdf": { + "elapsed_s": 361.1, + "n_chunks": 179, + "n_chunks_parsed_ok": 179, + "agg_summary": { + "n_data_controllers": 3, + "n_dpo_contacts": 25, + "n_data_categories": 0, + "n_rights": 31, + "n_authorities": 35, + "n_laws": 0 + }, + "agg_full": { + "data_controllers": [ + { + "name": "Confirma Sistemas de Información, S.L.", + "address": null, + "registration": null + }, + { + "name": "FrauDfense, S.L.", + "address": null, + "registration": null + }, + { + "name": "Grupo BBVA", + "address": null, + "registration": null + } + ], + "dpo_contacts": [ + { + "email": "consultasgenerales@bbva.com", + "address": null + }, + { + "email": "consultasgenerales@bbva.com", + "address": null + }, + { + "email": "derechosprotecciondatos@bbva.com", + "address": null + }, + { + "email": "derechosprotecciondatos@bbva.com", + "address": null + }, + { + "email": "dpogrupobbva@bbva.com", + "address": null + }, + { + "email": "dpogrupobbva@bbva.com", + "address": null + }, + { + "email": "dpo@confirmasistemas.es", + "address": null + }, + { + "email": "derechosprotecciondatos@bbva.com", + "address": "Servicio Atención al Cliente Grupo BBVA, APDO: 1598 - 28080 Madrid" + }, + { + "email": "derechosprotecciondatos@bbva.com", + "address": "Servicio Atención al Cliente Grupo BBVA, APDO: 1598 - 28080 Madrid" + }, + { + "email": "derechosprotecciondatos@bbva.com", + "address": "Servicio Atención al Cliente Grupo BBVA, APDO: 1598 - 28080 Madrid" + }, + { + "email": "dpogrupobbva@bbva.com", + "address": "Servicio Atención al Cliente Grupo BBVA, APDO: 1598 - 28080 Madrid" + }, + { + "email": "dpogrupobbva@bbva.com", + "address": null + }, + { + "email": "derechosprotecciondatos@bbva.com", + "address": null + }, + { + "email": "derechosprotectiondatos@bbva.com", + "address": null + }, + { + "email": "protecciondedatos@fraudfense.com", + "address": null + }, + { + "email": "protecciondedatos@fraudfense.com", + "address": null + }, + { + "email": "derechosprotecciondatos@bbva.com", + "address": null + }, + { + "email": "protecciondedatos@fraudfense.com", + "address": "Servicio Atención al Cliente Grupo BBVA, APDO: 1598 - 28080 Madrid" + }, + { + "email": "protecciondedatos@fraudfense.com", + "address": "Servicio Atención al Cliente Grupo BBVA, APDO: 1598 - 28080 Madrid" + }, + { + "email": "derechosprotecciondatos@bbva.com", + "address": null + }, + { + "email": "derechosprotecciondatos@bbva.com", + "address": null + }, + { + "email": "derechosprotecciondatos@bbva.com", + "address": null + }, + { + "email": "derechosprotection.datos@bbva.com", + "address": "Grupo BBVA, APDO: 1598 - 28080 Madrid" + }, + { + "email": "derechosprotecciondatos@bbva.com", + "address": "APDO: 1598 - 28080 Madrid" + }, + { + "email": "derechosprotecciondatos@bbva.com", + "address": "Servicio Atención al Cliente Grupo BBVA, APDO: 1598 28080 Madrid" + } + ], + "data_categories": {}, + "rights_listed": { + "Acceso": 1, + "Rectificación": 1, + "Supresión": 1, + "Solicitud de otros derechos": 1, + "derecho a que un operador intervenga": 2, + "derecho de acceso": 5, + "rectificación": 7, + "supresión": 7, + "oposición": 7, + "limitación del tratamiento": 7, + "portabilidad": 1, + "derecho a la portabilidad": 6, + "derecho de oposición": 1, + "derechos de acceso": 2, + "El derecho a que un operador explique la decisión adoptada": 1, + "Recoger comentarios al respecto": 1, + "derecho fundamental a la protección de datos personales": 5, + "explicación sobre la decisión": 1, + "recogida de comentarios": 1, + "impugnación": 1, + "respeto a tu derecho fundamental a la protección de datos personales": 1, + "Derecho de acceso": 1, + "Access": 1, + "Correction": 1, + "Suppression": 1, + "Opposition": 1, + "Limitation of processing": 1, + "derecho a que un operador intervenga para explicarte la decisión adoptada": 1, + "recoger tus comentarios sobre ello": 1, + "explicación de la decisión": 1, + "comentarios sobre la decisión": 1 + }, + "authorities": [ + { + "name": "Grupo BBVA", + "contact_options": [ + "https://www.bbva.es" + ], + "count": 5 + }, + { + "name": "BBVA", + "contact_options": [ + "https://www.bbva.es/general/tratamiento-datos.html", + "https://www.bbva.es", + "https://www.iberpay.com/es/servicios/sectoriales/prevencion-del-fraude/", + "https://916087356-1.servicio-online.net/sobre-nosotros1/nuestrospartners", + "https://www.bbva.es/content/dam/public-web/bbvaes/documents/legal/tratamiento-de-datos/decisiones-automatizadas-i.pdf", + "https://www.bbva.es", + "https://www.bbva.es/content/dam/public-web/bbvaes/documents/legal/tratamiento-de-datos/listado-productos-y-servicios.pdf", + "https://www.bbva.es", + "https://www.bbva.es", + "https://www.bbva.es", + "https://www.bbva.es", + "https://www.bbva.es/general/tratamiento-datos.html" + ], + "count": 46 + }, + { + "name": "Banco Bilbao Vizcaya Argentaria, S.A.", + "contact_options": [ + "https://protecciondedatos.bbva.es", + "https://www.bbva.es" + ], + "count": 19 + }, + { + "name": "Banco", + "contact_options": [], + "count": 1 + }, + { + "name": "Tesorería General de la Seguridad Social", + "contact_options": [], + "count": 2 + }, + { + "name": "Banco de España", + "contact_options": [], + "count": 5 + }, + { + "name": "Emailage", + "contact_options": [], + "count": 1 + }, + { + "name": "Ministerio de Hacienda", + "contact_options": [], + "count": 1 + }, + { + "name": "Secretaría de Estado de Economía y Apoyo a la Empresa", + "contact_options": [], + "count": 1 + }, + { + "name": "Sepblac", + "contact_options": [], + "count": 1 + }, + { + "name": "Banco Central Europeo", + "contact_options": [], + "count": 2 + }, + { + "name": "Autoridad Bancaria Europea", + "contact_options": [], + "count": 2 + }, + { + "name": "Entidades de crédito", + "contact_options": [], + "count": 1 + }, + { + "name": "MiFID", + "contact_options": [ + "https://www.miifid.org" + ], + "count": 1 + }, + { + "name": "Ministerio de Fomento", + "contact_options": [], + "count": 2 + }, + { + "name": "Ley de los Mercados de Valores", + "contact_options": [ + "https://www.bbva.es/content/dam/public-web/bbvaes/documents/legal/tratamiento-de-datos/perfil-de-riesgo-de-inversion.pdf" + ], + "count": 1 + }, + { + "name": "Reglamento del Fichero Confirma", + "contact_options": [], + "count": 1 + }, + { + "name": "Ministerio de Hacienda y Portavoz", + "contact_options": [], + "count": 1 + }, + { + "name": "Agencia Española de Protección de Datos", + "contact_options": [ + "www.aepd.es", + "https://www.aepd.es", + "www.aepd.es", + "https://www.aepd.es", + "www.aepd.es", + "https://www.aepd.es", + "https://www.aepd.es" + ], + "count": 7 + }, + { + "name": "Sociedad Española de Sistemas de Pago S.A.", + "contact_options": [ + "https://www.iberpay.com/es/servicios/sectoriales/prevencion-del-fraude/" + ], + "count": 1 + }, + { + "name": "Delegado de Protección de Datos de BBVA", + "contact_options": [ + "https://www.bbva.es/general/tratamiento-datos.html", + "https://www.bbva.es/general/tratamiento-datos.html#contacto-dpo", + "https://www.bbva.es/general/tratamiento-datos.html" + ], + "count": 3 + }, + { + "name": "Fraudfense", + "contact_options": [ + "https://916087356-1.servicio-online.net/sobre-nosotros1/nuestrospartners" + ], + "count": 1 + }, + { + "name": "FrauDfense, S.L.", + "contact_options": [ + "https://www.bbva.es/general/tratamiento-datos.html" + ], + "count": 1 + }, + { + "name": "Ficha Dfense", + "contact_options": [], + "count": 1 + }, + { + "name": "Ficha FrauD", + "contact_options": [], + "count": 1 + }, + { + "name": "CIRBE", + "contact_options": [], + "count": 1 + }, + { + "name": "Autoridades nacionales e internacionales", + "contact_options": [], + "count": 1 + }, + { + "name": "Confirma", + "contact_options": [], + "count": 1 + }, + { + "name": "Solicitantes", + "contact_options": [], + "count": 1 + }, + { + "name": "Juez", + "contact_options": [], + "count": 1 + }, + { + "name": "Tribunal", + "contact_options": [], + "count": 1 + }, + { + "name": "Ministerio Fiscal", + "contact_options": [], + "count": 1 + }, + { + "name": "Seguros y Reaseguros", + "contact_options": [ + "https://www.bbva.es" + ], + "count": 1 + }, + { + "name": "Comisión Europea", + "contact_options": [], + "count": 1 + }, + { + "name": "Delegado de Protección de Datos", + "contact_options": [ + "https://www.bbva.es/general/tratamiento-datos.html" + ], + "count": 1 + } + ], + "laws": {} + }, + "graph": { + "nodes": { + "Confirma Sistemas de Información, S.L.": "data_controller", + "consultasgenerales@bbva.com": "email", + "derechosprotecciondatos@bbva.com": "email", + "dpogrupobbva@bbva.com": "email", + "dpo@confirmasistemas.es": "email", + "Servicio Atención al Cliente Grupo BBVA, APDO: 1598 - 28080 Madrid": "location", + "derechosprotectiondatos@bbva.com": "email", + "protecciondedatos@fraudfense.com": "email", + "derechosprotection.datos@bbva.com": "email", + "Grupo BBVA, APDO: 1598 - 28080 Madrid": "location", + "APDO: 1598 - 28080 Madrid": "location", + "Servicio Atención al Cliente Grupo BBVA, APDO: 1598 28080 Madrid": "location", + "Acceso": "right", + "Rectificación": "right", + "Supresión": "right", + "Solicitud de otros derechos": "right", + "derecho a que un operador intervenga": "right", + "derecho de acceso": "right", + "rectificación": "right", + "supresión": "right", + "oposición": "right", + "limitación del tratamiento": "right", + "portabilidad": "right", + "derecho a la portabilidad": "right", + "derecho de oposición": "right", + "derechos de acceso": "right", + "El derecho a que un operador explique la decisión adoptada": "right", + "Recoger comentarios al respecto": "right", + "derecho fundamental a la protección de datos personales": "right", + "explicación sobre la decisión": "right", + "recogida de comentarios": "right", + "impugnación": "right", + "respeto a tu derecho fundamental a la protección de datos personales": "right", + "Derecho de acceso": "right", + "Access": "right", + "Correction": "right", + "Suppression": "right", + "Opposition": "right", + "Limitation of processing": "right", + "derecho a que un operador intervenga para explicarte la decisión adoptada": "right", + "recoger tus comentarios sobre ello": "right", + "explicación de la decisión": "right", + "comentarios sobre la decisión": "right", + "Grupo BBVA": "authority", + "https://www.bbva.es": "url", + "Delegado de Protección de Datos de BBVA": "authority", + "https://www.bbva.es/general/tratamiento-datos.html": "url", + "Banco Bilbao Vizcaya Argentaria, S.A.": "authority", + "https://protecciondedatos.bbva.es": "url", + "Tesorería General de la Seguridad Social": "authority", + "Banco de España": "authority", + "Emailage": "authority", + "Ministerio de Hacienda y Portavoz": "authority", + "Secretaría de Estado de Economía y Apoyo a la Empresa": "authority", + "Sepblac": "authority", + "Banco Central Europeo": "authority", + "Autoridad Bancaria Europea": "authority", + "Entidades de crédito": "authority", + "MiFID": "authority", + "https://www.miifid.org": "url", + "Ministerio de Fomento": "authority", + "Ley de los Mercados de Valores": "authority", + "https://www.bbva.es/content/dam/public-web/bbvaes/documents/legal/tratamiento-de-datos/perfil-de-riesgo-de-inversion.pdf": "url", + "Reglamento del Fichero Confirma": "authority", + "Agencia Española de Protección de Datos": "authority", + "www.aepd.es": "url", + "Sociedad Española de Sistemas de Pago S.A.": "authority", + "https://www.iberpay.com/es/servicios/sectoriales/prevencion-del-fraude/": "url", + "FrauDfense, S.L.": "authority", + "https://916087356-1.servicio-online.net/sobre-nosotros1/nuestrospartners": "url", + "Ficha Dfense": "authority", + "Ficha FrauD": "authority", + "CIRBE": "authority", + "Autoridades nacionales e internacionales": "authority", + "Solicitantes": "authority", + "Juez": "authority", + "Tribunal": "authority", + "Ministerio Fiscal": "authority", + "Seguros y Reaseguros": "authority", + "Comisión Europea": "authority" + }, + "edges": [ + [ + "Sociedad Española de Sistemas de Pago S.A.", + "contact", + "https://www.iberpay.com/es/servicios/sectoriales/prevencion-del-fraude/" + ], + [ + "Delegado de Protección de Datos de BBVA", + "contact", + "https://www.bbva.es/general/tratamiento-datos.html" + ], + [ + "Ley de los Mercados de Valores", + "contact", + "https://www.bbva.es/content/dam/public-web/bbvaes/documents/legal/tratamiento-de-datos/perfil-de-riesgo-de-inversion.pdf" + ], + [ + "MiFID", + "contact", + "https://www.miifid.org" + ], + [ + "Banco Bilbao Vizcaya Argentaria, S.A.", + "contact", + "https://protecciondedatos.bbva.es" + ], + [ + "Seguros y Reaseguros", + "contact", + "https://www.bbva.es" + ], + [ + "Agencia Española de Protección de Datos", + "contact", + "www.aepd.es" + ], + [ + "FrauDfense, S.L.", + "contact", + "https://916087356-1.servicio-online.net/sobre-nosotros1/nuestrospartners" + ], + [ + "Grupo BBVA", + "contact", + "https://www.bbva.es" + ], + [ + "FrauDfense, S.L.", + "contact", + "https://www.bbva.es/general/tratamiento-datos.html" + ] + ] + }, + "n_nodes": 80, + "n_edges": 10 + } +} \ No newline at end of file diff --git a/nuextract_results.json b/nuextract_results.json new file mode 100644 index 0000000..75cbec3 --- /dev/null +++ b/nuextract_results.json @@ -0,0 +1,142 @@ +{ + "meta": { + "device": "cuda", + "dtype": "torch.bfloat16", + "model": "numind/NuExtract-2.0-2B" + }, + "cpu_baseline": { + "T1_flat": { + "elapsed_s": 24.98, + "in_tok": 245, + "out_tok": 79 + }, + "T2_rich": { + "elapsed_s": 117.51, + "in_tok": 351, + "out_tok": 370 + } + }, + "T1_corp_short_flat": { + "elapsed_s": 2.88, + "n_input_tokens": 245, + "n_output_tokens": 79, + "raw_text": "{\"people\": [\"Pablo Isla\", \"Jose Maria Alvarez-Pallete\", \"Ignacio Galan\", \"Marina Serrano\", \"Carlos Torres\"], \"organizations\": [\"Telefonica\", \"Inditex\", \"Iberdrola\", \"Endesa\", \"BBVA\"], \"locations\": [\"Artixo, A Coruna\", \"Bilbao\"]}", + "parsed": { + "people": [ + "Pablo Isla", + "Jose Maria Alvarez-Pallete", + "Ignacio Galan", + "Marina Serrano", + "Carlos Torres" + ], + "organizations": [ + "Telefonica", + "Inditex", + "Iberdrola", + "Endesa", + "BBVA" + ], + "locations": [ + "Artixo, A Coruna", + "Bilbao" + ] + }, + "input_chars": 658 + }, + "T2_corp_short_rich": { + "elapsed_s": 9.94, + "n_input_tokens": 351, + "n_output_tokens": 363, + "raw_text": "{\"organizations\": [{\"name\": \"Telefonica\", \"ceo\": \"Jose Maria Alvarez-Pallete\", \"chairman_president\": \"Jose Maria Alvarez-Pallete\", \"headquartered_in\": \"Madrid\", \"subsidiaries\": [], \"parent_company\": null}, {\"name\": \"Iberdrola\", \"ceo\": \"Ignacio Galan\", \"chairman_president\": null, \"headquartered_in\": \"Bilbao\", \"subsidiaries\": [], \"parent_company\": null}, {\"name\": \"Endesa\", \"ceo\": \"Marina Serrano\", \"chairman_president\": null, \"headquartered_in\": \"Bilbao\", \"subsidiaries\": [], \"parent_company\": null}, {\"name\": \"BBVA\", \"ceo\": \"Carlos Torres\", \"chairman_president\": null, \"headquartered_in\": \"Bilbao\", \"subsidiaries\": [], \"parent_company\": null}], \"people\": [{\"name\": \"Pablo Isla\", \"role\": \"Consejero\", \"organization\": \"Inditex\"}, {\"name\": \"Jose Maria Alvarez-Pallete\", \"role\": \"Presidente\", \"organization\": \"Telefonica\"}, {\"name\": \"Ignacio Galan\", \"role\": \"Presidente\", \"organization\": \"Iberdrola\"}, {\"name\": \"Marina Serrano\", \"role\": \"CEO\", \"organization\": \"Endesa\"}], \"agreements\": [{\"between\": [\"Telefonica\", \"Iberdrola\", \"Endesa\"], \"topic\": \"colaboracion en proyectos eolicos en Galicia\", \"amount\": \"2.000 millones de euros en cinco anos\"}]}", + "parsed": { + "between": [ + "Telefonica", + "Iberdrola", + "Endesa" + ], + "topic": "colaboracion en proyectos eolicos en Galicia", + "amount": "2.000 millones de euros en cinco anos" + }, + "input_chars": 658 + }, + "T3_long_text_rich": { + "elapsed_s": 53.56, + "n_input_tokens": 952, + "n_output_tokens": 2048, + "raw_text": "{\"organizations\": [{\"name\": \"BBVA\", \"ceo\": \"Carlos Torres\", \"chairman_president\": \"Carlos Torres\", \"headquartered_in\": \"Bilbao\", \"subsidiaries\": [\"Banco Sabadell\"], \"parent_company\": \"BBVA\"}, {\"name\": \"Banco Santander\", \"ceo\": \"Ana Botin\", \"chairman_president\": \"Ana Botin\", \"headquartered_in\": \"Valencia\", \"subsidiaries\": [\"CaixaBank\", \"Bankia\"], \"parent_company\": \"Banco Santander\"}, {\"name\": \"CaixaBank\", \"ceo\": \"Hector Grisi\", \"chairman_president\": \"Hector Grisi\", \"headquartered_in\": \"Valencia\", \"subsidiaries\": [\"Bankia\"], \"parent_company\": \"CaixaBank\"}, {\"name\": \"Banco de Espana\", \"ceo\": \"Pablo Hernandez de Cos\", \"chairman_president\": \"Pablo Hernandez de Cos\", \"headquartered_in\": null, \"subsidiaries\": [], \"parent_company\": \"Banco de Espana\"}, {\"name\": \"La Comision Nacional del Mercado de Valores\", \"ceo\": \"Rodrigo Buenaventura\", \"chairman_president\": \"Rodrigo Buenaventura\", \"headquartered_in\": null, \"subsidiaries\": [], \"parent_company\": null}, {\"name\": \"Telefonica\", \"ceo\": \"Jose Maria Alvarez-Pallete\", \"chairman_president\": \"Jose Maria Alvarez-Pallete\", \"headquartered_in\": null, \"subsidiaries\": [], \"parent_company\": \"Telefonica\"}, {\"name\": \"Saudi Telecom Company\", \"ceo\": \"Andy Jassy\", \"chairman_president\": \"Andy Jassy\", \"headquartered_in\": null, \"subsidiaries\": [], \"parent_company\": \"Saudi Telecom Company\"}, {\"name\": \"Recurso Humanos del Grupo Mapfre\", \"ceo\": \"Cristina Aldamiz-Echevarria\", \"chairman_president\": \"Cristina Aldamiz-Echevarria\", \"headquartered_in\": null, \"subsidiaries\": [], \"parent_company\": \"Grupo Mapfre\"}], \"people\": [{\"name\": \"Carlos Torres\", \"role\": \"presidente\", \"organization\": \"BBVA\"}, {\"name\": \"Onur Genc\", \"role\": \"consejero delegado\", \"organization\": \"Banco Sabadell\"}, {\"name\": \"Josep Oliu\", \"role\": \"presidente\", \"organization\": \"Sabadell\"}, {\"name\": \"Josep Oliu\", \"role\": \"presidente\", \"organization\": \"Sabadell\"}, {\"name\": \"Josu Jon Imaz\", \"role\": \"CEO\", \"organization\": \"Repsol\"}, {\"name\": \"Antonio Brufau\", \"role\": \"presidente\", \"organization\": \"Repsol\"}, {\"name\": \"Josu Jon Imaz\", \"role\": \"CEO\", \"organization\": \"Repsol\"}, {\"name\": \"Antonio Brufau\", \"role\": \"presidente\", \"organization\": \"Repsol\"}, {\"name\": \"Josu Jon Imaz\", \"role\": \"CEO\", \"organization\": \"Repsol\"}, {\"name\": \"Ignacio Galan\", \"role\": \"CEO\", \"organization\": \"Iberdrola\"}, {\"name\": \"Andy Jassy\", \"role\": \"CEO\", \"organization\": \"Amazon\"}, {\"name\": \"Marina Serrano\", \"role\": \"CEO\", \"organization\": \"Endesa\"}, {\"name\": \"Florentino Perez\", \"role\": \"presidente\", \"organization\": \"ACS\"}, {\"name\": \"Rafael del Pino\", \"role\": \"presidente\", \"organization\": \"Ferrovial\"}, {\"name\": \"Pablo Isla\", \"role\": \"expresidente de Inditex\", \"organization\": \"Inditex\"}, {\"name\": \"Jose Maria Alvarez-Pallete\", \"role\": \"presidente\", \"organization\": \"Telefonica\"}, {\"name\": \"Cristina Aldamiz-Echevarria\", \"role\": \"directora de Recursos Humanos\", \"organization\": \"Grupo Mapfre\"}, {\"name\": \"Francisco Reynes\", \"role\": \"presidente\", \"organization\": \"Naturgy\"}, {\"name\": \"Marc Murtra\", \"role\": \"presidente\", \"organization\": \"Indra\"}, {\"name\": \"Margarita Robles\", \"role\": \"presidente\", \"organization\": \"Indra\"}, {\"name\": \"Antonio Huertas\", \"role\": \"directora de Recursos Humanos\", \"organization\": \"Grupo Mapfre\"}], \"agreements\": [{\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"integracion operativa\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA hostil\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"independencia del banco\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"contrato a Iberdrola\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"entrada del estado en su capital\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"topic\": \"OPA parcial\", \"amount\": null}, {\"between\": [\"BBVA\", \"Sabadell\"], \"", + "parsed": null, + "input_chars": 2722 + }, + "pdf_meta": { + "n_chunks": 97, + "clean_chars": 88714 + }, + "T4_pdf_chunks": [ + { + "chunk_idx": 0, + "elapsed_s": 2.84, + "n_input_tokens": 581, + "n_output_tokens": 115, + "raw_text": "{\"data_controller\": {\"name\": \"Banco Bilbao Vizcaya Argentaria, S.A.\", \"address\": \"Plaza de San Nicolás 4, 48005 Bilbao, España\", \"registration\": \"C.I.F. A-48265169\"}, \"dpo_contact\": {\"email\": \"consultasgenerales@bbva.com\", \"address\": null}, \"data_categories\": [], \"rights_listed\": [], \"authorities_mentioned\": [], \"laws_mentioned\": []}", + "parsed": { + "email": "consultasgenerales@bbva.com", + "address": null + }, + "input_chars": 1448 + }, + { + "chunk_idx": 5, + "elapsed_s": 4.03, + "n_input_tokens": 559, + "n_output_tokens": 160, + "raw_text": "{\"data_controller\": {\"name\": \"Banco Bilbao Vizcaya Argentaria, S.A.\", \"address\": \"Plaza San Nicolás, 4, 48005 Bilbao, España\", \"registration\": \"C.I.F. A-48265169\"}, \"dpo_contact\": {\"email\": \"consultasgenerales@bbva.com\", \"address\": \"Plaza de San Nicolás 4, 48005, Bilbao, España\"}, \"data_categories\": [\"Datos identificativos y de contacto\", \"Información sobre productos y servicios\", \"Información sobre servicios de comercio\"], \"rights_listed\": [], \"authorities_mentioned\": [], \"laws_mentioned\": []}", + "parsed": { + "email": "consultasgenerales@bbva.com", + "address": "Plaza de San Nicolás 4, 48005, Bilbao, España" + }, + "input_chars": 1483 + }, + { + "chunk_idx": 15, + "elapsed_s": 4.69, + "n_input_tokens": 530, + "n_output_tokens": 184, + "raw_text": "{\"data_controller\": {\"name\": null, \"address\": null, \"registration\": null}, \"dpo_contact\": {\"email\": null, \"address\": null}, \"data_categories\": [\"Datos de firma (incluida la firma digitalizada y electrónica)\", \"Datos económicos y de solvencia patrimonial\", \"Datos sociodemográficos\", \"Datos obtenidos de otras fuentes\", \"Información de solvencia de la Central de Información de Riesgos del Banco de España (CIRBE)\", \"Información obtenida de los sistemas de información crediticia\", \"Datos de otros productos en los que participes como garante\", \"Datos de firma (incluida la firma digitalizada y electrónica)\", \"Datos de productos y servicios en los que intervengas\"], \"rights_listed\": [], \"authorities_mentioned\": [], \"laws_mentioned\": []}", + "parsed": { + "email": null, + "address": null + }, + "input_chars": 1487 + }, + { + "chunk_idx": 30, + "elapsed_s": 2.67, + "n_input_tokens": 528, + "n_output_tokens": 106, + "raw_text": "{\"data_controller\": {\"name\": \"Confirma Sistemas de Información, S.L.\", \"address\": \"Avda. de la Industria, 18, TRES CANTOS (28760) MADRID\", \"registration\": null}, \"dpo_contact\": {\"email\": \"dpogrupobbva@bbva.com\", \"address\": null}, \"data_categories\": [], \"rights_listed\": [], \"authorities_mentioned\": [], \"laws_mentioned\": []}", + "parsed": { + "email": "dpogrupobbva@bbva.com", + "address": null + }, + "input_chars": 1491 + }, + { + "chunk_idx": 60, + "elapsed_s": 1.77, + "n_input_tokens": 500, + "n_output_tokens": 71, + "raw_text": "{\"data_controller\": {\"name\": null, \"address\": null, \"registration\": null}, \"dpo_contact\": {\"email\": null, \"address\": null}, \"data_categories\": [], \"rights_listed\": [\"derecho fundamental a la protección de datos personales\"], \"authorities_mentioned\": [], \"laws_mentioned\": []}", + "parsed": { + "email": null, + "address": null + }, + "input_chars": 1409 + } + ], + "full_pdf_extrapolation": { + "avg_per_chunk_s": 3.2, + "n_chunks": 97, + "estimated_total_s": 310.4, + "estimated_total_min": 5.2 + } +} \ No newline at end of file diff --git a/openie_results.json b/openie_results.json new file mode 100644 index 0000000..7843b5f --- /dev/null +++ b/openie_results.json @@ -0,0 +1,681 @@ +{ + "corpus_en": { + "personal_simple": "John kissed Mary at the park.", + "personal_love": "Anna loves Bob and Bob admires Anna.", + "corporate_short": "Carlos Torres chairs BBVA which has its headquarters in Bilbao.", + "corporate_history": "Pablo Isla chaired Inditex from 2011 to 2022 and now serves on the board of Telefonica.", + "mixed_emotional": "After the meeting, Sarah hugged her brother Tom who had just graduated." + }, + "corpus_es": { + "personal_simple": "Enmanuel quiere a Ashlly desde hace anos.", + "personal_family": "Maria abrazo a su hermano Tomas tras la reunion.", + "corporate_short": "Carlos Torres preside BBVA, con sede central en Bilbao.", + "corporate_history": "Pablo Isla presidio Inditex de 2011 a 2022 y ahora forma parte del consejo de Telefonica.", + "mixed_emotional": "Despues de la cena, Sara llamo a su madre Lucia para contarle las noticias." + }, + "A_triplet_extract_en": { + "personal_simple": { + "text": "John kissed Mary at the park.", + "elapsed_s": 2.342, + "n_triples": 3, + "triples": [ + { + "subject": "John", + "relation": "kissed", + "object": "Mary", + "confidence": 1.0 + }, + { + "subject": "John", + "relation": "kissed at", + "object": "the park", + "confidence": 1.0 + }, + { + "subject": "John", + "relation": "kissed at", + "object": "park", + "confidence": 1.0 + } + ] + }, + "personal_love": { + "text": "Anna loves Bob and Bob admires Anna.", + "elapsed_s": 0.022, + "n_triples": 5, + "triples": [ + { + "subject": "Anna", + "relation": "loves", + "object": "Bob and Bob admires Anna", + "confidence": 1.0 + }, + { + "subject": "Anna", + "relation": "loves", + "object": "admires", + "confidence": 0.9 + }, + { + "subject": "Anna", + "relation": "loves", + "object": "Bob Bob admires Anna", + "confidence": 0.5 + }, + { + "subject": "Bob Bob", + "relation": "admires", + "object": "Anna", + "confidence": 0.5 + }, + { + "subject": "Anna", + "relation": "loves", + "object": "Bob and admires Anna", + "confidence": 0.5 + } + ] + }, + "corporate_short": { + "text": "Carlos Torres chairs BBVA which has its headquarters in Bilbao.", + "elapsed_s": 0.024, + "n_triples": 1, + "triples": [ + { + "subject": "which", + "relation": "has headquarters in", + "object": "Bilbao", + "confidence": 1.0 + } + ] + }, + "corporate_history": { + "text": "Pablo Isla chaired Inditex from 2011 to 2022 and now serves on the board of Telefonica.", + "elapsed_s": 0.044, + "n_triples": 7, + "triples": [ + { + "subject": "Pablo Isla", + "relation": "chaired", + "object": "Inditex", + "confidence": 1.0 + }, + { + "subject": "Pablo Isla", + "relation": "chaired from", + "object": "2011", + "confidence": 1.0 + }, + { + "subject": "Pablo Isla", + "relation": "chaired to", + "object": "2022", + "confidence": 0.5 + }, + { + "subject": "Pablo Isla", + "relation": "chaired now", + "object": "Inditex", + "confidence": 0.5 + }, + { + "subject": "Pablo Isla", + "relation": "chaired now from", + "object": "2011", + "confidence": 0.5 + }, + { + "subject": "Pablo Isla", + "relation": "chaired now to", + "object": "2022", + "confidence": 0.5 + }, + { + "subject": "Pablo Isla", + "relation": "chaired now on", + "object": "board of Telefonica", + "confidence": 0.5 + } + ] + }, + "mixed_emotional": { + "text": "After the meeting, Sarah hugged her brother Tom who had just graduated.", + "elapsed_s": 0.049, + "n_triples": 10, + "triples": [ + { + "subject": "Sarah", + "relation": "hugged", + "object": "her brother Tom who had just graduated", + "confidence": 1.0 + }, + { + "subject": "Sarah", + "relation": "hugged After", + "object": "the meeting", + "confidence": 1.0 + }, + { + "subject": "Sarah", + "relation": "hugged", + "object": "her brother Tom", + "confidence": 1.0 + }, + { + "subject": "Sarah", + "relation": "hugged", + "object": "who had just", + "confidence": 1.0 + }, + { + "subject": "Sarah", + "relation": "hugged", + "object": "her brother who had just graduated", + "confidence": 1.0 + }, + { + "subject": "Sarah", + "relation": "hugged After", + "object": "meeting", + "confidence": 1.0 + }, + { + "subject": "Sarah", + "relation": "hugged", + "object": "her brother who had just", + "confidence": 1.0 + }, + { + "subject": "Sarah", + "relation": "hugged", + "object": "her brother Tom who had graduated", + "confidence": 1.0 + }, + { + "subject": "Sarah", + "relation": "hugged", + "object": "her brother who just graduated", + "confidence": 0.38 + }, + { + "subject": "Sarah", + "relation": "hugged", + "object": "who had just graduated", + "confidence": 0.38 + } + ] + } + }, + "B_spacy_es_dep": { + "personal_simple": { + "text": "Enmanuel quiere a Ashlly desde hace anos.", + "elapsed_s": 0.005, + "n_triples": 1, + "n_ents": 2, + "triples": [ + { + "subject": "Enmanuel", + "relation": "querer", + "object": "a Ashlly", + "verb_form": "quiere" + } + ], + "entities": [ + { + "text": "Enmanuel", + "label": "PER" + }, + { + "text": "Ashlly", + "label": "PER" + } + ] + }, + "personal_family": { + "text": "Maria abrazo a su hermano Tomas tras la reunion.", + "elapsed_s": 0.003, + "n_triples": 0, + "n_ents": 2, + "triples": [], + "entities": [ + { + "text": "Maria", + "label": "PER" + }, + { + "text": "Tomas", + "label": "PER" + } + ] + }, + "corporate_short": { + "text": "Carlos Torres preside BBVA, con sede central en Bilbao.", + "elapsed_s": 0.004, + "n_triples": 3, + "n_ents": 3, + "triples": [ + { + "subject": "Carlos Torres", + "relation": "presidir", + "object": "BBVA", + "verb_form": "preside" + }, + { + "subject": "Carlos Torres", + "relation": "presidir", + "object": ", con sede central en Bilbao", + "verb_form": "preside" + }, + { + "subject": "Carlos Torres", + "relation": "presidir", + "object": ", con sede central en Bilbao", + "verb_form": "preside" + } + ], + "entities": [ + { + "text": "Carlos Torres", + "label": "PER" + }, + { + "text": "BBVA", + "label": "ORG" + }, + { + "text": "Bilbao", + "label": "LOC" + } + ] + }, + "corporate_history": { + "text": "Pablo Isla presidio Inditex de 2011 a 2022 y ahora forma parte del consejo de Telefonica.", + "elapsed_s": 0.005, + "n_triples": 1, + "n_ents": 2, + "triples": [ + { + "subject": "Pablo Isla presidio Inditex de 2011 a 2022", + "relation": "formar", + "object": "del consejo de Telefonica", + "verb_form": "forma" + } + ], + "entities": [ + { + "text": "Pablo Isla", + "label": "PER" + }, + { + "text": "Telefonica", + "label": "ORG" + } + ] + }, + "mixed_emotional": { + "text": "Despues de la cena, Sara llamo a su madre Lucia para contarle las noticias.", + "elapsed_s": 0.005, + "n_triples": 2, + "n_ents": 2, + "triples": [ + { + "subject": "Despues de la cena ,", + "relation": "llamo", + "object": "a su madre Lucia", + "verb_form": "llamo" + }, + { + "subject": "Sara", + "relation": "llamo", + "object": "a su madre Lucia", + "verb_form": "llamo" + } + ], + "entities": [ + { + "text": "Sara", + "label": "PER" + }, + { + "text": "Lucia", + "label": "PER" + } + ] + } + }, + "C_gliner2_universal_es": { + "personal_simple": { + "text": "Enmanuel quiere a Ashlly desde hace anos.", + "elapsed_s": 1.198, + "n_ents": 4, + "n_rels": 9, + "entities": { + "person": [ + "Enmanuel", + "Ashlly" + ], + "organization": [ + "Ashlly" + ], + "date": [ + "anos" + ] + }, + "relations": { + "loves": [ + [ + "Enmanuel", + "Ashlly" + ] + ], + "knows": [ + [ + "Enmanuel", + "Ashlly" + ] + ], + "kissed": [ + [ + "Enmanuel", + "Ashlly" + ] + ], + "hugged": [ + [ + "Enmanuel", + "Ashlly" + ] + ], + "founded_by": [ + [ + "Ashlly", + "Enmanuel" + ] + ], + "agreement_with": [ + [ + "Enmanuel", + "Ashlly" + ] + ], + "acquired": [ + [ + "Enmanuel", + "Ashlly" + ] + ], + "mentions": [ + [ + "Enmanuel", + "Ashlly" + ] + ], + "owns": [ + [ + "Enmanuel", + "Ashlly" + ] + ] + } + }, + "personal_family": { + "text": "Maria abrazo a su hermano Tomas tras la reunion.", + "elapsed_s": 1.271, + "n_ents": 3, + "n_rels": 10, + "entities": { + "person": [ + "Maria", + "Tomas" + ], + "event": [ + "reunion" + ] + }, + "relations": { + "loves": [ + [ + "Maria", + "Tomas" + ] + ], + "knows": [ + [ + "Maria", + "Tomas" + ] + ], + "parent_of": [ + [ + "Maria", + "Tomas" + ] + ], + "child_of": [ + [ + "Tomas", + "Maria" + ] + ], + "sibling_of": [ + [ + "Tomas", + "Tomas" + ] + ], + "friend_of": [ + [ + "Maria", + "Tomas" + ] + ], + "kissed": [ + [ + "Maria", + "Tomas" + ] + ], + "hugged": [ + [ + "Maria", + "Tomas" + ] + ], + "from": [ + [ + "Maria", + "Maria" + ] + ], + "mentions": [ + [ + "Maria", + "Tomas" + ] + ] + } + }, + "corporate_short": { + "text": "Carlos Torres preside BBVA, con sede central en Bilbao.", + "elapsed_s": 1.301, + "n_ents": 3, + "n_rels": 9, + "entities": { + "person": [ + "Carlos Torres" + ], + "organization": [ + "BBVA" + ], + "location": [ + "Bilbao" + ] + }, + "relations": { + "works_at": [ + [ + "Carlos Torres", + "BBVA" + ] + ], + "ceo_of": [ + [ + "Carlos Torres", + "BBVA" + ] + ], + "president_of": [ + [ + "Carlos Torres", + "BBVA" + ] + ], + "employed_by": [ + [ + "Carlos Torres", + "BBVA" + ] + ], + "located_in": [ + [ + "BBVA", + "Bilbao" + ] + ], + "headquartered_in": [ + [ + "BBVA", + "Bilbao" + ] + ], + "born_in": [ + [ + "Carlos Torres", + "Bilbao" + ] + ], + "lives_in": [ + [ + "Carlos Torres", + "Bilbao" + ] + ], + "founded_by": [ + [ + "BBVA", + "Carlos Torres" + ] + ] + } + }, + "corporate_history": { + "text": "Pablo Isla presidio Inditex de 2011 a 2022 y ahora forma parte del consejo de Telefonica.", + "elapsed_s": 1.149, + "n_ents": 4, + "n_rels": 2, + "entities": { + "person": [ + "Pablo Isla" + ], + "organization": [ + "consejo de Telefonica" + ], + "date": [ + "2022", + "2011" + ] + }, + "relations": { + "president_of": [ + [ + "Pablo Isla", + "consejo de Telefonica" + ] + ], + "agreement_with": [ + [ + "Pablo Isla", + "consejo de Telefonica" + ] + ] + } + }, + "mixed_emotional": { + "text": "Despues de la cena, Sara llamo a su madre Lucia para contarle las noticias.", + "elapsed_s": 1.217, + "n_ents": 3, + "n_rels": 10, + "entities": { + "person": [ + "Sara", + "Lucia" + ], + "event": [ + "cena" + ] + }, + "relations": { + "married_to": [ + [ + "Sara", + "Lucia" + ] + ], + "parent_of": [ + [ + "Lucia", + "Sara" + ] + ], + "child_of": [ + [ + "Sara", + "Lucia" + ] + ], + "sibling_of": [ + [ + "Sara", + "Sara" + ] + ], + "friend_of": [ + [ + "Sara", + "Lucia" + ] + ], + "kissed": [ + [ + "Sara", + "Lucia" + ] + ], + "from": [ + [ + "Sara", + "Lucia" + ] + ], + "agreement_with": [ + [ + "Sara", + "Lucia" + ] + ], + "related_to": [ + [ + "Sara", + "Lucia" + ] + ], + "mentions": [ + [ + "Sara", + "Lucia" + ] + ] + } + } + } +} \ No newline at end of file diff --git a/playground/index.html b/playground/index.html new file mode 100644 index 0000000..fe86944 --- /dev/null +++ b/playground/index.html @@ -0,0 +1,291 @@ + + + + +GLiNER2 Playground — graph_explorer + + + + + +
+
+

GLiNER2 Playground graph_explorer

+ + + +
+ + + +
+ +
+
—
nodos
+
—
relaciones
+
—
tiempo (s)
+
+ +
+
person
+
organization
+
location
+
+ +
+ 📰 Ej: corporate ES (Pablo Isla / Inditex) + 🔒 Ej: OSINT ES (APT-29 / CozyBear) + 🏦 Ej: banca ES (BBVA / Sabadell / OPA) +
+ +
+ Stack aplicado + 
1. snake_case verbal labels
+2. threshold (configurable)
+3. post-filter typed (head_type, tail_type)
+4. coreferencia normalize+substring
+5. chunking automatico > 1500 chars
+6. layout server-side (networkx spring_layout)
+7. render: sigma.js + graphology
+
+ +
+ Relaciones extraidas (texto) + 
(corre una extraccion para verlo)
+
+ +
+ Entidades extraidas por tipo + 
(corre una extraccion para verlo)
+
+ +
+ JSON completo + 
(corre una extraccion para verlo)
+
+ +
+ Relaciones descartadas por filtro typed + 
(corre una extraccion para verlo)
+
+
+
+
+
Pega un texto y pulsa Procesar
+
+
+ + + + diff --git a/playground/server.py b/playground/server.py new file mode 100644 index 0000000..2380151 --- /dev/null +++ b/playground/server.py @@ -0,0 +1,264 @@ +"""Playground server — GLiNER2 + post-filter typed sobre cualquier texto. + +Aplica las recetas del notebook 08: + - snake_case verbal labels + - threshold=0.3 + - post-filter por (head_type, tail_type) + - coreference simple normalize+substring + +Run: + cd playground && ../.venv/bin/python3 server.py +Luego: http://localhost:7878 +""" +from __future__ import annotations + +import os +import re +import sys +import time +import warnings +from collections import defaultdict +from pathlib import Path + +warnings.filterwarnings("ignore") +os.environ.setdefault("HF_HUB_DISABLE_PROGRESS_BARS", "1") + +# sys.path cleanup (mismo workaround documentado en notebook 08) +_pf = "/home/lucas/fn_registry/python/functions" +sys.path = [p for p in sys.path if not p.startswith(_pf + "/")] +if _pf not in sys.path: + sys.path.insert(0, _pf) + +from fastapi import FastAPI +from fastapi.responses import FileResponse, JSONResponse +from fastapi.staticfiles import StaticFiles +from pydantic import BaseModel +from gliner2 import GLiNER2 + +HERE = Path(__file__).resolve().parent + +# ── carga modelo una sola vez ── +print("[load] GLiNER2-large-v1 (CPU)...", flush=True) +t0 = time.time() +MODEL = GLiNER2.from_pretrained("fastino/gliner2-large-v1") +print(f"[load] done in {time.time()-t0:.1f}s", flush=True) + +# ── recetas del notebook 08 ── +ENTITY_LABELS = ["person", "organization", "location"] +RELATION_LABELS = [ + "works_at", "located_in", "ceo_of", "president_of", + "headquartered_in", "agreement_with", "subsidiary_of", "founded_by", +] +ALLOWED = { + "works_at": (["person"], ["organization"]), + "ceo_of": (["person"], ["organization"]), + "president_of": (["person"], ["organization"]), + "headquartered_in": (["organization"], ["location"]), + "located_in": (["organization", "person", "location"], ["location"]), + "agreement_with": (["organization"], ["organization"]), + "subsidiary_of": (["organization"], ["organization"]), + "founded_by": (["organization"], ["person"]), +} + + +def normalize_name(s: str) -> str: + s = re.sub(r"[\.,;:\"'`()\[\]]", "", s.strip()) + s = re.sub(r"\s+", " ", s) + return s.strip().lower() + + +def merge_aliases(names: list[str]) -> dict[str, str]: + norm_groups: dict = defaultdict(list) + for n in names: + norm_groups[normalize_name(n)].append(n) + canonical: dict = {} + for nrm, group in norm_groups.items(): + winner = max(group, key=lambda x: (len(x), x)) + for n in group: + canonical[n] = winner + canon_set = sorted(set(canonical.values()), key=len, reverse=True) + absorbed: dict = {} + for long_n in canon_set: + long_norm = normalize_name(long_n) + for short_n in canon_set: + if short_n == long_n or short_n in absorbed: + continue + short_norm = normalize_name(short_n) + if len(short_norm) < 4: + continue + if re.search(r"\b" + re.escape(short_norm) + r"\b", long_norm): + absorbed[short_n] = long_n + final: dict = {} + for orig, canon in canonical.items(): + final[orig] = absorbed.get(canon, canon) + return final + + +def filter_typed(rels: dict, name_to_type: dict, allowed: dict) -> tuple[list, list]: + keep: list = [] + drop: list = [] + for rt, pairs in rels.items(): + head_ok, tail_ok = allowed.get(rt, (None, None)) + for h, t in pairs: + ht = name_to_type.get(h.lower().strip()) + tt = name_to_type.get(t.lower().strip()) + if head_ok is None or (ht in head_ok and tt in tail_ok): + keep.append({"from": h, "kind": rt, "to": t, "head_type": ht, "tail_type": tt}) + else: + drop.append({"from": h, "kind": rt, "to": t, "head_type": ht, "tail_type": tt}) + return keep, drop + + +def chunk_text(text: str, max_chars: int = 1500, overlap_sentences: int = 2): + """Split largo en chunks con sliding window. Same pattern as notebook 06.""" + sentences = re.split(r"(?<=[\.!?])\s+", text) + sentences = [s.strip() for s in sentences if s.strip()] + chunks = [] + i = 0 + while i < len(sentences): + current_sents: list[str] = [] + current_len = 0 + if chunks and overlap_sentences > 0: + prev_sents = chunks[-1][-overlap_sentences:] + overlap_len = sum(len(s) + 1 for s in prev_sents) + next_sentence_len = len(sentences[i]) + 1 + if overlap_len + next_sentence_len <= max_chars: + current_sents = list(prev_sents) + current_len = overlap_len + if i < len(sentences): + current_sents.append(sentences[i]) + current_len += len(sentences[i]) + 1 + i += 1 + while i < len(sentences) and current_len + len(sentences[i]) + 1 <= max_chars: + current_sents.append(sentences[i]) + current_len += len(sentences[i]) + 1 + i += 1 + chunks.append(current_sents) + return [" ".join(c) for c in chunks] + + +def extract_graph(text: str, threshold: float = 0.3, max_chars_per_chunk: int = 1500) -> dict: + schema = MODEL.create_schema().entities(ENTITY_LABELS).relations(RELATION_LABELS) + + # Chunking automatico si el texto es largo + if len(text) <= max_chars_per_chunk: + chunks = [text] + else: + chunks = chunk_text(text, max_chars=max_chars_per_chunk, overlap_sentences=2) + print(f"[extract] {len(text)}c → {len(chunks)} chunks", flush=True) + + t0 = time.time() + + # Acumuladores deduplicados + name_to_type: dict = {} # name_lower → type (last seen wins) + name_canonical: dict = {} # name_lower → original casing + raw_relations: dict = {} # rel_type → list of (h, t) + + for idx, chunk in enumerate(chunks): + r = MODEL.extract(chunk, schema=schema, threshold=threshold) + for typ, names in r["entities"].items(): + for n in names: + key = n.lower().strip() + if not key: continue + if key not in name_to_type: + name_to_type[key] = typ + name_canonical[key] = n.strip() + # if seen with different name_canonical, keep the longer + elif len(n.strip()) > len(name_canonical[key]): + name_canonical[key] = n.strip() + for rt, pairs in r["relation_extraction"].items(): + if rt not in raw_relations: raw_relations[rt] = [] + for h, t in pairs: + raw_relations[rt].append((h.strip(), t.strip())) + if (idx + 1) % 10 == 0: + print(f"[extract] chunk {idx+1}/{len(chunks)} ents acum={len(name_to_type)} rels acum={sum(len(v) for v in raw_relations.values())}", flush=True) + + # Post-filter typed + keep, drop = filter_typed(raw_relations, name_to_type, ALLOWED) + + # Coreferencia: alias map sobre los canonical names + original_names = list(name_canonical.values()) + alias = merge_aliases(original_names) + + # Construir nodos con alias aplicado + nodes_dict: dict = {} + for key, typ in name_to_type.items(): + canon_orig = name_canonical[key] + canon_resolved = alias.get(canon_orig, canon_orig) + if canon_resolved not in nodes_dict: + nodes_dict[canon_resolved] = typ + + # Construir aristas dedupeadas tras alias + edges_set: set = set() + for e in keep: + h_canon = alias.get(e["from"], e["from"]) + t_canon = alias.get(e["to"], e["to"]) + if h_canon == t_canon: + continue + if h_canon not in nodes_dict: + nodes_dict[h_canon] = e.get("head_type") or "?" + if t_canon not in nodes_dict: + nodes_dict[t_canon] = e.get("tail_type") or "?" + edges_set.add((h_canon, e["kind"], t_canon)) + + # Layout server-side (sigma solo renderiza) + import networkx as nx + G = nx.DiGraph() + for n, t in nodes_dict.items(): + G.add_node(n) + for h, k, t in edges_set: + G.add_edge(h, t, kind=k) + if G.number_of_nodes() > 0: + try: + pos = nx.spring_layout(G, k=2.0, iterations=80, seed=42) + except Exception: + pos = {n: (0.0, 0.0) for n in G.nodes} + else: + pos = {} + + elapsed = time.time() - t0 + print(f"[extract] done {elapsed:.2f}s nodos={len(nodes_dict)} aristas={len(edges_set)}", flush=True) + + return { + "elapsed_s": round(elapsed, 2), + "n_chunks": len(chunks), + "n_nodes": len(nodes_dict), + "n_edges": len(edges_set), + "n_dropped_typed": len(drop), + "nodes": [ + {"id": n, "label": n, "type": t, + "x": float(pos.get(n, (0.0, 0.0))[0]), + "y": float(pos.get(n, (0.0, 0.0))[1])} + for n, t in nodes_dict.items() + ], + "edges": [{"from": h, "to": t, "label": k} for h, k, t in edges_set], + "dropped": drop[:10], + } + + +# ── API ── +app = FastAPI(title="GLiNER2 Playground") +app.mount("/static", StaticFiles(directory=HERE / "static"), name="static") + + +class ExtractReq(BaseModel): + text: str + threshold: float = 0.3 + + +@app.get("/") +def index(): + return FileResponse(HERE / "index.html") + + +@app.post("/extract") +def extract(req: ExtractReq): + if not req.text.strip(): + return JSONResponse({"error": "empty text"}, status_code=400) + return extract_graph(req.text, threshold=req.threshold) + + +if __name__ == "__main__": + import uvicorn + print("\nServing at http://localhost:7878\n", flush=True) + uvicorn.run(app, host="0.0.0.0", port=7878, log_level="warning") diff --git a/playground/static/graphology.umd.min.js b/playground/static/graphology.umd.min.js new file mode 100644 index 0000000..08ff6bc --- /dev/null +++ b/playground/static/graphology.umd.min.js @@ -0,0 +1,2 @@ +!function(t,e){"object"==typeof exports&&"undefined"!=typeof module?module.exports=e():"function"==typeof define&&define.amd?define(e):(t="undefined"!=typeof globalThis?globalThis:t||self).graphology=e()}(this,(function(){"use strict";function t(e){return t="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t},t(e)}function e(t,e){t.prototype=Object.create(e.prototype),t.prototype.constructor=t,r(t,e)}function n(t){return n=Object.setPrototypeOf?Object.getPrototypeOf.bind():function(t){return t.__proto__||Object.getPrototypeOf(t)},n(t)}function r(t,e){return r=Object.setPrototypeOf?Object.setPrototypeOf.bind():function(t,e){return t.__proto__=e,t},r(t,e)}function i(){if("undefined"==typeof Reflect||!Reflect.construct)return!1;if(Reflect.construct.sham)return!1;if("function"==typeof Proxy)return!0;try{return Boolean.prototype.valueOf.call(Reflect.construct(Boolean,[],(function(){}))),!0}catch(t){return!1}}function o(t,e,n){return o=i()?Reflect.construct.bind():function(t,e,n){var i=[null];i.push.apply(i,e);var o=new(Function.bind.apply(t,i));return n&&r(o,n.prototype),o},o.apply(null,arguments)}function a(t){var e="function"==typeof Map?new Map:void 0;return a=function(t){if(null===t||(i=t,-1===Function.toString.call(i).indexOf("[native code]")))return t;var i;if("function"!=typeof t)throw new TypeError("Super expression must either be null or a function");if(void 0!==e){if(e.has(t))return e.get(t);e.set(t,a)}function a(){return o(t,arguments,n(this).constructor)}return a.prototype=Object.create(t.prototype,{constructor:{value:a,enumerable:!1,writable:!0,configurable:!0}}),r(a,t)},a(t)}function c(t){if(void 0===t)throw new ReferenceError("this hasn't been initialised - super() hasn't been called");return t}var u=function(){for(var t=arguments[0],e=1,n=arguments.length;e0&&a.length>i&&!a.warned){a.warned=!0;var u=new Error("Possible EventEmitter memory leak detected. "+a.length+" "+String(e)+" listeners added. Use emitter.setMaxListeners() to increase limit");u.name="MaxListenersExceededWarning",u.emitter=t,u.type=e,u.count=a.length,c=u,console&&console.warn&&console.warn(c)}return t}function E(){if(!this.fired)return this.target.removeListener(this.type,this.wrapFn),this.fired=!0,0===arguments.length?this.listener.call(this.target):this.listener.apply(this.target,arguments)}function A(t,e,n){var r={fired:!1,wrapFn:void 0,target:t,type:e,listener:n},i=E.bind(r);return i.listener=n,r.wrapFn=i,i}function L(t,e,n){var r=t._events;if(void 0===r)return[];var i=r[e];return void 0===i?[]:"function"==typeof i?n?[i.listener||i]:[i]:n?function(t){for(var e=new Array(t.length),n=0;n0&&(o=e[0]),o instanceof Error)throw o;var a=new Error("Unhandled error."+(o?" ("+o.message+")":""));throw a.context=o,a}var c=i[t];if(void 0===c)return!1;if("function"==typeof c)v(c,this,e);else{var u=c.length,d=D(c,u);for(n=0;n=0;o--)if(n[o]===e||n[o].listener===e){a=n[o].listener,i=o;break}if(i<0)return this;0===i?n.shift():function(t,e){for(;e+1=0;r--)this.removeListener(t,e[r]);return this},m.prototype.listeners=function(t){return L(this,t,!0)},m.prototype.rawListeners=function(t){return L(this,t,!1)},m.listenerCount=function(t,e){return"function"==typeof t.listenerCount?t.listenerCount(e):S.call(t,e)},m.prototype.listenerCount=S,m.prototype.eventNames=function(){return this._eventsCount>0?g(this._events):[]},"undefined"!=typeof Symbol&&(N.prototype[Symbol.iterator]=function(){return this}),N.of=function(){var t=arguments,e=t.length,n=0;return new N((function(){return n>=e?{done:!0}:{done:!1,value:t[n++]}}))},N.empty=function(){return new N((function(){return{done:!0}}))},N.fromSequence=function(t){var e=0,n=t.length;return new N((function(){return e>=n?{done:!0}:{done:!1,value:t[e++]}}))},N.is=function(t){return t instanceof N||"object"==typeof t&&null!==t&&"function"==typeof t.next};var O=N,j={};j.ARRAY_BUFFER_SUPPORT="undefined"!=typeof ArrayBuffer,j.SYMBOL_SUPPORT="undefined"!=typeof Symbol;var C=O,M=j,z=M.ARRAY_BUFFER_SUPPORT,W=M.SYMBOL_SUPPORT;var P=function(t){var e=function(t){return"string"==typeof t||Array.isArray(t)||z&&ArrayBuffer.isView(t)?C.fromSequence(t):"object"!=typeof t||null===t?null:W&&"function"==typeof t[Symbol.iterator]?t[Symbol.iterator]():"function"==typeof t.next?t:null}(t);if(!e)throw new Error("obliterator: target is not iterable nor a valid iterator.");return e},R=P,K=function(t,e){for(var n,r=arguments.length>1?e:1/0,i=r!==1/0?new Array(r):[],o=0,a=R(t);;){if(o===r)return i;if((n=a.next()).done)return o!==e&&(i.length=o),i;i[o++]=n.value}},T=function(t){function n(e){var n;return(n=t.call(this)||this).name="GraphError",n.message=e,n}return e(n,t),n}(a(Error)),B=function(t){function n(e){var r;return(r=t.call(this,e)||this).name="InvalidArgumentsGraphError","function"==typeof Error.captureStackTrace&&Error.captureStackTrace(c(r),n.prototype.constructor),r}return e(n,t),n}(T),F=function(t){function n(e){var r;return(r=t.call(this,e)||this).name="NotFoundGraphError","function"==typeof Error.captureStackTrace&&Error.captureStackTrace(c(r),n.prototype.constructor),r}return e(n,t),n}(T),I=function(t){function n(e){var r;return(r=t.call(this,e)||this).name="UsageGraphError","function"==typeof Error.captureStackTrace&&Error.captureStackTrace(c(r),n.prototype.constructor),r}return e(n,t),n}(T);function Y(t,e){this.key=t,this.attributes=e,this.clear()}function q(t,e){this.key=t,this.attributes=e,this.clear()}function J(t,e){this.key=t,this.attributes=e,this.clear()}function V(t,e,n,r,i){this.key=e,this.attributes=i,this.undirected=t,this.source=n,this.target=r}Y.prototype.clear=function(){this.inDegree=0,this.outDegree=0,this.undirectedDegree=0,this.undirectedLoops=0,this.directedLoops=0,this.in={},this.out={},this.undirected={}},q.prototype.clear=function(){this.inDegree=0,this.outDegree=0,this.directedLoops=0,this.in={},this.out={}},J.prototype.clear=function(){this.undirectedDegree=0,this.undirectedLoops=0,this.undirected={}},V.prototype.attach=function(){var t="out",e="in";this.undirected&&(t=e="undirected");var n=this.source.key,r=this.target.key;this.source[t][r]=this,this.undirected&&n===r||(this.target[e][n]=this)},V.prototype.attachMulti=function(){var t="out",e="in",n=this.source.key,r=this.target.key;this.undirected&&(t=e="undirected");var i=this.source[t],o=i[r];if(void 0===o)return i[r]=this,void(this.undirected&&n===r||(this.target[e][n]=this));o.previous=this,this.next=o,i[r]=this,this.target[e][n]=this},V.prototype.detach=function(){var t=this.source.key,e=this.target.key,n="out",r="in";this.undirected&&(n=r="undirected"),delete this.source[n][e],delete this.target[r][t]},V.prototype.detachMulti=function(){var t=this.source.key,e=this.target.key,n="out",r="in";this.undirected&&(n=r="undirected"),void 0===this.previous?void 0===this.next?(delete this.source[n][e],delete this.target[r][t]):(this.next.previous=void 0,this.source[n][e]=this.next,this.target[r][t]=this.next):(this.previous.next=this.next,void 0!==this.next&&(this.next.previous=this.previous))};function H(t,e,n,r,i,o,a){var c,u,d,s;if(r=""+r,0===n){if(!(c=t._nodes.get(r)))throw new F("Graph.".concat(e,': could not find the "').concat(r,'" node in the graph.'));d=i,s=o}else if(3===n){if(i=""+i,!(u=t._edges.get(i)))throw new F("Graph.".concat(e,': could not find the "').concat(i,'" edge in the graph.'));var h=u.source.key,p=u.target.key;if(r===h)c=u.target;else{if(r!==p)throw new F("Graph.".concat(e,': the "').concat(r,'" node is not attached to the "').concat(i,'" edge (').concat(h,", ").concat(p,")."));c=u.source}d=o,s=a}else{if(!(u=t._edges.get(r)))throw new F("Graph.".concat(e,': could not find the "').concat(r,'" edge in the graph.'));c=1===n?u.source:u.target,d=i,s=o}return[c,d,s]}var Q=[{name:function(t){return"get".concat(t,"Attribute")},attacher:function(t,e,n){t.prototype[e]=function(t,r,i){var o=H(this,e,n,t,r,i),a=o[0],c=o[1];return a.attributes[c]}}},{name:function(t){return"get".concat(t,"Attributes")},attacher:function(t,e,n){t.prototype[e]=function(t,r){return H(this,e,n,t,r)[0].attributes}}},{name:function(t){return"has".concat(t,"Attribute")},attacher:function(t,e,n){t.prototype[e]=function(t,r,i){var o=H(this,e,n,t,r,i),a=o[0],c=o[1];return a.attributes.hasOwnProperty(c)}}},{name:function(t){return"set".concat(t,"Attribute")},attacher:function(t,e,n){t.prototype[e]=function(t,r,i,o){var a=H(this,e,n,t,r,i,o),c=a[0],u=a[1],d=a[2];return c.attributes[u]=d,this.emit("nodeAttributesUpdated",{key:c.key,type:"set",attributes:c.attributes,name:u}),this}}},{name:function(t){return"update".concat(t,"Attribute")},attacher:function(t,e,n){t.prototype[e]=function(t,r,i,o){var a=H(this,e,n,t,r,i,o),c=a[0],u=a[1],d=a[2];if("function"!=typeof d)throw new B("Graph.".concat(e,": updater should be a function."));var s=c.attributes,h=d(s[u]);return s[u]=h,this.emit("nodeAttributesUpdated",{key:c.key,type:"set",attributes:c.attributes,name:u}),this}}},{name:function(t){return"remove".concat(t,"Attribute")},attacher:function(t,e,n){t.prototype[e]=function(t,r,i){var o=H(this,e,n,t,r,i),a=o[0],c=o[1];return delete a.attributes[c],this.emit("nodeAttributesUpdated",{key:a.key,type:"remove",attributes:a.attributes,name:c}),this}}},{name:function(t){return"replace".concat(t,"Attributes")},attacher:function(t,e,n){t.prototype[e]=function(t,r,i){var o=H(this,e,n,t,r,i),a=o[0],c=o[1];if(!s(c))throw new B("Graph.".concat(e,": provided attributes are not a plain object."));return a.attributes=c,this.emit("nodeAttributesUpdated",{key:a.key,type:"replace",attributes:a.attributes}),this}}},{name:function(t){return"merge".concat(t,"Attributes")},attacher:function(t,e,n){t.prototype[e]=function(t,r,i){var o=H(this,e,n,t,r,i),a=o[0],c=o[1];if(!s(c))throw new B("Graph.".concat(e,": provided attributes are not a plain object."));return u(a.attributes,c),this.emit("nodeAttributesUpdated",{key:a.key,type:"merge",attributes:a.attributes,data:c}),this}}},{name:function(t){return"update".concat(t,"Attributes")},attacher:function(t,e,n){t.prototype[e]=function(t,r,i){var o=H(this,e,n,t,r,i),a=o[0],c=o[1];if("function"!=typeof c)throw new B("Graph.".concat(e,": provided updater is not a function."));return a.attributes=c(a.attributes),this.emit("nodeAttributesUpdated",{key:a.key,type:"update",attributes:a.attributes}),this}}}];var X=[{name:function(t){return"get".concat(t,"Attribute")},attacher:function(t,e,n){t.prototype[e]=function(t,r){var i;if("mixed"!==this.type&&"mixed"!==n&&n!==this.type)throw new I("Graph.".concat(e,": cannot find this type of edges in your ").concat(this.type," graph."));if(arguments.length>2){if(this.multi)throw new I("Graph.".concat(e,": cannot use a {source,target} combo when asking about an edge's attributes in a MultiGraph since we cannot infer the one you want information about."));var o=""+t,a=""+r;if(r=arguments[2],!(i=d(this,o,a,n)))throw new F("Graph.".concat(e,': could not find an edge for the given path ("').concat(o,'" - "').concat(a,'").'))}else{if("mixed"!==n)throw new I("Graph.".concat(e,": calling this method with only a key (vs. a source and target) does not make sense since an edge with this key could have the other type."));if(t=""+t,!(i=this._edges.get(t)))throw new F("Graph.".concat(e,': could not find the "').concat(t,'" edge in the graph.'))}return i.attributes[r]}}},{name:function(t){return"get".concat(t,"Attributes")},attacher:function(t,e,n){t.prototype[e]=function(t){var r;if("mixed"!==this.type&&"mixed"!==n&&n!==this.type)throw new I("Graph.".concat(e,": cannot find this type of edges in your ").concat(this.type," graph."));if(arguments.length>1){if(this.multi)throw new I("Graph.".concat(e,": cannot use a {source,target} combo when asking about an edge's attributes in a MultiGraph since we cannot infer the one you want information about."));var i=""+t,o=""+arguments[1];if(!(r=d(this,i,o,n)))throw new F("Graph.".concat(e,': could not find an edge for the given path ("').concat(i,'" - "').concat(o,'").'))}else{if("mixed"!==n)throw new I("Graph.".concat(e,": calling this method with only a key (vs. a source and target) does not make sense since an edge with this key could have the other type."));if(t=""+t,!(r=this._edges.get(t)))throw new F("Graph.".concat(e,': could not find the "').concat(t,'" edge in the graph.'))}return r.attributes}}},{name:function(t){return"has".concat(t,"Attribute")},attacher:function(t,e,n){t.prototype[e]=function(t,r){var i;if("mixed"!==this.type&&"mixed"!==n&&n!==this.type)throw new I("Graph.".concat(e,": cannot find this type of edges in your ").concat(this.type," graph."));if(arguments.length>2){if(this.multi)throw new I("Graph.".concat(e,": cannot use a {source,target} combo when asking about an edge's attributes in a MultiGraph since we cannot infer the one you want information about."));var o=""+t,a=""+r;if(r=arguments[2],!(i=d(this,o,a,n)))throw new F("Graph.".concat(e,': could not find an edge for the given path ("').concat(o,'" - "').concat(a,'").'))}else{if("mixed"!==n)throw new I("Graph.".concat(e,": calling this method with only a key (vs. a source and target) does not make sense since an edge with this key could have the other type."));if(t=""+t,!(i=this._edges.get(t)))throw new F("Graph.".concat(e,': could not find the "').concat(t,'" edge in the graph.'))}return i.attributes.hasOwnProperty(r)}}},{name:function(t){return"set".concat(t,"Attribute")},attacher:function(t,e,n){t.prototype[e]=function(t,r,i){var o;if("mixed"!==this.type&&"mixed"!==n&&n!==this.type)throw new I("Graph.".concat(e,": cannot find this type of edges in your ").concat(this.type," graph."));if(arguments.length>3){if(this.multi)throw new I("Graph.".concat(e,": cannot use a {source,target} combo when asking about an edge's attributes in a MultiGraph since we cannot infer the one you want information about."));var a=""+t,c=""+r;if(r=arguments[2],i=arguments[3],!(o=d(this,a,c,n)))throw new F("Graph.".concat(e,': could not find an edge for the given path ("').concat(a,'" - "').concat(c,'").'))}else{if("mixed"!==n)throw new I("Graph.".concat(e,": calling this method with only a key (vs. a source and target) does not make sense since an edge with this key could have the other type."));if(t=""+t,!(o=this._edges.get(t)))throw new F("Graph.".concat(e,': could not find the "').concat(t,'" edge in the graph.'))}return o.attributes[r]=i,this.emit("edgeAttributesUpdated",{key:o.key,type:"set",attributes:o.attributes,name:r}),this}}},{name:function(t){return"update".concat(t,"Attribute")},attacher:function(t,e,n){t.prototype[e]=function(t,r,i){var o;if("mixed"!==this.type&&"mixed"!==n&&n!==this.type)throw new I("Graph.".concat(e,": cannot find this type of edges in your ").concat(this.type," graph."));if(arguments.length>3){if(this.multi)throw new I("Graph.".concat(e,": cannot use a {source,target} combo when asking about an edge's attributes in a MultiGraph since we cannot infer the one you want information about."));var a=""+t,c=""+r;if(r=arguments[2],i=arguments[3],!(o=d(this,a,c,n)))throw new F("Graph.".concat(e,': could not find an edge for the given path ("').concat(a,'" - "').concat(c,'").'))}else{if("mixed"!==n)throw new I("Graph.".concat(e,": calling this method with only a key (vs. a source and target) does not make sense since an edge with this key could have the other type."));if(t=""+t,!(o=this._edges.get(t)))throw new F("Graph.".concat(e,': could not find the "').concat(t,'" edge in the graph.'))}if("function"!=typeof i)throw new B("Graph.".concat(e,": updater should be a function."));return o.attributes[r]=i(o.attributes[r]),this.emit("edgeAttributesUpdated",{key:o.key,type:"set",attributes:o.attributes,name:r}),this}}},{name:function(t){return"remove".concat(t,"Attribute")},attacher:function(t,e,n){t.prototype[e]=function(t,r){var i;if("mixed"!==this.type&&"mixed"!==n&&n!==this.type)throw new I("Graph.".concat(e,": cannot find this type of edges in your ").concat(this.type," graph."));if(arguments.length>2){if(this.multi)throw new I("Graph.".concat(e,": cannot use a {source,target} combo when asking about an edge's attributes in a MultiGraph since we cannot infer the one you want information about."));var o=""+t,a=""+r;if(r=arguments[2],!(i=d(this,o,a,n)))throw new F("Graph.".concat(e,': could not find an edge for the given path ("').concat(o,'" - "').concat(a,'").'))}else{if("mixed"!==n)throw new I("Graph.".concat(e,": calling this method with only a key (vs. a source and target) does not make sense since an edge with this key could have the other type."));if(t=""+t,!(i=this._edges.get(t)))throw new F("Graph.".concat(e,': could not find the "').concat(t,'" edge in the graph.'))}return delete i.attributes[r],this.emit("edgeAttributesUpdated",{key:i.key,type:"remove",attributes:i.attributes,name:r}),this}}},{name:function(t){return"replace".concat(t,"Attributes")},attacher:function(t,e,n){t.prototype[e]=function(t,r){var i;if("mixed"!==this.type&&"mixed"!==n&&n!==this.type)throw new I("Graph.".concat(e,": cannot find this type of edges in your ").concat(this.type," graph."));if(arguments.length>2){if(this.multi)throw new I("Graph.".concat(e,": cannot use a {source,target} combo when asking about an edge's attributes in a MultiGraph since we cannot infer the one you want information about."));var o=""+t,a=""+r;if(r=arguments[2],!(i=d(this,o,a,n)))throw new F("Graph.".concat(e,': could not find an edge for the given path ("').concat(o,'" - "').concat(a,'").'))}else{if("mixed"!==n)throw new I("Graph.".concat(e,": calling this method with only a key (vs. a source and target) does not make sense since an edge with this key could have the other type."));if(t=""+t,!(i=this._edges.get(t)))throw new F("Graph.".concat(e,': could not find the "').concat(t,'" edge in the graph.'))}if(!s(r))throw new B("Graph.".concat(e,": provided attributes are not a plain object."));return i.attributes=r,this.emit("edgeAttributesUpdated",{key:i.key,type:"replace",attributes:i.attributes}),this}}},{name:function(t){return"merge".concat(t,"Attributes")},attacher:function(t,e,n){t.prototype[e]=function(t,r){var i;if("mixed"!==this.type&&"mixed"!==n&&n!==this.type)throw new I("Graph.".concat(e,": cannot find this type of edges in your ").concat(this.type," graph."));if(arguments.length>2){if(this.multi)throw new I("Graph.".concat(e,": cannot use a {source,target} combo when asking about an edge's attributes in a MultiGraph since we cannot infer the one you want information about."));var o=""+t,a=""+r;if(r=arguments[2],!(i=d(this,o,a,n)))throw new F("Graph.".concat(e,': could not find an edge for the given path ("').concat(o,'" - "').concat(a,'").'))}else{if("mixed"!==n)throw new I("Graph.".concat(e,": calling this method with only a key (vs. a source and target) does not make sense since an edge with this key could have the other type."));if(t=""+t,!(i=this._edges.get(t)))throw new F("Graph.".concat(e,': could not find the "').concat(t,'" edge in the graph.'))}if(!s(r))throw new B("Graph.".concat(e,": provided attributes are not a plain object."));return u(i.attributes,r),this.emit("edgeAttributesUpdated",{key:i.key,type:"merge",attributes:i.attributes,data:r}),this}}},{name:function(t){return"update".concat(t,"Attributes")},attacher:function(t,e,n){t.prototype[e]=function(t,r){var i;if("mixed"!==this.type&&"mixed"!==n&&n!==this.type)throw new I("Graph.".concat(e,": cannot find this type of edges in your ").concat(this.type," graph."));if(arguments.length>2){if(this.multi)throw new I("Graph.".concat(e,": cannot use a {source,target} combo when asking about an edge's attributes in a MultiGraph since we cannot infer the one you want information about."));var o=""+t,a=""+r;if(r=arguments[2],!(i=d(this,o,a,n)))throw new F("Graph.".concat(e,': could not find an edge for the given path ("').concat(o,'" - "').concat(a,'").'))}else{if("mixed"!==n)throw new I("Graph.".concat(e,": calling this method with only a key (vs. a source and target) does not make sense since an edge with this key could have the other type."));if(t=""+t,!(i=this._edges.get(t)))throw new F("Graph.".concat(e,': could not find the "').concat(t,'" edge in the graph.'))}if("function"!=typeof r)throw new B("Graph.".concat(e,": provided updater is not a function."));return i.attributes=r(i.attributes),this.emit("edgeAttributesUpdated",{key:i.key,type:"update",attributes:i.attributes}),this}}}];var Z=O,$=P,tt=function(){var t=arguments,e=null,n=-1;return new Z((function(){for(var r=null;;){if(null===e){if(++n>=t.length)return{done:!0};e=$(t[n])}if(!0!==(r=e.next()).done)break;e=null}return r}))},et=[{name:"edges",type:"mixed"},{name:"inEdges",type:"directed",direction:"in"},{name:"outEdges",type:"directed",direction:"out"},{name:"inboundEdges",type:"mixed",direction:"in"},{name:"outboundEdges",type:"mixed",direction:"out"},{name:"directedEdges",type:"directed"},{name:"undirectedEdges",type:"undirected"}];function nt(t,e,n,r){var i=!1;for(var o in e)if(o!==r){var a=e[o];if(i=n(a.key,a.attributes,a.source.key,a.target.key,a.source.attributes,a.target.attributes,a.undirected),t&&i)return a.key}}function rt(t,e,n,r){var i,o,a,c=!1;for(var u in e)if(u!==r){i=e[u];do{if(o=i.source,a=i.target,c=n(i.key,i.attributes,o.key,a.key,o.attributes,a.attributes,i.undirected),t&&c)return i.key;i=i.next}while(void 0!==i)}}function it(t,e){var n,r=Object.keys(t),i=r.length,o=0;return new O((function(){do{if(n)n=n.next;else{if(o>=i)return{done:!0};var a=r[o++];if(a===e){n=void 0;continue}n=t[a]}}while(!n);return{done:!1,value:{edge:n.key,attributes:n.attributes,source:n.source.key,target:n.target.key,sourceAttributes:n.source.attributes,targetAttributes:n.target.attributes,undirected:n.undirected}}}))}function ot(t,e,n,r){var i=e[n];if(i){var o=i.source,a=i.target;return r(i.key,i.attributes,o.key,a.key,o.attributes,a.attributes,i.undirected)&&t?i.key:void 0}}function at(t,e,n,r){var i=e[n];if(i){var o=!1;do{if(o=r(i.key,i.attributes,i.source.key,i.target.key,i.source.attributes,i.target.attributes,i.undirected),t&&o)return i.key;i=i.next}while(void 0!==i)}}function ct(t,e){var n=t[e];return void 0!==n.next?new O((function(){if(!n)return{done:!0};var t={edge:n.key,attributes:n.attributes,source:n.source.key,target:n.target.key,sourceAttributes:n.source.attributes,targetAttributes:n.target.attributes,undirected:n.undirected};return n=n.next,{done:!1,value:t}})):O.of({edge:n.key,attributes:n.attributes,source:n.source.key,target:n.target.key,sourceAttributes:n.source.attributes,targetAttributes:n.target.attributes,undirected:n.undirected})}function ut(t,e){if(0===t.size)return[];if("mixed"===e||e===t.type)return"function"==typeof Array.from?Array.from(t._edges.keys()):K(t._edges.keys(),t._edges.size);for(var n,r,i="undirected"===e?t.undirectedSize:t.directedSize,o=new Array(i),a="undirected"===e,c=t._edges.values(),u=0;!0!==(n=c.next()).done;)(r=n.value).undirected===a&&(o[u++]=r.key);return o}function dt(t,e,n,r){if(0!==e.size)for(var i,o,a="mixed"!==n&&n!==e.type,c="undirected"===n,u=!1,d=e._edges.values();!0!==(i=d.next()).done;)if(o=i.value,!a||o.undirected===c){var s=o,h=s.key,p=s.attributes,f=s.source,l=s.target;if(u=r(h,p,f.key,l.key,f.attributes,l.attributes,o.undirected),t&&u)return h}}function st(t,e){if(0===t.size)return O.empty();var n="mixed"!==e&&e!==t.type,r="undirected"===e,i=t._edges.values();return new O((function(){for(var t,e;;){if((t=i.next()).done)return t;if(e=t.value,!n||e.undirected===r)break}return{value:{edge:e.key,attributes:e.attributes,source:e.source.key,target:e.target.key,sourceAttributes:e.source.attributes,targetAttributes:e.target.attributes,undirected:e.undirected},done:!1}}))}function ht(t,e,n,r,i,o){var a,c=e?rt:nt;if("undirected"!==n){if("out"!==r&&(a=c(t,i.in,o),t&&a))return a;if("in"!==r&&(a=c(t,i.out,o,r?void 0:i.key),t&&a))return a}if("directed"!==n&&(a=c(t,i.undirected,o),t&&a))return a}function pt(t,e,n,r){var i=[];return ht(!1,t,e,n,r,(function(t){i.push(t)})),i}function ft(t,e,n){var r=O.empty();return"undirected"!==t&&("out"!==e&&void 0!==n.in&&(r=tt(r,it(n.in))),"in"!==e&&void 0!==n.out&&(r=tt(r,it(n.out,e?void 0:n.key)))),"directed"!==t&&void 0!==n.undirected&&(r=tt(r,it(n.undirected))),r}function lt(t,e,n,r,i,o,a){var c,u=n?at:ot;if("undirected"!==e){if(void 0!==i.in&&"out"!==r&&(c=u(t,i.in,o,a),t&&c))return c;if(void 0!==i.out&&"in"!==r&&(r||i.key!==o)&&(c=u(t,i.out,o,a),t&&c))return c}if("directed"!==e&&void 0!==i.undirected&&(c=u(t,i.undirected,o,a),t&&c))return c}function gt(t,e,n,r,i){var o=[];return lt(!1,t,e,n,r,i,(function(t){o.push(t)})),o}function yt(t,e,n,r){var i=O.empty();return"undirected"!==t&&(void 0!==n.in&&"out"!==e&&r in n.in&&(i=tt(i,ct(n.in,r))),void 0!==n.out&&"in"!==e&&r in n.out&&(e||n.key!==r)&&(i=tt(i,ct(n.out,r)))),"directed"!==t&&void 0!==n.undirected&&r in n.undirected&&(i=tt(i,ct(n.undirected,r))),i}var wt=[{name:"neighbors",type:"mixed"},{name:"inNeighbors",type:"directed",direction:"in"},{name:"outNeighbors",type:"directed",direction:"out"},{name:"inboundNeighbors",type:"mixed",direction:"in"},{name:"outboundNeighbors",type:"mixed",direction:"out"},{name:"directedNeighbors",type:"directed"},{name:"undirectedNeighbors",type:"undirected"}];function vt(){this.A=null,this.B=null}function bt(t,e,n,r,i){for(var o in r){var a=r[o],c=a.source,u=a.target,d=c===n?u:c;if(!e||!e.has(d.key)){var s=i(d.key,d.attributes);if(t&&s)return d.key}}}function mt(t,e,n,r,i){if("mixed"!==e){if("undirected"===e)return bt(t,null,r,r.undirected,i);if("string"==typeof n)return bt(t,null,r,r[n],i)}var o,a=new vt;if("undirected"!==e){if("out"!==n){if(o=bt(t,null,r,r.in,i),t&&o)return o;a.wrap(r.in)}if("in"!==n){if(o=bt(t,a,r,r.out,i),t&&o)return o;a.wrap(r.out)}}if("directed"!==e&&(o=bt(t,a,r,r.undirected,i),t&&o))return o}function kt(t,e,n){var r=Object.keys(n),i=r.length,o=0;return new O((function(){var a=null;do{if(o>=i)return t&&t.wrap(n),{done:!0};var c=n[r[o++]],u=c.source,d=c.target;a=u===e?d:u,t&&t.has(a.key)&&(a=null)}while(null===a);return{done:!1,value:{neighbor:a.key,attributes:a.attributes}}}))}function _t(t,e){var n=e.name,r=e.type,i=e.direction;t.prototype[n]=function(t){if("mixed"!==r&&"mixed"!==this.type&&r!==this.type)return[];t=""+t;var e=this._nodes.get(t);if(void 0===e)throw new F("Graph.".concat(n,': could not find the "').concat(t,'" node in the graph.'));return function(t,e,n){if("mixed"!==t){if("undirected"===t)return Object.keys(n.undirected);if("string"==typeof e)return Object.keys(n[e])}var r=[];return mt(!1,t,e,n,(function(t){r.push(t)})),r}("mixed"===r?this.type:r,i,e)}}function Gt(t,e){var n=e.name,r=e.type,i=e.direction,o=n.slice(0,-1)+"Entries";t.prototype[o]=function(t){if("mixed"!==r&&"mixed"!==this.type&&r!==this.type)return O.empty();t=""+t;var e=this._nodes.get(t);if(void 0===e)throw new F("Graph.".concat(o,': could not find the "').concat(t,'" node in the graph.'));return function(t,e,n){if("mixed"!==t){if("undirected"===t)return kt(null,n,n.undirected);if("string"==typeof e)return kt(null,n,n[e])}var r=O.empty(),i=new vt;return"undirected"!==t&&("out"!==e&&(r=tt(r,kt(i,n,n.in))),"in"!==e&&(r=tt(r,kt(i,n,n.out)))),"directed"!==t&&(r=tt(r,kt(i,n,n.undirected))),r}("mixed"===r?this.type:r,i,e)}}function xt(t,e,n,r,i){for(var o,a,c,u,d,s,h,p=r._nodes.values(),f=r.type;!0!==(o=p.next()).done;){var l=!1;if(a=o.value,"undirected"!==f)for(c in u=a.out){d=u[c];do{if(s=d.target,l=!0,h=i(a.key,s.key,a.attributes,s.attributes,d.key,d.attributes,d.undirected),t&&h)return d;d=d.next}while(d)}if("directed"!==f)for(c in u=a.undirected)if(!(e&&a.key>c)){d=u[c];do{if((s=d.target).key!==c&&(s=d.source),l=!0,h=i(a.key,s.key,a.attributes,s.attributes,d.key,d.attributes,d.undirected),t&&h)return d;d=d.next}while(d)}if(n&&!l&&(h=i(a.key,null,a.attributes,null,null,null,null),t&&h))return null}}function Et(t){if(!s(t))throw new B('Graph.import: invalid serialized node. A serialized node should be a plain object with at least a "key" property.');if(!("key"in t))throw new B("Graph.import: serialized node is missing its key.");if("attributes"in t&&(!s(t.attributes)||null===t.attributes))throw new B("Graph.import: invalid attributes. Attributes should be a plain object, null or omitted.")}function At(t){if(!s(t))throw new B('Graph.import: invalid serialized edge. A serialized edge should be a plain object with at least a "source" & "target" property.');if(!("source"in t))throw new B("Graph.import: serialized edge is missing its source.");if(!("target"in t))throw new B("Graph.import: serialized edge is missing its target.");if("attributes"in t&&(!s(t.attributes)||null===t.attributes))throw new B("Graph.import: invalid attributes. Attributes should be a plain object, null or omitted.");if("undirected"in t&&"boolean"!=typeof t.undirected)throw new B("Graph.import: invalid undirectedness information. Undirected should be boolean or omitted.")}vt.prototype.wrap=function(t){null===this.A?this.A=t:null===this.B&&(this.B=t)},vt.prototype.has=function(t){return null!==this.A&&t in this.A||null!==this.B&&t in this.B};var Lt,St=(Lt=255&Math.floor(256*Math.random()),function(){return Lt++}),Dt=new Set(["directed","undirected","mixed"]),Ut=new Set(["domain","_events","_eventsCount","_maxListeners"]),Nt={allowSelfLoops:!0,multi:!1,type:"mixed"};function Ot(t,e,n){var r=new t.NodeDataClass(e,n);return t._nodes.set(e,r),t.emit("nodeAdded",{key:e,attributes:n}),r}function jt(t,e,n,r,i,o,a,c){if(!r&&"undirected"===t.type)throw new I("Graph.".concat(e,": you cannot add a directed edge to an undirected graph. Use the #.addEdge or #.addUndirectedEdge instead."));if(r&&"directed"===t.type)throw new I("Graph.".concat(e,": you cannot add an undirected edge to a directed graph. Use the #.addEdge or #.addDirectedEdge instead."));if(c&&!s(c))throw new B("Graph.".concat(e,': invalid attributes. Expecting an object but got "').concat(c,'"'));if(o=""+o,a=""+a,c=c||{},!t.allowSelfLoops&&o===a)throw new I("Graph.".concat(e,': source & target are the same ("').concat(o,"\"), thus creating a loop explicitly forbidden by this graph 'allowSelfLoops' option set to false."));var u=t._nodes.get(o),d=t._nodes.get(a);if(!u)throw new F("Graph.".concat(e,': source node "').concat(o,'" not found.'));if(!d)throw new F("Graph.".concat(e,': target node "').concat(a,'" not found.'));var h={key:null,undirected:r,source:o,target:a,attributes:c};if(n)i=t._edgeKeyGenerator();else if(i=""+i,t._edges.has(i))throw new I("Graph.".concat(e,': the "').concat(i,'" edge already exists in the graph.'));if(!t.multi&&(r?void 0!==u.undirected[a]:void 0!==u.out[a]))throw new I("Graph.".concat(e,': an edge linking "').concat(o,'" to "').concat(a,"\" already exists. If you really want to add multiple edges linking those nodes, you should create a multi graph by using the 'multi' option."));var p=new V(r,i,u,d,c);t._edges.set(i,p);var f=o===a;return r?(u.undirectedDegree++,d.undirectedDegree++,f&&(u.undirectedLoops++,t._undirectedSelfLoopCount++)):(u.outDegree++,d.inDegree++,f&&(u.directedLoops++,t._directedSelfLoopCount++)),t.multi?p.attachMulti():p.attach(),r?t._undirectedSize++:t._directedSize++,h.key=i,t.emit("edgeAdded",h),i}function Ct(t,e,n,r,i,o,a,c,d){if(!r&&"undirected"===t.type)throw new I("Graph.".concat(e,": you cannot merge/update a directed edge to an undirected graph. Use the #.mergeEdge/#.updateEdge or #.addUndirectedEdge instead."));if(r&&"directed"===t.type)throw new I("Graph.".concat(e,": you cannot merge/update an undirected edge to a directed graph. Use the #.mergeEdge/#.updateEdge or #.addDirectedEdge instead."));if(c)if(d){if("function"!=typeof c)throw new B("Graph.".concat(e,': invalid updater function. Expecting a function but got "').concat(c,'"'))}else if(!s(c))throw new B("Graph.".concat(e,': invalid attributes. Expecting an object but got "').concat(c,'"'));var h;if(o=""+o,a=""+a,d&&(h=c,c=void 0),!t.allowSelfLoops&&o===a)throw new I("Graph.".concat(e,': source & target are the same ("').concat(o,"\"), thus creating a loop explicitly forbidden by this graph 'allowSelfLoops' option set to false."));var p,f,l=t._nodes.get(o),g=t._nodes.get(a);if(!n&&(p=t._edges.get(i))){if(!(p.source.key===o&&p.target.key===a||r&&p.source.key===a&&p.target.key===o))throw new I("Graph.".concat(e,': inconsistency detected when attempting to merge the "').concat(i,'" edge with "').concat(o,'" source & "').concat(a,'" target vs. ("').concat(p.source.key,'", "').concat(p.target.key,'").'));f=p}if(f||t.multi||!l||(f=r?l.undirected[a]:l.out[a]),f){var y=[f.key,!1,!1,!1];if(d?!h:!c)return y;if(d){var w=f.attributes;f.attributes=h(w),t.emit("edgeAttributesUpdated",{type:"replace",key:f.key,attributes:f.attributes})}else u(f.attributes,c),t.emit("edgeAttributesUpdated",{type:"merge",key:f.key,attributes:f.attributes,data:c});return y}c=c||{},d&&h&&(c=h(c));var v={key:null,undirected:r,source:o,target:a,attributes:c};if(n)i=t._edgeKeyGenerator();else if(i=""+i,t._edges.has(i))throw new I("Graph.".concat(e,': the "').concat(i,'" edge already exists in the graph.'));var b=!1,m=!1;l||(l=Ot(t,o,{}),b=!0,o===a&&(g=l,m=!0)),g||(g=Ot(t,a,{}),m=!0),p=new V(r,i,l,g,c),t._edges.set(i,p);var k=o===a;return r?(l.undirectedDegree++,g.undirectedDegree++,k&&(l.undirectedLoops++,t._undirectedSelfLoopCount++)):(l.outDegree++,g.inDegree++,k&&(l.directedLoops++,t._directedSelfLoopCount++)),t.multi?p.attachMulti():p.attach(),r?t._undirectedSize++:t._directedSize++,v.key=i,t.emit("edgeAdded",v),[i,!0,b,m]}function Mt(t,e){t._edges.delete(e.key);var n=e.source,r=e.target,i=e.attributes,o=e.undirected,a=n===r;o?(n.undirectedDegree--,r.undirectedDegree--,a&&(n.undirectedLoops--,t._undirectedSelfLoopCount--)):(n.outDegree--,r.inDegree--,a&&(n.directedLoops--,t._directedSelfLoopCount--)),t.multi?e.detachMulti():e.detach(),o?t._undirectedSize--:t._directedSize--,t.emit("edgeDropped",{key:e.key,attributes:i,source:n.key,target:r.key,undirected:o})}var zt=function(n){function r(t){var e;if(e=n.call(this)||this,"boolean"!=typeof(t=u({},Nt,t)).multi)throw new B("Graph.constructor: invalid 'multi' option. Expecting a boolean but got \"".concat(t.multi,'".'));if(!Dt.has(t.type))throw new B('Graph.constructor: invalid \'type\' option. Should be one of "mixed", "directed" or "undirected" but got "'.concat(t.type,'".'));if("boolean"!=typeof t.allowSelfLoops)throw new B("Graph.constructor: invalid 'allowSelfLoops' option. Expecting a boolean but got \"".concat(t.allowSelfLoops,'".'));var r="mixed"===t.type?Y:"directed"===t.type?q:J;p(c(e),"NodeDataClass",r);var i="geid_"+St()+"_",o=0;return p(c(e),"_attributes",{}),p(c(e),"_nodes",new Map),p(c(e),"_edges",new Map),p(c(e),"_directedSize",0),p(c(e),"_undirectedSize",0),p(c(e),"_directedSelfLoopCount",0),p(c(e),"_undirectedSelfLoopCount",0),p(c(e),"_edgeKeyGenerator",(function(){var t;do{t=i+o++}while(e._edges.has(t));return t})),p(c(e),"_options",t),Ut.forEach((function(t){return p(c(e),t,e[t])})),f(c(e),"order",(function(){return e._nodes.size})),f(c(e),"size",(function(){return e._edges.size})),f(c(e),"directedSize",(function(){return e._directedSize})),f(c(e),"undirectedSize",(function(){return e._undirectedSize})),f(c(e),"selfLoopCount",(function(){return e._directedSelfLoopCount+e._undirectedSelfLoopCount})),f(c(e),"directedSelfLoopCount",(function(){return e._directedSelfLoopCount})),f(c(e),"undirectedSelfLoopCount",(function(){return e._undirectedSelfLoopCount})),f(c(e),"multi",e._options.multi),f(c(e),"type",e._options.type),f(c(e),"allowSelfLoops",e._options.allowSelfLoops),f(c(e),"implementation",(function(){return"graphology"})),e}e(r,n);var i=r.prototype;return i._resetInstanceCounters=function(){this._directedSize=0,this._undirectedSize=0,this._directedSelfLoopCount=0,this._undirectedSelfLoopCount=0},i.hasNode=function(t){return this._nodes.has(""+t)},i.hasDirectedEdge=function(t,e){if("undirected"===this.type)return!1;if(1===arguments.length){var n=""+t,r=this._edges.get(n);return!!r&&!r.undirected}if(2===arguments.length){t=""+t,e=""+e;var i=this._nodes.get(t);return!!i&&i.out.hasOwnProperty(e)}throw new B("Graph.hasDirectedEdge: invalid arity (".concat(arguments.length,", instead of 1 or 2). You can either ask for an edge id or for the existence of an edge between a source & a target."))},i.hasUndirectedEdge=function(t,e){if("directed"===this.type)return!1;if(1===arguments.length){var n=""+t,r=this._edges.get(n);return!!r&&r.undirected}if(2===arguments.length){t=""+t,e=""+e;var i=this._nodes.get(t);return!!i&&i.undirected.hasOwnProperty(e)}throw new B("Graph.hasDirectedEdge: invalid arity (".concat(arguments.length,", instead of 1 or 2). You can either ask for an edge id or for the existence of an edge between a source & a target."))},i.hasEdge=function(t,e){if(1===arguments.length){var n=""+t;return this._edges.has(n)}if(2===arguments.length){t=""+t,e=""+e;var r=this._nodes.get(t);return!!r&&(void 0!==r.out&&r.out.hasOwnProperty(e)||void 0!==r.undirected&&r.undirected.hasOwnProperty(e))}throw new B("Graph.hasEdge: invalid arity (".concat(arguments.length,", instead of 1 or 2). You can either ask for an edge id or for the existence of an edge between a source & a target."))},i.directedEdge=function(t,e){if("undirected"!==this.type){if(t=""+t,e=""+e,this.multi)throw new I("Graph.directedEdge: this method is irrelevant with multigraphs since there might be multiple edges between source & target. See #.directedEdges instead.");var n=this._nodes.get(t);if(!n)throw new F('Graph.directedEdge: could not find the "'.concat(t,'" source node in the graph.'));if(!this._nodes.has(e))throw new F('Graph.directedEdge: could not find the "'.concat(e,'" target node in the graph.'));var r=n.out&&n.out[e]||void 0;return r?r.key:void 0}},i.undirectedEdge=function(t,e){if("directed"!==this.type){if(t=""+t,e=""+e,this.multi)throw new I("Graph.undirectedEdge: this method is irrelevant with multigraphs since there might be multiple edges between source & target. See #.undirectedEdges instead.");var n=this._nodes.get(t);if(!n)throw new F('Graph.undirectedEdge: could not find the "'.concat(t,'" source node in the graph.'));if(!this._nodes.has(e))throw new F('Graph.undirectedEdge: could not find the "'.concat(e,'" target node in the graph.'));var r=n.undirected&&n.undirected[e]||void 0;return r?r.key:void 0}},i.edge=function(t,e){if(this.multi)throw new I("Graph.edge: this method is irrelevant with multigraphs since there might be multiple edges between source & target. See #.edges instead.");t=""+t,e=""+e;var n=this._nodes.get(t);if(!n)throw new F('Graph.edge: could not find the "'.concat(t,'" source node in the graph.'));if(!this._nodes.has(e))throw new F('Graph.edge: could not find the "'.concat(e,'" target node in the graph.'));var r=n.out&&n.out[e]||n.undirected&&n.undirected[e]||void 0;if(r)return r.key},i.areDirectedNeighbors=function(t,e){t=""+t,e=""+e;var n=this._nodes.get(t);if(!n)throw new F('Graph.areDirectedNeighbors: could not find the "'.concat(t,'" node in the graph.'));return"undirected"!==this.type&&(e in n.in||e in n.out)},i.areOutNeighbors=function(t,e){t=""+t,e=""+e;var n=this._nodes.get(t);if(!n)throw new F('Graph.areOutNeighbors: could not find the "'.concat(t,'" node in the graph.'));return"undirected"!==this.type&&e in n.out},i.areInNeighbors=function(t,e){t=""+t,e=""+e;var n=this._nodes.get(t);if(!n)throw new F('Graph.areInNeighbors: could not find the "'.concat(t,'" node in the graph.'));return"undirected"!==this.type&&e in n.in},i.areUndirectedNeighbors=function(t,e){t=""+t,e=""+e;var n=this._nodes.get(t);if(!n)throw new F('Graph.areUndirectedNeighbors: could not find the "'.concat(t,'" node in the graph.'));return"directed"!==this.type&&e in n.undirected},i.areNeighbors=function(t,e){t=""+t,e=""+e;var n=this._nodes.get(t);if(!n)throw new F('Graph.areNeighbors: could not find the "'.concat(t,'" node in the graph.'));return"undirected"!==this.type&&(e in n.in||e in n.out)||"directed"!==this.type&&e in n.undirected},i.areInboundNeighbors=function(t,e){t=""+t,e=""+e;var n=this._nodes.get(t);if(!n)throw new F('Graph.areInboundNeighbors: could not find the "'.concat(t,'" node in the graph.'));return"undirected"!==this.type&&e in n.in||"directed"!==this.type&&e in n.undirected},i.areOutboundNeighbors=function(t,e){t=""+t,e=""+e;var n=this._nodes.get(t);if(!n)throw new F('Graph.areOutboundNeighbors: could not find the "'.concat(t,'" node in the graph.'));return"undirected"!==this.type&&e in n.out||"directed"!==this.type&&e in n.undirected},i.inDegree=function(t){t=""+t;var e=this._nodes.get(t);if(!e)throw new F('Graph.inDegree: could not find the "'.concat(t,'" node in the graph.'));return"undirected"===this.type?0:e.inDegree},i.outDegree=function(t){t=""+t;var e=this._nodes.get(t);if(!e)throw new F('Graph.outDegree: could not find the "'.concat(t,'" node in the graph.'));return"undirected"===this.type?0:e.outDegree},i.directedDegree=function(t){t=""+t;var e=this._nodes.get(t);if(!e)throw new F('Graph.directedDegree: could not find the "'.concat(t,'" node in the graph.'));return"undirected"===this.type?0:e.inDegree+e.outDegree},i.undirectedDegree=function(t){t=""+t;var e=this._nodes.get(t);if(!e)throw new F('Graph.undirectedDegree: could not find the "'.concat(t,'" node in the graph.'));return"directed"===this.type?0:e.undirectedDegree},i.inboundDegree=function(t){t=""+t;var e=this._nodes.get(t);if(!e)throw new F('Graph.inboundDegree: could not find the "'.concat(t,'" node in the graph.'));var n=0;return"directed"!==this.type&&(n+=e.undirectedDegree),"undirected"!==this.type&&(n+=e.inDegree),n},i.outboundDegree=function(t){t=""+t;var e=this._nodes.get(t);if(!e)throw new F('Graph.outboundDegree: could not find the "'.concat(t,'" node in the graph.'));var n=0;return"directed"!==this.type&&(n+=e.undirectedDegree),"undirected"!==this.type&&(n+=e.outDegree),n},i.degree=function(t){t=""+t;var e=this._nodes.get(t);if(!e)throw new F('Graph.degree: could not find the "'.concat(t,'" node in the graph.'));var n=0;return"directed"!==this.type&&(n+=e.undirectedDegree),"undirected"!==this.type&&(n+=e.inDegree+e.outDegree),n},i.inDegreeWithoutSelfLoops=function(t){t=""+t;var e=this._nodes.get(t);if(!e)throw new F('Graph.inDegreeWithoutSelfLoops: could not find the "'.concat(t,'" node in the graph.'));return"undirected"===this.type?0:e.inDegree-e.directedLoops},i.outDegreeWithoutSelfLoops=function(t){t=""+t;var e=this._nodes.get(t);if(!e)throw new F('Graph.outDegreeWithoutSelfLoops: could not find the "'.concat(t,'" node in the graph.'));return"undirected"===this.type?0:e.outDegree-e.directedLoops},i.directedDegreeWithoutSelfLoops=function(t){t=""+t;var e=this._nodes.get(t);if(!e)throw new F('Graph.directedDegreeWithoutSelfLoops: could not find the "'.concat(t,'" node in the graph.'));return"undirected"===this.type?0:e.inDegree+e.outDegree-2*e.directedLoops},i.undirectedDegreeWithoutSelfLoops=function(t){t=""+t;var e=this._nodes.get(t);if(!e)throw new F('Graph.undirectedDegreeWithoutSelfLoops: could not find the "'.concat(t,'" node in the graph.'));return"directed"===this.type?0:e.undirectedDegree-2*e.undirectedLoops},i.inboundDegreeWithoutSelfLoops=function(t){t=""+t;var e=this._nodes.get(t);if(!e)throw new F('Graph.inboundDegreeWithoutSelfLoops: could not find the "'.concat(t,'" node in the graph.'));var n=0,r=0;return"directed"!==this.type&&(n+=e.undirectedDegree,r+=2*e.undirectedLoops),"undirected"!==this.type&&(n+=e.inDegree,r+=e.directedLoops),n-r},i.outboundDegreeWithoutSelfLoops=function(t){t=""+t;var e=this._nodes.get(t);if(!e)throw new F('Graph.outboundDegreeWithoutSelfLoops: could not find the "'.concat(t,'" node in the graph.'));var n=0,r=0;return"directed"!==this.type&&(n+=e.undirectedDegree,r+=2*e.undirectedLoops),"undirected"!==this.type&&(n+=e.outDegree,r+=e.directedLoops),n-r},i.degreeWithoutSelfLoops=function(t){t=""+t;var e=this._nodes.get(t);if(!e)throw new F('Graph.degreeWithoutSelfLoops: could not find the "'.concat(t,'" node in the graph.'));var n=0,r=0;return"directed"!==this.type&&(n+=e.undirectedDegree,r+=2*e.undirectedLoops),"undirected"!==this.type&&(n+=e.inDegree+e.outDegree,r+=2*e.directedLoops),n-r},i.source=function(t){t=""+t;var e=this._edges.get(t);if(!e)throw new F('Graph.source: could not find the "'.concat(t,'" edge in the graph.'));return e.source.key},i.target=function(t){t=""+t;var e=this._edges.get(t);if(!e)throw new F('Graph.target: could not find the "'.concat(t,'" edge in the graph.'));return e.target.key},i.extremities=function(t){t=""+t;var e=this._edges.get(t);if(!e)throw new F('Graph.extremities: could not find the "'.concat(t,'" edge in the graph.'));return[e.source.key,e.target.key]},i.opposite=function(t,e){t=""+t,e=""+e;var n=this._edges.get(e);if(!n)throw new F('Graph.opposite: could not find the "'.concat(e,'" edge in the graph.'));var r=n.source.key,i=n.target.key;if(t===r)return i;if(t===i)return r;throw new F('Graph.opposite: the "'.concat(t,'" node is not attached to the "').concat(e,'" edge (').concat(r,", ").concat(i,")."))},i.hasExtremity=function(t,e){t=""+t,e=""+e;var n=this._edges.get(t);if(!n)throw new F('Graph.hasExtremity: could not find the "'.concat(t,'" edge in the graph.'));return n.source.key===e||n.target.key===e},i.isUndirected=function(t){t=""+t;var e=this._edges.get(t);if(!e)throw new F('Graph.isUndirected: could not find the "'.concat(t,'" edge in the graph.'));return e.undirected},i.isDirected=function(t){t=""+t;var e=this._edges.get(t);if(!e)throw new F('Graph.isDirected: could not find the "'.concat(t,'" edge in the graph.'));return!e.undirected},i.isSelfLoop=function(t){t=""+t;var e=this._edges.get(t);if(!e)throw new F('Graph.isSelfLoop: could not find the "'.concat(t,'" edge in the graph.'));return e.source===e.target},i.addNode=function(t,e){var n=function(t,e,n){if(n&&!s(n))throw new B('Graph.addNode: invalid attributes. Expecting an object but got "'.concat(n,'"'));if(e=""+e,n=n||{},t._nodes.has(e))throw new I('Graph.addNode: the "'.concat(e,'" node already exist in the graph.'));var r=new t.NodeDataClass(e,n);return t._nodes.set(e,r),t.emit("nodeAdded",{key:e,attributes:n}),r}(this,t,e);return n.key},i.mergeNode=function(t,e){if(e&&!s(e))throw new B('Graph.mergeNode: invalid attributes. Expecting an object but got "'.concat(e,'"'));t=""+t,e=e||{};var n=this._nodes.get(t);return n?(e&&(u(n.attributes,e),this.emit("nodeAttributesUpdated",{type:"merge",key:t,attributes:n.attributes,data:e})),[t,!1]):(n=new this.NodeDataClass(t,e),this._nodes.set(t,n),this.emit("nodeAdded",{key:t,attributes:e}),[t,!0])},i.updateNode=function(t,e){if(e&&"function"!=typeof e)throw new B('Graph.updateNode: invalid updater function. Expecting a function but got "'.concat(e,'"'));t=""+t;var n=this._nodes.get(t);if(n){if(e){var r=n.attributes;n.attributes=e(r),this.emit("nodeAttributesUpdated",{type:"replace",key:t,attributes:n.attributes})}return[t,!1]}var i=e?e({}):{};return n=new this.NodeDataClass(t,i),this._nodes.set(t,n),this.emit("nodeAdded",{key:t,attributes:i}),[t,!0]},i.dropNode=function(t){t=""+t;var e,n=this._nodes.get(t);if(!n)throw new F('Graph.dropNode: could not find the "'.concat(t,'" node in the graph.'));if("undirected"!==this.type){for(var r in n.out){e=n.out[r];do{Mt(this,e),e=e.next}while(e)}for(var i in n.in){e=n.in[i];do{Mt(this,e),e=e.next}while(e)}}if("directed"!==this.type)for(var o in n.undirected){e=n.undirected[o];do{Mt(this,e),e=e.next}while(e)}this._nodes.delete(t),this.emit("nodeDropped",{key:t,attributes:n.attributes})},i.dropEdge=function(t){var e;if(arguments.length>1){var n=""+arguments[0],r=""+arguments[1];if(!(e=d(this,n,r,this.type)))throw new F('Graph.dropEdge: could not find the "'.concat(n,'" -> "').concat(r,'" edge in the graph.'))}else if(t=""+t,!(e=this._edges.get(t)))throw new F('Graph.dropEdge: could not find the "'.concat(t,'" edge in the graph.'));return Mt(this,e),this},i.dropDirectedEdge=function(t,e){if(arguments.length<2)throw new I("Graph.dropDirectedEdge: it does not make sense to try and drop a directed edge by key. What if the edge with this key is undirected? Use #.dropEdge for this purpose instead.");if(this.multi)throw new I("Graph.dropDirectedEdge: cannot use a {source,target} combo when dropping an edge in a MultiGraph since we cannot infer the one you want to delete as there could be multiple ones.");var n=d(this,t=""+t,e=""+e,"directed");if(!n)throw new F('Graph.dropDirectedEdge: could not find a "'.concat(t,'" -> "').concat(e,'" edge in the graph.'));return Mt(this,n),this},i.dropUndirectedEdge=function(t,e){if(arguments.length<2)throw new I("Graph.dropUndirectedEdge: it does not make sense to drop a directed edge by key. What if the edge with this key is undirected? Use #.dropEdge for this purpose instead.");if(this.multi)throw new I("Graph.dropUndirectedEdge: cannot use a {source,target} combo when dropping an edge in a MultiGraph since we cannot infer the one you want to delete as there could be multiple ones.");var n=d(this,t,e,"undirected");if(!n)throw new F('Graph.dropUndirectedEdge: could not find a "'.concat(t,'" -> "').concat(e,'" edge in the graph.'));return Mt(this,n),this},i.clear=function(){this._edges.clear(),this._nodes.clear(),this._resetInstanceCounters(),this.emit("cleared")},i.clearEdges=function(){for(var t,e=this._nodes.values();!0!==(t=e.next()).done;)t.value.clear();this._edges.clear(),this._resetInstanceCounters(),this.emit("edgesCleared")},i.getAttribute=function(t){return this._attributes[t]},i.getAttributes=function(){return this._attributes},i.hasAttribute=function(t){return this._attributes.hasOwnProperty(t)},i.setAttribute=function(t,e){return this._attributes[t]=e,this.emit("attributesUpdated",{type:"set",attributes:this._attributes,name:t}),this},i.updateAttribute=function(t,e){if("function"!=typeof e)throw new B("Graph.updateAttribute: updater should be a function.");var n=this._attributes[t];return this._attributes[t]=e(n),this.emit("attributesUpdated",{type:"set",attributes:this._attributes,name:t}),this},i.removeAttribute=function(t){return delete this._attributes[t],this.emit("attributesUpdated",{type:"remove",attributes:this._attributes,name:t}),this},i.replaceAttributes=function(t){if(!s(t))throw new B("Graph.replaceAttributes: provided attributes are not a plain object.");return this._attributes=t,this.emit("attributesUpdated",{type:"replace",attributes:this._attributes}),this},i.mergeAttributes=function(t){if(!s(t))throw new B("Graph.mergeAttributes: provided attributes are not a plain object.");return u(this._attributes,t),this.emit("attributesUpdated",{type:"merge",attributes:this._attributes,data:t}),this},i.updateAttributes=function(t){if("function"!=typeof t)throw new B("Graph.updateAttributes: provided updater is not a function.");return this._attributes=t(this._attributes),this.emit("attributesUpdated",{type:"update",attributes:this._attributes}),this},i.updateEachNodeAttributes=function(t,e){if("function"!=typeof t)throw new B("Graph.updateEachNodeAttributes: expecting an updater function.");if(e&&!l(e))throw new B("Graph.updateEachNodeAttributes: invalid hints. Expecting an object having the following shape: {attributes?: [string]}");for(var n,r,i=this._nodes.values();!0!==(n=i.next()).done;)(r=n.value).attributes=t(r.key,r.attributes);this.emit("eachNodeAttributesUpdated",{hints:e||null})},i.updateEachEdgeAttributes=function(t,e){if("function"!=typeof t)throw new B("Graph.updateEachEdgeAttributes: expecting an updater function.");if(e&&!l(e))throw new B("Graph.updateEachEdgeAttributes: invalid hints. Expecting an object having the following shape: {attributes?: [string]}");for(var n,r,i,o,a=this._edges.values();!0!==(n=a.next()).done;)i=(r=n.value).source,o=r.target,r.attributes=t(r.key,r.attributes,i.key,o.key,i.attributes,o.attributes,r.undirected);this.emit("eachEdgeAttributesUpdated",{hints:e||null})},i.forEachAdjacencyEntry=function(t){if("function"!=typeof t)throw new B("Graph.forEachAdjacencyEntry: expecting a callback.");xt(!1,!1,!1,this,t)},i.forEachAdjacencyEntryWithOrphans=function(t){if("function"!=typeof t)throw new B("Graph.forEachAdjacencyEntryWithOrphans: expecting a callback.");xt(!1,!1,!0,this,t)},i.forEachAssymetricAdjacencyEntry=function(t){if("function"!=typeof t)throw new B("Graph.forEachAssymetricAdjacencyEntry: expecting a callback.");xt(!1,!0,!1,this,t)},i.forEachAssymetricAdjacencyEntryWithOrphans=function(t){if("function"!=typeof t)throw new B("Graph.forEachAssymetricAdjacencyEntryWithOrphans: expecting a callback.");xt(!1,!0,!0,this,t)},i.nodes=function(){return"function"==typeof Array.from?Array.from(this._nodes.keys()):K(this._nodes.keys(),this._nodes.size)},i.forEachNode=function(t){if("function"!=typeof t)throw new B("Graph.forEachNode: expecting a callback.");for(var e,n,r=this._nodes.values();!0!==(e=r.next()).done;)t((n=e.value).key,n.attributes)},i.findNode=function(t){if("function"!=typeof t)throw new B("Graph.findNode: expecting a callback.");for(var e,n,r=this._nodes.values();!0!==(e=r.next()).done;)if(t((n=e.value).key,n.attributes))return n.key},i.mapNodes=function(t){if("function"!=typeof t)throw new B("Graph.mapNode: expecting a callback.");for(var e,n,r=this._nodes.values(),i=new Array(this.order),o=0;!0!==(e=r.next()).done;)n=e.value,i[o++]=t(n.key,n.attributes);return i},i.someNode=function(t){if("function"!=typeof t)throw new B("Graph.someNode: expecting a callback.");for(var e,n,r=this._nodes.values();!0!==(e=r.next()).done;)if(t((n=e.value).key,n.attributes))return!0;return!1},i.everyNode=function(t){if("function"!=typeof t)throw new B("Graph.everyNode: expecting a callback.");for(var e,n,r=this._nodes.values();!0!==(e=r.next()).done;)if(!t((n=e.value).key,n.attributes))return!1;return!0},i.filterNodes=function(t){if("function"!=typeof t)throw new B("Graph.filterNodes: expecting a callback.");for(var e,n,r=this._nodes.values(),i=[];!0!==(e=r.next()).done;)t((n=e.value).key,n.attributes)&&i.push(n.key);return i},i.reduceNodes=function(t,e){if("function"!=typeof t)throw new B("Graph.reduceNodes: expecting a callback.");if(arguments.length<2)throw new B("Graph.reduceNodes: missing initial value. You must provide it because the callback takes more than one argument and we cannot infer the initial value from the first iteration, as you could with a simple array.");for(var n,r,i=e,o=this._nodes.values();!0!==(n=o.next()).done;)i=t(i,(r=n.value).key,r.attributes);return i},i.nodeEntries=function(){var t=this._nodes.values();return new O((function(){var e=t.next();if(e.done)return e;var n=e.value;return{value:{node:n.key,attributes:n.attributes},done:!1}}))},i.export=function(){var t=this,e=new Array(this._nodes.size),n=0;this._nodes.forEach((function(t,r){e[n++]=function(t,e){var n={key:t};return h(e.attributes)||(n.attributes=u({},e.attributes)),n}(r,t)}));var r=new Array(this._edges.size);return n=0,this._edges.forEach((function(e,i){r[n++]=function(t,e,n){var r={key:e,source:n.source.key,target:n.target.key};return h(n.attributes)||(r.attributes=u({},n.attributes)),"mixed"===t&&n.undirected&&(r.undirected=!0),r}(t.type,i,e)})),{options:{type:this.type,multi:this.multi,allowSelfLoops:this.allowSelfLoops},attributes:this.getAttributes(),nodes:e,edges:r}},i.import=function(t){var e,n,i,o,a,c=this,u=arguments.length>1&&void 0!==arguments[1]&&arguments[1];if(t instanceof r)return t.forEachNode((function(t,e){u?c.mergeNode(t,e):c.addNode(t,e)})),t.forEachEdge((function(t,e,n,r,i,o,a){u?a?c.mergeUndirectedEdgeWithKey(t,n,r,e):c.mergeDirectedEdgeWithKey(t,n,r,e):a?c.addUndirectedEdgeWithKey(t,n,r,e):c.addDirectedEdgeWithKey(t,n,r,e)})),this;if(!s(t))throw new B("Graph.import: invalid argument. Expecting a serialized graph or, alternatively, a Graph instance.");if(t.attributes){if(!s(t.attributes))throw new B("Graph.import: invalid attributes. Expecting a plain object.");u?this.mergeAttributes(t.attributes):this.replaceAttributes(t.attributes)}if(t.nodes){if(i=t.nodes,!Array.isArray(i))throw new B("Graph.import: invalid nodes. Expecting an array.");for(e=0,n=i.length;e",c="",u=t.source.key,d=t.target.key;t.undirected&&u>d&&(o=u,u=d,d=o);var s="(".concat(u,")").concat(a,"(").concat(d,")");n.startsWith("geid_")?e.multi&&(void 0===i[s]?i[s]=0:i[s]++,c+="".concat(i[s],". ")):c+="[".concat(n,"]: "),r[c+=s]=t.attributes}));var o={};for(var a in this)this.hasOwnProperty(a)&&!Ut.has(a)&&"function"!=typeof this[a]&&"symbol"!==t(a)&&(o[a]=this[a]);return o.attributes=this._attributes,o.nodes=n,o.edges=r,p(o,"constructor",this.constructor),o},r}(y.exports.EventEmitter);"undefined"!=typeof Symbol&&(zt.prototype[Symbol.for("nodejs.util.inspect.custom")]=zt.prototype.inspect),[{name:function(t){return"".concat(t,"Edge")},generateKey:!0},{name:function(t){return"".concat(t,"DirectedEdge")},generateKey:!0,type:"directed"},{name:function(t){return"".concat(t,"UndirectedEdge")},generateKey:!0,type:"undirected"},{name:function(t){return"".concat(t,"EdgeWithKey")}},{name:function(t){return"".concat(t,"DirectedEdgeWithKey")},type:"directed"},{name:function(t){return"".concat(t,"UndirectedEdgeWithKey")},type:"undirected"}].forEach((function(t){["add","merge","update"].forEach((function(e){var n=t.name(e),r="add"===e?jt:Ct;t.generateKey?zt.prototype[n]=function(i,o,a){return r(this,n,!0,"undirected"===(t.type||this.type),null,i,o,a,"update"===e)}:zt.prototype[n]=function(i,o,a,c){return r(this,n,!1,"undirected"===(t.type||this.type),i,o,a,c,"update"===e)}}))})),function(t){Q.forEach((function(e){var n=e.name,r=e.attacher;r(t,n("Node"),0),r(t,n("Source"),1),r(t,n("Target"),2),r(t,n("Opposite"),3)}))}(zt),function(t){X.forEach((function(e){var n=e.name,r=e.attacher;r(t,n("Edge"),"mixed"),r(t,n("DirectedEdge"),"directed"),r(t,n("UndirectedEdge"),"undirected")}))}(zt),function(t){et.forEach((function(e){!function(t,e){var n=e.name,r=e.type,i=e.direction;t.prototype[n]=function(t,e){if("mixed"!==r&&"mixed"!==this.type&&r!==this.type)return[];if(!arguments.length)return ut(this,r);if(1===arguments.length){t=""+t;var o=this._nodes.get(t);if(void 0===o)throw new F("Graph.".concat(n,': could not find the "').concat(t,'" node in the graph.'));return pt(this.multi,"mixed"===r?this.type:r,i,o)}if(2===arguments.length){t=""+t,e=""+e;var a=this._nodes.get(t);if(!a)throw new F("Graph.".concat(n,': could not find the "').concat(t,'" source node in the graph.'));if(!this._nodes.has(e))throw new F("Graph.".concat(n,': could not find the "').concat(e,'" target node in the graph.'));return gt(r,this.multi,i,a,e)}throw new B("Graph.".concat(n,": too many arguments (expecting 0, 1 or 2 and got ").concat(arguments.length,")."))}}(t,e),function(t,e){var n=e.name,r=e.type,i=e.direction,o="forEach"+n[0].toUpperCase()+n.slice(1,-1);t.prototype[o]=function(t,e,n){if("mixed"===r||"mixed"===this.type||r===this.type){if(1===arguments.length)return dt(!1,this,r,n=t);if(2===arguments.length){t=""+t,n=e;var a=this._nodes.get(t);if(void 0===a)throw new F("Graph.".concat(o,': could not find the "').concat(t,'" node in the graph.'));return ht(!1,this.multi,"mixed"===r?this.type:r,i,a,n)}if(3===arguments.length){t=""+t,e=""+e;var c=this._nodes.get(t);if(!c)throw new F("Graph.".concat(o,': could not find the "').concat(t,'" source node in the graph.'));if(!this._nodes.has(e))throw new F("Graph.".concat(o,': could not find the "').concat(e,'" target node in the graph.'));return lt(!1,r,this.multi,i,c,e,n)}throw new B("Graph.".concat(o,": too many arguments (expecting 1, 2 or 3 and got ").concat(arguments.length,")."))}};var a="map"+n[0].toUpperCase()+n.slice(1);t.prototype[a]=function(){var t,e=Array.prototype.slice.call(arguments),n=e.pop();if(0===e.length){var i=0;"directed"!==r&&(i+=this.undirectedSize),"undirected"!==r&&(i+=this.directedSize),t=new Array(i);var a=0;e.push((function(e,r,i,o,c,u,d){t[a++]=n(e,r,i,o,c,u,d)}))}else t=[],e.push((function(e,r,i,o,a,c,u){t.push(n(e,r,i,o,a,c,u))}));return this[o].apply(this,e),t};var c="filter"+n[0].toUpperCase()+n.slice(1);t.prototype[c]=function(){var t=Array.prototype.slice.call(arguments),e=t.pop(),n=[];return t.push((function(t,r,i,o,a,c,u){e(t,r,i,o,a,c,u)&&n.push(t)})),this[o].apply(this,t),n};var u="reduce"+n[0].toUpperCase()+n.slice(1);t.prototype[u]=function(){var t,e,n=Array.prototype.slice.call(arguments);if(n.length<2||n.length>4)throw new B("Graph.".concat(u,": invalid number of arguments (expecting 2, 3 or 4 and got ").concat(n.length,")."));if("function"==typeof n[n.length-1]&&"function"!=typeof n[n.length-2])throw new B("Graph.".concat(u,": missing initial value. You must provide it because the callback takes more than one argument and we cannot infer the initial value from the first iteration, as you could with a simple array."));2===n.length?(t=n[0],e=n[1],n=[]):3===n.length?(t=n[1],e=n[2],n=[n[0]]):4===n.length&&(t=n[2],e=n[3],n=[n[0],n[1]]);var r=e;return n.push((function(e,n,i,o,a,c,u){r=t(r,e,n,i,o,a,c,u)})),this[o].apply(this,n),r}}(t,e),function(t,e){var n=e.name,r=e.type,i=e.direction,o="find"+n[0].toUpperCase()+n.slice(1,-1);t.prototype[o]=function(t,e,n){if("mixed"!==r&&"mixed"!==this.type&&r!==this.type)return!1;if(1===arguments.length)return dt(!0,this,r,n=t);if(2===arguments.length){t=""+t,n=e;var a=this._nodes.get(t);if(void 0===a)throw new F("Graph.".concat(o,': could not find the "').concat(t,'" node in the graph.'));return ht(!0,this.multi,"mixed"===r?this.type:r,i,a,n)}if(3===arguments.length){t=""+t,e=""+e;var c=this._nodes.get(t);if(!c)throw new F("Graph.".concat(o,': could not find the "').concat(t,'" source node in the graph.'));if(!this._nodes.has(e))throw new F("Graph.".concat(o,': could not find the "').concat(e,'" target node in the graph.'));return lt(!0,r,this.multi,i,c,e,n)}throw new B("Graph.".concat(o,": too many arguments (expecting 1, 2 or 3 and got ").concat(arguments.length,")."))};var a="some"+n[0].toUpperCase()+n.slice(1,-1);t.prototype[a]=function(){var t=Array.prototype.slice.call(arguments),e=t.pop();return t.push((function(t,n,r,i,o,a,c){return e(t,n,r,i,o,a,c)})),!!this[o].apply(this,t)};var c="every"+n[0].toUpperCase()+n.slice(1,-1);t.prototype[c]=function(){var t=Array.prototype.slice.call(arguments),e=t.pop();return t.push((function(t,n,r,i,o,a,c){return!e(t,n,r,i,o,a,c)})),!this[o].apply(this,t)}}(t,e),function(t,e){var n=e.name,r=e.type,i=e.direction,o=n.slice(0,-1)+"Entries";t.prototype[o]=function(t,e){if("mixed"!==r&&"mixed"!==this.type&&r!==this.type)return O.empty();if(!arguments.length)return st(this,r);if(1===arguments.length){t=""+t;var n=this._nodes.get(t);if(!n)throw new F("Graph.".concat(o,': could not find the "').concat(t,'" node in the graph.'));return ft(r,i,n)}if(2===arguments.length){t=""+t,e=""+e;var a=this._nodes.get(t);if(!a)throw new F("Graph.".concat(o,': could not find the "').concat(t,'" source node in the graph.'));if(!this._nodes.has(e))throw new F("Graph.".concat(o,': could not find the "').concat(e,'" target node in the graph.'));return yt(r,i,a,e)}throw new B("Graph.".concat(o,": too many arguments (expecting 0, 1 or 2 and got ").concat(arguments.length,")."))}}(t,e)}))}(zt),function(t){wt.forEach((function(e){_t(t,e),function(t,e){var n=e.name,r=e.type,i=e.direction,o="forEach"+n[0].toUpperCase()+n.slice(1,-1);t.prototype[o]=function(t,e){if("mixed"===r||"mixed"===this.type||r===this.type){t=""+t;var n=this._nodes.get(t);if(void 0===n)throw new F("Graph.".concat(o,': could not find the "').concat(t,'" node in the graph.'));mt(!1,"mixed"===r?this.type:r,i,n,e)}};var a="map"+n[0].toUpperCase()+n.slice(1);t.prototype[a]=function(t,e){var n=[];return this[o](t,(function(t,r){n.push(e(t,r))})),n};var c="filter"+n[0].toUpperCase()+n.slice(1);t.prototype[c]=function(t,e){var n=[];return this[o](t,(function(t,r){e(t,r)&&n.push(t)})),n};var u="reduce"+n[0].toUpperCase()+n.slice(1);t.prototype[u]=function(t,e,n){if(arguments.length<3)throw new B("Graph.".concat(u,": missing initial value. You must provide it because the callback takes more than one argument and we cannot infer the initial value from the first iteration, as you could with a simple array."));var r=n;return this[o](t,(function(t,n){r=e(r,t,n)})),r}}(t,e),function(t,e){var n=e.name,r=e.type,i=e.direction,o=n[0].toUpperCase()+n.slice(1,-1),a="find"+o;t.prototype[a]=function(t,e){if("mixed"===r||"mixed"===this.type||r===this.type){t=""+t;var n=this._nodes.get(t);if(void 0===n)throw new F("Graph.".concat(a,': could not find the "').concat(t,'" node in the graph.'));return mt(!0,"mixed"===r?this.type:r,i,n,e)}};var c="some"+o;t.prototype[c]=function(t,e){return!!this[a](t,e)};var u="every"+o;t.prototype[u]=function(t,e){return!this[a](t,(function(t,n){return!e(t,n)}))}}(t,e),Gt(t,e)}))}(zt);var Wt=function(t){function n(e){var n=u({type:"directed"},e);if("multi"in n&&!1!==n.multi)throw new B("DirectedGraph.from: inconsistent indication that the graph should be multi in given options!");if("directed"!==n.type)throw new B('DirectedGraph.from: inconsistent "'+n.type+'" type in given options!');return t.call(this,n)||this}return e(n,t),n}(zt),Pt=function(t){function n(e){var n=u({type:"undirected"},e);if("multi"in n&&!1!==n.multi)throw new B("UndirectedGraph.from: inconsistent indication that the graph should be multi in given options!");if("undirected"!==n.type)throw new B('UndirectedGraph.from: inconsistent "'+n.type+'" type in given options!');return t.call(this,n)||this}return e(n,t),n}(zt),Rt=function(t){function n(e){var n=u({multi:!0},e);if("multi"in n&&!0!==n.multi)throw new B("MultiGraph.from: inconsistent indication that the graph should be simple in given options!");return t.call(this,n)||this}return e(n,t),n}(zt),Kt=function(t){function n(e){var n=u({type:"directed",multi:!0},e);if("multi"in n&&!0!==n.multi)throw new B("MultiDirectedGraph.from: inconsistent indication that the graph should be simple in given options!");if("directed"!==n.type)throw new B('MultiDirectedGraph.from: inconsistent "'+n.type+'" type in given options!');return t.call(this,n)||this}return e(n,t),n}(zt),Tt=function(t){function n(e){var n=u({type:"undirected",multi:!0},e);if("multi"in n&&!0!==n.multi)throw new B("MultiUndirectedGraph.from: inconsistent indication that the graph should be simple in given options!");if("undirected"!==n.type)throw new B('MultiUndirectedGraph.from: inconsistent "'+n.type+'" type in given options!');return t.call(this,n)||this}return e(n,t),n}(zt);function Bt(t){t.from=function(e,n){var r=u({},e.options,n),i=new t(r);return i.import(e),i}}return Bt(zt),Bt(Wt),Bt(Pt),Bt(Rt),Bt(Kt),Bt(Tt),zt.Graph=zt,zt.DirectedGraph=Wt,zt.UndirectedGraph=Pt,zt.MultiGraph=Rt,zt.MultiDirectedGraph=Kt,zt.MultiUndirectedGraph=Tt,zt.InvalidArgumentsGraphError=B,zt.NotFoundGraphError=F,zt.UsageGraphError=I,zt})); +//# sourceMappingURL=graphology.umd.min.js.map diff --git a/playground/static/sigma.min.js b/playground/static/sigma.min.js new file mode 100644 index 0000000..8995fd7 --- /dev/null +++ b/playground/static/sigma.min.js @@ -0,0 +1,1351 @@ +(function(Ce,de){typeof exports=="object"&&typeof module<"u"?module.exports=de():typeof define=="function"&&define.amd?define(de):(Ce=typeof globalThis<"u"?globalThis:Ce||self,Ce.Sigma=de())})(this,function(){"use strict";function Ce(r,e){if(typeof r!="object"||!r)return r;var t=r[Symbol.toPrimitive];if(t!==void 0){var i=t.call(r,e||"default");if(typeof i!="object")return i;throw new TypeError("@@toPrimitive must return a primitive value.")}return(e==="string"?String:Number)(r)}function de(r){var e=Ce(r,"string");return typeof e=="symbol"?e:e+""}function ne(r,e){if(!(r instanceof e))throw new TypeError("Cannot call a class as a function")}function Rn(r,e){for(var t=0;t>8&255,o=t>>16&255,a=t>>24&255;return[i,n,o,a]}var at={};function pr(r){if(typeof at[r]<"u")return at[r];var e=(r&16711680)>>>16,t=(r&65280)>>>8,i=r&255,n=255,o=vr(e,t,i,n);return at[r]=o,o}function O(r,e,t){return(e=de(e))in r?Object.defineProperty(r,e,{value:t,enumerable:!0,configurable:!0,writable:!0}):r[e]=t,r}function _r(r,e){var t=Object.keys(r);if(Object.getOwnPropertySymbols){var i=Object.getOwnPropertySymbols(r);e&&(i=i.filter(function(n){return Object.getOwnPropertyDescriptor(r,n).enumerable})),t.push.apply(t,i)}return t}function me(r){for(var e=1;e border) + gl_FragColor = transparent; + else + gl_FragColor = v_color; + + #else + float t = 0.0; + if (dist > border) + t = 1.0; + else if (dist > 0.0) + t = dist / border; + + gl_FragColor = mix(v_color, transparent, t); + #endif +} +`,In=kn,zn=` +attribute vec4 a_id; +attribute vec4 a_color; +attribute vec2 a_position; +attribute float a_size; +attribute float a_angle; + +uniform mat3 u_matrix; +uniform float u_sizeRatio; +uniform float u_correctionRatio; + +varying vec4 v_color; +varying vec2 v_diffVector; +varying float v_radius; +varying float v_border; + +const float bias = 255.0 / 254.0; + +void main() { + float size = a_size * u_correctionRatio / u_sizeRatio * 4.0; + vec2 diffVector = size * vec2(cos(a_angle), sin(a_angle)); + vec2 position = a_position + diffVector; + gl_Position = vec4( + (u_matrix * vec3(position, 1)).xy, + 0, + 1 + ); + + v_diffVector = diffVector; + v_radius = size / 2.0; + + #ifdef PICKING_MODE + // For picking mode, we use the ID as the color: + v_color = a_id; + #else + // For normal mode, we use the color: + v_color = a_color; + #endif + + v_color.a *= bias; +} +`,Gn=zn,Cr=WebGLRenderingContext,wr=Cr.UNSIGNED_BYTE,ht=Cr.FLOAT,Mn=["u_sizeRatio","u_correctionRatio","u_matrix"],dt=function(r){Ae(e,r);function e(){return ne(this,e),we(this,e,arguments)}return oe(e,[{key:"getDefinition",value:function(){return{VERTICES:3,VERTEX_SHADER_SOURCE:Gn,FRAGMENT_SHADER_SOURCE:In,METHOD:WebGLRenderingContext.TRIANGLES,UNIFORMS:Mn,ATTRIBUTES:[{name:"a_position",size:2,type:ht},{name:"a_size",size:1,type:ht},{name:"a_color",size:4,type:wr,normalized:!0},{name:"a_id",size:4,type:wr,normalized:!0}],CONSTANT_ATTRIBUTES:[{name:"a_angle",size:1,type:ht}],CONSTANT_DATA:[[e.ANGLE_1],[e.ANGLE_2],[e.ANGLE_3]]}}},{key:"processVisibleItem",value:function(i,n,o){var a=this.array,s=K(o.color);a[n++]=o.x,a[n++]=o.y,a[n++]=o.size,a[n++]=s,a[n++]=i}},{key:"setUniforms",value:function(i,n){var o=n.gl,a=n.uniformLocations,s=a.u_sizeRatio,c=a.u_correctionRatio,l=a.u_matrix;o.uniform1f(c,i.correctionRatio),o.uniform1f(s,i.sizeRatio),o.uniformMatrix3fv(l,!1,i.matrix)}}]),e}(Ge);O(dt,"ANGLE_1",0),O(dt,"ANGLE_2",2*Math.PI/3),O(dt,"ANGLE_3",4*Math.PI/3);var Un=` +precision mediump float; + +varying vec4 v_color; + +void main(void) { + gl_FragColor = v_color; +} +`,Bn=Un,Hn=` +attribute vec2 a_position; +attribute vec2 a_normal; +attribute float a_radius; +attribute vec3 a_barycentric; + +#ifdef PICKING_MODE +attribute vec4 a_id; +#else +attribute vec4 a_color; +#endif + +uniform mat3 u_matrix; +uniform float u_sizeRatio; +uniform float u_correctionRatio; +uniform float u_minEdgeThickness; +uniform float u_lengthToThicknessRatio; +uniform float u_widenessToThicknessRatio; + +varying vec4 v_color; + +const float bias = 255.0 / 254.0; + +void main() { + float minThickness = u_minEdgeThickness; + + float normalLength = length(a_normal); + vec2 unitNormal = a_normal / normalLength; + + // These first computations are taken from edge.vert.glsl and + // edge.clamped.vert.glsl. Please read it to get better comments on what's + // happening: + float pixelsThickness = max(normalLength / u_sizeRatio, minThickness); + float webGLThickness = pixelsThickness * u_correctionRatio; + float webGLNodeRadius = a_radius * 2.0 * u_correctionRatio / u_sizeRatio; + float webGLArrowHeadLength = webGLThickness * u_lengthToThicknessRatio * 2.0; + float webGLArrowHeadThickness = webGLThickness * u_widenessToThicknessRatio; + + float da = a_barycentric.x; + float db = a_barycentric.y; + float dc = a_barycentric.z; + + vec2 delta = vec2( + da * (webGLNodeRadius * unitNormal.y) + + db * ((webGLNodeRadius + webGLArrowHeadLength) * unitNormal.y + webGLArrowHeadThickness * unitNormal.x) + + dc * ((webGLNodeRadius + webGLArrowHeadLength) * unitNormal.y - webGLArrowHeadThickness * unitNormal.x), + + da * (-webGLNodeRadius * unitNormal.x) + + db * (-(webGLNodeRadius + webGLArrowHeadLength) * unitNormal.x + webGLArrowHeadThickness * unitNormal.y) + + dc * (-(webGLNodeRadius + webGLArrowHeadLength) * unitNormal.x - webGLArrowHeadThickness * unitNormal.y) + ); + + vec2 position = (u_matrix * vec3(a_position + delta, 1)).xy; + + gl_Position = vec4(position, 0, 1); + + #ifdef PICKING_MODE + // For picking mode, we use the ID as the color: + v_color = a_id; + #else + // For normal mode, we use the color: + v_color = a_color; + #endif + + v_color.a *= bias; +} +`,$n=Hn,Ar=WebGLRenderingContext,Sr=Ar.UNSIGNED_BYTE,Me=Ar.FLOAT,jn=["u_matrix","u_sizeRatio","u_correctionRatio","u_minEdgeThickness","u_lengthToThicknessRatio","u_widenessToThicknessRatio"],ft={extremity:"target",lengthToThicknessRatio:2.5,widenessToThicknessRatio:2};function xr(r){var e=me(me({},ft),{});return function(t){Ae(i,t);function i(){return ne(this,i),we(this,i,arguments)}return oe(i,[{key:"getDefinition",value:function(){return{VERTICES:3,VERTEX_SHADER_SOURCE:$n,FRAGMENT_SHADER_SOURCE:Bn,METHOD:WebGLRenderingContext.TRIANGLES,UNIFORMS:jn,ATTRIBUTES:[{name:"a_position",size:2,type:Me},{name:"a_normal",size:2,type:Me},{name:"a_radius",size:1,type:Me},{name:"a_color",size:4,type:Sr,normalized:!0},{name:"a_id",size:4,type:Sr,normalized:!0}],CONSTANT_ATTRIBUTES:[{name:"a_barycentric",size:3,type:Me}],CONSTANT_DATA:[[1,0,0],[0,1,0],[0,0,1]]}}},{key:"processVisibleItem",value:function(o,a,s,c,l){if(e.extremity==="source"){var u=[c,s];s=u[0],c=u[1]}var h=l.size||1,d=c.size||1,m=s.x,f=s.y,p=c.x,b=c.y,g=K(l.color),_=p-m,v=b-f,y=_*_+v*v,C=0,S=0;y&&(y=1/Math.sqrt(y),C=-v*y*h,S=_*y*h);var w=this.array;w[a++]=p,w[a++]=b,w[a++]=-C,w[a++]=-S,w[a++]=d,w[a++]=g,w[a++]=o}},{key:"setUniforms",value:function(o,a){var s=a.gl,c=a.uniformLocations,l=c.u_matrix,u=c.u_sizeRatio,h=c.u_correctionRatio,d=c.u_minEdgeThickness,m=c.u_lengthToThicknessRatio,f=c.u_widenessToThicknessRatio;s.uniformMatrix3fv(l,!1,o.matrix),s.uniform1f(u,o.sizeRatio),s.uniform1f(h,o.correctionRatio),s.uniform1f(d,o.minEdgeThickness),s.uniform1f(m,e.lengthToThicknessRatio),s.uniform1f(f,e.widenessToThicknessRatio)}}]),i}(ut)}xr();var Vn=` +precision mediump float; + +varying vec4 v_color; +varying vec2 v_normal; +varying float v_thickness; +varying float v_feather; + +const vec4 transparent = vec4(0.0, 0.0, 0.0, 0.0); + +void main(void) { + // We only handle antialiasing for normal mode: + #ifdef PICKING_MODE + gl_FragColor = v_color; + #else + float dist = length(v_normal) * v_thickness; + + float t = smoothstep( + v_thickness - v_feather, + v_thickness, + dist + ); + + gl_FragColor = mix(v_color, transparent, t); + #endif +} +`,Wn=Vn,Yn=` +attribute vec4 a_id; +attribute vec4 a_color; +attribute vec2 a_normal; +attribute float a_normalCoef; +attribute vec2 a_positionStart; +attribute vec2 a_positionEnd; +attribute float a_positionCoef; +attribute float a_radius; +attribute float a_radiusCoef; + +uniform mat3 u_matrix; +uniform float u_zoomRatio; +uniform float u_sizeRatio; +uniform float u_pixelRatio; +uniform float u_correctionRatio; +uniform float u_minEdgeThickness; +uniform float u_lengthToThicknessRatio; +uniform float u_feather; + +varying vec4 v_color; +varying vec2 v_normal; +varying float v_thickness; +varying float v_feather; + +const float bias = 255.0 / 254.0; + +void main() { + float minThickness = u_minEdgeThickness; + + float radius = a_radius * a_radiusCoef; + vec2 normal = a_normal * a_normalCoef; + vec2 position = a_positionStart * (1.0 - a_positionCoef) + a_positionEnd * a_positionCoef; + + float normalLength = length(normal); + vec2 unitNormal = normal / normalLength; + + // These first computations are taken from edge.vert.glsl. Please read it to + // get better comments on what's happening: + float pixelsThickness = max(normalLength, minThickness * u_sizeRatio); + float webGLThickness = pixelsThickness * u_correctionRatio / u_sizeRatio; + + // Here, we move the point to leave space for the arrow head: + float direction = sign(radius); + float webGLNodeRadius = direction * radius * 2.0 * u_correctionRatio / u_sizeRatio; + float webGLArrowHeadLength = webGLThickness * u_lengthToThicknessRatio * 2.0; + + vec2 compensationVector = vec2(-direction * unitNormal.y, direction * unitNormal.x) * (webGLNodeRadius + webGLArrowHeadLength); + + // Here is the proper position of the vertex + gl_Position = vec4((u_matrix * vec3(position + unitNormal * webGLThickness + compensationVector, 1)).xy, 0, 1); + + v_thickness = webGLThickness / u_zoomRatio; + + v_normal = unitNormal; + + v_feather = u_feather * u_correctionRatio / u_zoomRatio / u_pixelRatio * 2.0; + + #ifdef PICKING_MODE + // For picking mode, we use the ID as the color: + v_color = a_id; + #else + // For normal mode, we use the color: + v_color = a_color; + #endif + + v_color.a *= bias; +} +`,Xn=Yn,Fr=WebGLRenderingContext,Lr=Fr.UNSIGNED_BYTE,ae=Fr.FLOAT,qn=["u_matrix","u_zoomRatio","u_sizeRatio","u_correctionRatio","u_pixelRatio","u_feather","u_minEdgeThickness","u_lengthToThicknessRatio"],Kn={lengthToThicknessRatio:ft.lengthToThicknessRatio};function Nr(r){var e=me(me({},Kn),{});return function(t){Ae(i,t);function i(){return ne(this,i),we(this,i,arguments)}return oe(i,[{key:"getDefinition",value:function(){return{VERTICES:6,VERTEX_SHADER_SOURCE:Xn,FRAGMENT_SHADER_SOURCE:Wn,METHOD:WebGLRenderingContext.TRIANGLES,UNIFORMS:qn,ATTRIBUTES:[{name:"a_positionStart",size:2,type:ae},{name:"a_positionEnd",size:2,type:ae},{name:"a_normal",size:2,type:ae},{name:"a_color",size:4,type:Lr,normalized:!0},{name:"a_id",size:4,type:Lr,normalized:!0},{name:"a_radius",size:1,type:ae}],CONSTANT_ATTRIBUTES:[{name:"a_positionCoef",size:1,type:ae},{name:"a_normalCoef",size:1,type:ae},{name:"a_radiusCoef",size:1,type:ae}],CONSTANT_DATA:[[0,1,0],[0,-1,0],[1,1,1],[1,1,1],[0,-1,0],[1,-1,-1]]}}},{key:"processVisibleItem",value:function(o,a,s,c,l){var u=l.size||1,h=s.x,d=s.y,m=c.x,f=c.y,p=K(l.color),b=m-h,g=f-d,_=c.size||1,v=b*b+g*g,y=0,C=0;v&&(v=1/Math.sqrt(v),y=-g*v*u,C=b*v*u);var S=this.array;S[a++]=h,S[a++]=d,S[a++]=m,S[a++]=f,S[a++]=y,S[a++]=C,S[a++]=p,S[a++]=o,S[a++]=_}},{key:"setUniforms",value:function(o,a){var s=a.gl,c=a.uniformLocations,l=c.u_matrix,u=c.u_zoomRatio,h=c.u_feather,d=c.u_pixelRatio,m=c.u_correctionRatio,f=c.u_sizeRatio,p=c.u_minEdgeThickness,b=c.u_lengthToThicknessRatio;s.uniformMatrix3fv(l,!1,o.matrix),s.uniform1f(u,o.zoomRatio),s.uniform1f(f,o.sizeRatio),s.uniform1f(m,o.correctionRatio),s.uniform1f(d,o.pixelRatio),s.uniform1f(h,o.antiAliasingFeather),s.uniform1f(p,o.minEdgeThickness),s.uniform1f(b,e.lengthToThicknessRatio)}}]),i}(ut)}Nr();function Zn(r){return On([Nr(),xr()])}Zn();function Qn(r){return r&&r.__esModule&&Object.prototype.hasOwnProperty.call(r,"default")?r.default:r}var Jn=function(e){return e!==null&&typeof e=="object"&&typeof e.addUndirectedEdgeWithKey=="function"&&typeof e.dropNode=="function"&&typeof e.multi=="boolean"};const eo=Qn(Jn);function to(r,e){if(!(r instanceof e))throw new TypeError("Cannot call a class as a function")}function ro(r,e){if(typeof r!="object"||!r)return r;var t=r[Symbol.toPrimitive];if(t!==void 0){var i=t.call(r,e||"default");if(typeof i!="object")return i;throw new TypeError("@@toPrimitive must return a primitive value.")}return(e==="string"?String:Number)(r)}function Pr(r){var e=ro(r,"string");return typeof e=="symbol"?e:e+""}function io(r,e){for(var t=0;tr.length)&&(e=r.length);for(var t=0,i=Array(e);tG){var M="…";for(p=p+M,N=o.measureText(p).width;N>G&&p.length>1;)p=p.slice(0,-2)+M,N=o.measureText(p).width;if(p.length<4)return}for(var q={},te=0,re=p.length;ter.length)&&(e=r.length);for(var t=0,i=Array(e);t v_radius) + gl_FragColor = transparent; + else { + gl_FragColor = v_color; + gl_FragColor.a *= bias; + } + #else + // Sizes: +`).concat(e.flatMap(function(n,o){var a=n.size;if("fill"in a)return[];a=a;var s="attribute"in a?"v_borderSize_".concat(o+1):lt(a.value),c=(a.mode||Do)==="pixels"?"u_correctionRatio":"v_radius";return[" float borderSize_".concat(o+1," = ").concat(c," * ").concat(s,";")]}).join(` +`),` + // Now, let's split the remaining space between "fill" borders: + float fillBorderSize = (v_radius - (`).concat(e.flatMap(function(n,o){var a=n.size;return"fill"in a?[]:["borderSize_".concat(o+1)]}).join(" + "),") ) / ").concat(t,`; +`).concat(e.flatMap(function(n,o){var a=n.size;return"fill"in a?[" float borderSize_".concat(o+1," = fillBorderSize;")]:[]}).join(` +`),` + + // Finally, normalize all border sizes, to start from the full size and to end with the smallest: + float adjustedBorderSize_0 = v_radius; +`).concat(e.map(function(n,o){return" float adjustedBorderSize_".concat(o+1," = adjustedBorderSize_").concat(o," - borderSize_").concat(o+1,";")}).join(` +`),` + + // Colors: + vec4 borderColor_0 = transparent; +`).concat(e.map(function(n,o){var a=n.color,s=[];return"attribute"in a?s.push(" vec4 borderColor_".concat(o+1," = v_borderColor_").concat(o+1,";")):"transparent"in a?s.push(" vec4 borderColor_".concat(o+1," = vec4(0.0, 0.0, 0.0, 0.0);")):s.push(" vec4 borderColor_".concat(o+1," = u_borderColor_").concat(o+1,";")),s.push(" borderColor_".concat(o+1,".a *= bias;")),s.push(" if (borderSize_".concat(o+1," <= 1.0 * u_correctionRatio) { borderColor_").concat(o+1," = borderColor_").concat(o,"; }")),s.join(` +`)}).join(` +`),` + if (dist > adjustedBorderSize_0) { + gl_FragColor = borderColor_0; + } else `).concat(e.map(function(n,o){return"if (dist > adjustedBorderSize_".concat(o,` - aaBorder) { + gl_FragColor = mix(borderColor_`).concat(o+1,", borderColor_").concat(o,", (dist - adjustedBorderSize_").concat(o,` + aaBorder) / aaBorder); + } else if (dist > adjustedBorderSize_`).concat(o+1,`) { + gl_FragColor = borderColor_`).concat(o+1,`; + } else `)}).join(""),` { /* Nothing to add here */ } + #endif +} +`);return i}function zo(r){var e=r.borders,t=` +attribute vec2 a_position; +attribute float a_size; +attribute float a_angle; + +uniform mat3 u_matrix; +uniform float u_sizeRatio; +uniform float u_correctionRatio; + +varying vec2 v_diffVector; +varying float v_radius; + +#ifdef PICKING_MODE +attribute vec4 a_id; +varying vec4 v_color; +#else +`.concat(e.flatMap(function(i,n){var o=i.size;return"attribute"in o?["attribute float a_borderSize_".concat(n+1,";"),"varying float v_borderSize_".concat(n+1,";")]:[]}).join(` +`),` +`).concat(e.flatMap(function(i,n){var o=i.color;return"attribute"in o?["attribute vec4 a_borderColor_".concat(n+1,";"),"varying vec4 v_borderColor_".concat(n+1,";")]:[]}).join(` +`),` +#endif + +const float bias = 255.0 / 254.0; +const vec4 transparent = vec4(0.0, 0.0, 0.0, 0.0); + +void main() { + float size = a_size * u_correctionRatio / u_sizeRatio * 4.0; + vec2 diffVector = size * vec2(cos(a_angle), sin(a_angle)); + vec2 position = a_position + diffVector; + gl_Position = vec4( + (u_matrix * vec3(position, 1)).xy, + 0, + 1 + ); + + v_radius = size / 2.0; + v_diffVector = diffVector; + + #ifdef PICKING_MODE + v_color = a_id; + #else +`).concat(e.flatMap(function(i,n){var o=i.size;return"attribute"in o?[" v_borderSize_".concat(n+1," = a_borderSize_").concat(n+1,";")]:[]}).join(` +`),` +`).concat(e.flatMap(function(i,n){var o=i.color;return"attribute"in o?[" v_borderColor_".concat(n+1," = a_borderColor_").concat(n+1,";")]:[]}).join(` +`),` + #endif +} +`);return t}var qr=WebGLRenderingContext,Kr=qr.UNSIGNED_BYTE,He=qr.FLOAT;function Zr(r){var e,t=Xr(Xr({},Oo),r||{}),i=t.borders,n=t.drawLabel,o=t.drawHover,a=["u_sizeRatio","u_correctionRatio","u_matrix"].concat(bt(i.flatMap(function(s,c){var l=s.color;return"value"in l?["u_borderColor_".concat(c+1)]:[]})));return e=function(s){Fo(c,s);function c(){var l;Ro(this,c);for(var u=arguments.length,h=new Array(u),d=0;d0&&(a=t[0]),a instanceof Error)throw a;var s=new Error("Unhandled error."+(a?" ("+a.message+")":""));throw s.context=a,s}var c=o[e];if(c===void 0)return!1;if(typeof c=="function")Qr(c,this,t);else for(var l=c.length,u=ai(c,l),i=0;i0&&a.length>n&&!a.warned){a.warned=!0;var s=new Error("Possible EventEmitter memory leak detected. "+a.length+" "+String(e)+" listeners added. Use emitter.setMaxListeners() to increase limit");s.name="MaxListenersExceededWarning",s.emitter=r,s.type=e,s.count=a.length,Go(s)}return r}L.prototype.addListener=function(e,t){return ri(this,e,t,!1)},L.prototype.on=L.prototype.addListener,L.prototype.prependListener=function(e,t){return ri(this,e,t,!0)};function Mo(){if(!this.fired)return this.target.removeListener(this.type,this.wrapFn),this.fired=!0,arguments.length===0?this.listener.call(this.target):this.listener.apply(this.target,arguments)}function ii(r,e,t){var i={fired:!1,wrapFn:void 0,target:r,type:e,listener:t},n=Mo.bind(i);return n.listener=t,i.wrapFn=n,n}L.prototype.once=function(e,t){return je(t),this.on(e,ii(this,e,t)),this},L.prototype.prependOnceListener=function(e,t){return je(t),this.prependListener(e,ii(this,e,t)),this},L.prototype.removeListener=function(e,t){var i,n,o,a,s;if(je(t),n=this._events,n===void 0)return this;if(i=n[e],i===void 0)return this;if(i===t||i.listener===t)--this._eventsCount===0?this._events=Object.create(null):(delete n[e],n.removeListener&&this.emit("removeListener",e,i.listener||t));else if(typeof i!="function"){for(o=-1,a=i.length-1;a>=0;a--)if(i[a]===t||i[a].listener===t){s=i[a].listener,o=a;break}if(o<0)return this;o===0?i.shift():Uo(i,o),i.length===1&&(n[e]=i[0]),n.removeListener!==void 0&&this.emit("removeListener",e,s||t)}return this},L.prototype.off=L.prototype.removeListener,L.prototype.removeAllListeners=function(e){var t,i,n;if(i=this._events,i===void 0)return this;if(i.removeListener===void 0)return arguments.length===0?(this._events=Object.create(null),this._eventsCount=0):i[e]!==void 0&&(--this._eventsCount===0?this._events=Object.create(null):delete i[e]),this;if(arguments.length===0){var o=Object.keys(i),a;for(n=0;n=0;n--)this.removeListener(e,t[n]);return this};function ni(r,e,t){var i=r._events;if(i===void 0)return[];var n=i[e];return n===void 0?[]:typeof n=="function"?t?[n.listener||n]:[n]:t?Bo(n):ai(n,n.length)}L.prototype.listeners=function(e){return ni(this,e,!0)},L.prototype.rawListeners=function(e){return ni(this,e,!1)},L.listenerCount=function(r,e){return typeof r.listenerCount=="function"?r.listenerCount(e):oi.call(r,e)},L.prototype.listenerCount=oi;function oi(r){var e=this._events;if(e!==void 0){var t=e[r];if(typeof t=="function")return 1;if(t!==void 0)return t.length}return 0}L.prototype.eventNames=function(){return this._eventsCount>0?$e(this._events):[]};function ai(r,e){for(var t=new Array(e),i=0;ir.length)&&(e=r.length);for(var t=0,i=Array(e);t v_radius / paddingRatio || abs(diffVector.y) > v_radius / paddingRatio) { + color = u_colorizeImages ? gl_FragColor : v_color; + } + } + } + #endif + + // Crop in a circle when u_keepWithinCircle is truthy: + if (u_keepWithinCircle) { + if (dist < v_radius - border) { + gl_FragColor = color; + } else if (dist < v_radius) { + gl_FragColor = mix(transparent, color, (v_radius - dist) / border); + } + } + + // Crop in a square else: + else { + float squareHalfSize = v_radius * `).concat(Math.SQRT1_2*Math.cos(Math.PI/12),`; + if (abs(diffVector.x) > squareHalfSize || abs(diffVector.y) > squareHalfSize) { + gl_FragColor = transparent; + } else { + gl_FragColor = color; + } + } +} +`);return t}var ta=` +attribute vec4 a_id; +attribute vec4 a_color; +attribute vec2 a_position; +attribute float a_size; +attribute float a_angle; +attribute vec4 a_texture; +attribute float a_textureIndex; + +uniform mat3 u_matrix; +uniform float u_sizeRatio; +uniform float u_correctionRatio; + +varying vec4 v_color; +varying vec2 v_diffVector; +varying float v_radius; +varying vec4 v_texture; +varying float v_textureIndex; + +const float bias = 255.0 / 254.0; +const float marginRatio = 1.05; + +void main() { + float size = a_size * u_correctionRatio / u_sizeRatio * 4.0; + vec2 diffVector = size * vec2(cos(a_angle), sin(a_angle)); + vec2 position = a_position + diffVector * marginRatio; + gl_Position = vec4( + (u_matrix * vec3(position, 1)).xy, + 0, + 1 + ); + + v_diffVector = diffVector; + v_radius = size / 2.0 / marginRatio; + + #ifdef PICKING_MODE + // For picking mode, we use the ID as the color: + v_color = a_id; + #else + // For normal mode, we use the color: + v_color = a_color; + + // Pass the texture coordinates: + v_textureIndex = a_textureIndex; + v_texture = a_texture; + #endif + + v_color.a *= bias; +} +`,ra=ta;function ce(){ce=function(){return e};var r,e={},t=Object.prototype,i=t.hasOwnProperty,n=Object.defineProperty||function(T,E,R){T[E]=R.value},o=typeof Symbol=="function"?Symbol:{},a=o.iterator||"@@iterator",s=o.asyncIterator||"@@asyncIterator",c=o.toStringTag||"@@toStringTag";function l(T,E,R){return Object.defineProperty(T,E,{value:R,enumerable:!0,configurable:!0,writable:!0}),T[E]}try{l({},"")}catch{l=function(E,R,x){return E[R]=x}}function u(T,E,R,x){var A=E&&E.prototype instanceof g?E:g,F=Object.create(A.prototype),N=new H(x||[]);return n(F,"_invoke",{value:D(T,R,N)}),F}function h(T,E,R){try{return{type:"normal",arg:T.call(E,R)}}catch(x){return{type:"throw",arg:x}}}e.wrap=u;var d="suspendedStart",m="suspendedYield",f="executing",p="completed",b={};function g(){}function _(){}function v(){}var y={};l(y,a,function(){return this});var C=Object.getPrototypeOf,S=C&&C(C($([])));S&&S!==t&&i.call(S,a)&&(y=S);var w=v.prototype=g.prototype=Object.create(y);function P(T){["next","throw","return"].forEach(function(E){l(T,E,function(R){return this._invoke(E,R)})})}function k(T,E){function R(A,F,N,G){var M=h(T[A],T,F);if(M.type!=="throw"){var q=M.arg,te=q.value;return te&&typeof te=="object"&&i.call(te,"__await")?E.resolve(te.__await).then(function(re){R("next",re,N,G)},function(re){R("throw",re,N,G)}):E.resolve(te).then(function(re){q.value=re,N(q)},function(re){return R("throw",re,N,G)})}G(M.arg)}var x;n(this,"_invoke",{value:function(A,F){function N(){return new E(function(G,M){R(A,F,G,M)})}return x=x?x.then(N,N):N()}})}function D(T,E,R){var x=d;return function(A,F){if(x===f)throw Error("Generator is already running");if(x===p){if(A==="throw")throw F;return{value:r,done:!0}}for(R.method=A,R.arg=F;;){var N=R.delegate;if(N){var G=U(N,R);if(G){if(G===b)continue;return G}}if(R.method==="next")R.sent=R._sent=R.arg;else if(R.method==="throw"){if(x===d)throw x=p,R.arg;R.dispatchException(R.arg)}else R.method==="return"&&R.abrupt("return",R.arg);x=f;var M=h(T,E,R);if(M.type==="normal"){if(x=R.done?p:m,M.arg===b)continue;return{value:M.arg,done:R.done}}M.type==="throw"&&(x=p,R.method="throw",R.arg=M.arg)}}}function U(T,E){var R=E.method,x=T.iterator[R];if(x===r)return E.delegate=null,R==="throw"&&T.iterator.return&&(E.method="return",E.arg=r,U(T,E),E.method==="throw")||R!=="return"&&(E.method="throw",E.arg=new TypeError("The iterator does not provide a '"+R+"' method")),b;var A=h(x,T.iterator,E.arg);if(A.type==="throw")return E.method="throw",E.arg=A.arg,E.delegate=null,b;var F=A.arg;return F?F.done?(E[T.resultName]=F.value,E.next=T.nextLoc,E.method!=="return"&&(E.method="next",E.arg=r),E.delegate=null,b):F:(E.method="throw",E.arg=new TypeError("iterator result is not an object"),E.delegate=null,b)}function z(T){var E={tryLoc:T[0]};1 in T&&(E.catchLoc=T[1]),2 in T&&(E.finallyLoc=T[2],E.afterLoc=T[3]),this.tryEntries.push(E)}function B(T){var E=T.completion||{};E.type="normal",delete E.arg,T.completion=E}function H(T){this.tryEntries=[{tryLoc:"root"}],T.forEach(z,this),this.reset(!0)}function $(T){if(T||T===""){var E=T[a];if(E)return E.call(T);if(typeof T.next=="function")return T;if(!isNaN(T.length)){var R=-1,x=function A(){for(;++R=0;--x){var A=this.tryEntries[x],F=A.completion;if(A.tryLoc==="root")return R("end");if(A.tryLoc<=this.prev){var N=i.call(A,"catchLoc"),G=i.call(A,"finallyLoc");if(N&&G){if(this.prev=0;--R){var x=this.tryEntries[R];if(x.tryLoc<=this.prev&&i.call(x,"finallyLoc")&&this.prev=0;--E){var R=this.tryEntries[E];if(R.finallyLoc===T)return this.complete(R.completion,R.afterLoc),B(R),b}},catch:function(T){for(var E=this.tryEntries.length-1;E>=0;--E){var R=this.tryEntries[E];if(R.tryLoc===T){var x=R.completion;if(x.type==="throw"){var A=x.arg;B(R)}return A}}throw Error("illegal catch attempt")},delegateYield:function(T,E,R){return this.delegate={iterator:$(T),resultName:E,nextLoc:R},this.method==="next"&&(this.arg=r),b}},e}function mi(r,e,t,i,n,o,a){try{var s=r[o](a),c=s.value}catch(l){return void t(l)}s.done?e(c):Promise.resolve(c).then(i,n)}function St(r){return function(){var e=this,t=arguments;return new Promise(function(i,n){var o=r.apply(e,t);function a(c){mi(o,i,n,a,s,"next",c)}function s(c){mi(o,i,n,a,s,"throw",c)}a(void 0)})}}var xt={size:{mode:"max",value:512},objectFit:"cover",correctCentering:!1,maxTextureSize:4096,debounceTimeout:500,crossOrigin:"anonymous"},ia=1;function Ft(r){var e=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{},t=e.crossOrigin;return new Promise(function(i,n){var o=new Image;o.addEventListener("load",function(){i(o)},{once:!0}),o.addEventListener("error",function(a){n(a.error)},{once:!0}),t&&o.setAttribute("crossOrigin",t),o.src=r})}function na(r){return Lt.apply(this,arguments)}function Lt(){return Lt=St(ce().mark(function r(e){var t,i,n,o,a,s,c,l,u,h,d,m,f,p=arguments;return ce().wrap(function(g){for(;;)switch(g.prev=g.next){case 0:if(t=p.length>1&&p[1]!==void 0?p[1]:{},i=t.size,n=t.crossOrigin,n!=="use-credentials"){g.next=7;break}return g.next=4,fetch(e,{credentials:"include"});case 4:o=g.sent,g.next=10;break;case 7:return g.next=9,fetch(e);case 9:o=g.sent;case 10:return g.next=12,o.text();case 12:if(a=g.sent,s=new DOMParser().parseFromString(a,"image/svg+xml"),c=s.documentElement,l=c.getAttribute("width"),u=c.getAttribute("height"),!(!l||!u)){g.next=19;break}throw new Error("loadSVGImage: cannot use `size` if target SVG has no definite dimensions.");case 19:return typeof i=="number"&&(c.setAttribute("width",""+i),c.setAttribute("height",""+i)),h=new XMLSerializer().serializeToString(s),d=new Blob([h],{type:"image/svg+xml"}),m=URL.createObjectURL(d),f=Ft(m),f.finally(function(){return URL.revokeObjectURL(m)}),g.abrupt("return",f);case 26:case"end":return g.stop()}},r)})),Lt.apply(this,arguments)}function oa(r){return Nt.apply(this,arguments)}function Nt(){return Nt=St(ce().mark(function r(e){var t,i,n,o,a,s,c=arguments;return ce().wrap(function(u){for(;;)switch(u.prev=u.next){case 0:if(i=c.length>1&&c[1]!==void 0?c[1]:{},n=i.size,o=i.crossOrigin,a=((t=e.split(/[#?]/)[0].split(".").pop())===null||t===void 0?void 0:t.trim().toLowerCase())==="svg",!(a&&n)){u.next=16;break}return u.prev=3,u.next=6,na(e,{size:n,crossOrigin:o});case 6:s=u.sent,u.next=14;break;case 9:return u.prev=9,u.t0=u.catch(3),u.next=13,Ft(e,{crossOrigin:o});case 13:s=u.sent;case 14:u.next=19;break;case 16:return u.next=18,Ft(e,{crossOrigin:o});case 18:s=u.sent;case 19:return u.abrupt("return",s);case 20:case"end":return u.stop()}},r,null,[[3,9]])})),Nt.apply(this,arguments)}function aa(r,e,t){var i=t.objectFit,n=t.size,o=t.correctCentering,a=i==="contain"?Math.max(r.width,r.height):Math.min(r.width,r.height),s=n.mode==="auto"?a:n.mode==="force"?n.value:Math.min(n.value,a),c=(r.width-a)/2,l=(r.height-a)/2;if(o){var u=e.getCorrectionOffset(r,a);c=u.x,l=u.y}return{sourceX:c,sourceY:l,sourceSize:a,destinationSize:s}}function sa(r,e,t){for(var i=e.canvas,n=i.width,o=i.height,a=[],s=t.x,c=t.y,l=t.rowHeight,u=t.maxRowWidth,h={},d=0,m=r.length;do||s+C>n&&c+C+l>o||(s+C>n&&(u=Math.max(u,s),s=0,c+=l,l=C),a.push({key:p,image:b,sourceX:_,sourceY:v,sourceSize:g,destinationX:s,destinationY:c,destinationSize:y}),h[p]={x:s,y:c,size:y},s+=C,l=Math.max(l,C))}u=Math.max(u,s);for(var S=u,w=c+l,P=0,k=a.length;P0&&arguments[0]!==void 0?arguments[0]:{};return Rt(this,e),t=hi(this,e),I(W(t),"canvas",document.createElement("canvas")),I(W(t),"ctx",t.canvas.getContext("2d",{willReadFrequently:!0})),I(W(t),"corrector",new la),I(W(t),"imageStates",{}),I(W(t),"textures",[t.ctx.getImageData(0,0,1,1)]),I(W(t),"lastTextureCursor",{x:0,y:0,rowHeight:0,maxRowWidth:0}),I(W(t),"atlas",{}),t.options=j(j({},xt),i),t.canvas.width=t.options.maxTextureSize,t.canvas.height=t.options.maxTextureSize,t}return Ct(e,[{key:"scheduleGenerateTexture",value:function(){var i=this;typeof this.frameId!="number"&&(typeof this.options.debounceTimeout=="number"?this.frameId=window.setTimeout(function(){i.generateTextures(),i.frameId=void 0},this.options.debounceTimeout):this.generateTextures())}},{key:"generateTextures",value:function(){var i=ca({atlas:this.atlas,textures:this.textures,cursor:this.lastTextureCursor},this.imageStates,this.ctx),n=i.atlas,o=i.textures,a=i.cursor;this.atlas=n,this.textures=o,this.lastTextureCursor=a,this.emit(e.NEW_TEXTURE_EVENT,{atlas:n,textures:o})}},{key:"registerImage",value:function(){var t=St(ce().mark(function n(o){var a,s;return ce().wrap(function(l){for(;;)switch(l.prev=l.next){case 0:if(!this.imageStates[o]){l.next=2;break}return l.abrupt("return");case 2:return this.imageStates[o]={status:"loading"},l.prev=3,a=this.options.size,l.next=7,oa(o,{size:a.mode==="force"?a.value:void 0,crossOrigin:this.options.crossOrigin||void 0});case 7:s=l.sent,this.imageStates[o]=j({status:"ready",image:s},aa(s,this.corrector,this.options)),this.scheduleGenerateTexture(),l.next=15;break;case 12:l.prev=12,l.t0=l.catch(3),this.imageStates[o]={status:"error"};case 15:case"end":return l.stop()}},n,this,[[3,12]])}));function i(n){return t.apply(this,arguments)}return i}()},{key:"getAtlas",value:function(){return this.atlas}},{key:"getTextures",value:function(){return this.textures}}]),e}(ci.EventEmitter);I(Ve,"NEW_TEXTURE_EVENT","newTexture");var ua=["drawHover","drawLabel","drawingMode","keepWithinCircle","padding","colorAttribute","imageAttribute"],vi=WebGLRenderingContext,pi=vi.UNSIGNED_BYTE,Se=vi.FLOAT,ha=j(j({},xt),{},{drawingMode:"background",keepWithinCircle:!0,drawLabel:void 0,drawHover:void 0,padding:0,colorAttribute:"color",imageAttribute:"image"}),da=["u_sizeRatio","u_correctionRatio","u_cameraAngle","u_percentagePadding","u_matrix","u_colorizeImages","u_keepWithinCircle","u_atlas"];function Pt(r){var e,t=document.createElement("canvas").getContext("webgl"),i=Math.min(t.getParameter(t.MAX_TEXTURE_SIZE),xt.maxTextureSize);t.canvas.remove();var n=j(j(j({},ha),{maxTextureSize:i}),r||{}),o=n.drawHover,a=n.drawLabel,s=n.drawingMode,c=n.keepWithinCircle,l=n.padding,u=n.colorAttribute,h=n.imageAttribute,d=Jo(n,ua),m=new Ve(d);return e=function(f){fi(p,f);function p(b,g,_){var v;return Rt(this,p),v=hi(this,p,[b,g,_]),I(W(v),"drawLabel",a),I(W(v),"drawHover",o),I(W(v),"textureManagerCallback",null),v.textureManagerCallback=function(y){var C=y.atlas,S=y.textures,w=S.length!==v.textures.length;v.atlas=C,v.textureImages=S,w&&v.upgradeShaders(),v.bindTextures(),v.latestRenderParams&&v.render(v.latestRenderParams),v.renderer&&v.renderer.refresh&&v.renderer.refresh()},m.on(Ve.NEW_TEXTURE_EVENT,v.textureManagerCallback),v.atlas=m.getAtlas(),v.textureImages=m.getTextures(),v.textures=v.textureImages.map(function(){return b.createTexture()}),v.bindTextures(),v}return Ct(p,[{key:"getDefinition",value:function(){return{VERTICES:3,VERTEX_SHADER_SOURCE:ra,FRAGMENT_SHADER_SOURCE:ea({texturesCount:m.getTextures().length}),METHOD:WebGLRenderingContext.TRIANGLES,UNIFORMS:da,ATTRIBUTES:[{name:"a_position",size:2,type:Se},{name:"a_size",size:1,type:Se},{name:"a_color",size:4,type:pi,normalized:!0},{name:"a_id",size:4,type:pi,normalized:!0},{name:"a_texture",size:4,type:Se},{name:"a_textureIndex",size:1,type:Se}],CONSTANT_ATTRIBUTES:[{name:"a_angle",size:1,type:Se}],CONSTANT_DATA:[[p.ANGLE_1],[p.ANGLE_2],[p.ANGLE_3]]}}},{key:"upgradeShaders",value:function(){var g=this.getDefinition(),_=this.normalProgram,v=_.program,y=_.buffer,C=_.vertexShader,S=_.fragmentShader,w=_.gl;w.deleteProgram(v),w.deleteBuffer(y),w.deleteShader(C),w.deleteShader(S),this.normalProgram=this.getProgramInfo("normal",w,g.VERTEX_SHADER_SOURCE,g.FRAGMENT_SHADER_SOURCE,null)}},{key:"kill",value:function(){var g,_=(g=this.normalProgram)===null||g===void 0?void 0:g.gl;if(_)for(var v=0;v=this.textures.length){var v=g.createTexture();v&&this.textures.push(v)}g.activeTexture(g.TEXTURE0+_),g.bindTexture(g.TEXTURE_2D,this.textures[_]),g.texImage2D(g.TEXTURE_2D,0,g.RGBA,g.RGBA,g.UNSIGNED_BYTE,this.textureImages[_]),g.generateMipmap(g.TEXTURE_2D)}}},{key:"renderProgram",value:function(g,_){if(!_.isPicking)for(var v=_.gl,y=0;yr.length)&&(e=r.length);for(var t=0,i=Array(e);t v_radius) + gl_FragColor = transparent; + else { + gl_FragColor = v_color; + gl_FragColor.a *= bias; + } + #else + // Colors: +`).concat(e.map(function(n,o){var a=n.color,s=[];return"attribute"in a?s.push(" vec4 sliceColor_".concat(o+1," = v_sliceColor_").concat(o+1,";")):"transparent"in a?s.push(" vec4 sliceColor_".concat(o+1," = vec4(0.0, 0.0, 0.0, 0.0);")):s.push(" vec4 sliceColor_".concat(o+1," = u_sliceColor_").concat(o+1,";")),s.push(" sliceColor_".concat(o+1,".a *= bias;")),s.join(` +`)}).join(` +`),` + vec4 color = u_defaultColor; + color.a *= bias; + + // Sizes: +`).concat(e.map(function(n,o){var a=n.value;return" float sliceValue_".concat(o+1," = ").concat("attribute"in a?"v_sliceValue_".concat(o+1):lt(a.value),";")}).join(` +`),` + + // Angles and final color: + float total = `).concat(e.map(function(n,o){return"sliceValue_".concat(o+1)}).join(" + "),`; + float angle_0 = 0.0; + if (total > 0.0) { +`).concat(e.map(function(n,o){return" float angle_".concat(o+1," = angle_").concat(o," + sliceValue_").concat(o+1," * ").concat(2*Math.PI," / total;")}).join(` +`),` + `).concat(e.map(function(n,o){return"if (angle < angle_".concat(o+1,") color = sliceColor_").concat(o+1,";")}).join(` + else `),` + } + + if (dist < v_radius - aaBorder) { + gl_FragColor = color; + } else if (dist < v_radius) { + gl_FragColor = mix(transparent, color, (v_radius - dist) / aaBorder); + } + #endif +} +`);return i}function Sa(r){var e=r.slices,t=r.offset,i=` +attribute vec4 a_id; +attribute vec2 a_position; +attribute float a_size; +attribute float a_angle; + +uniform mat3 u_matrix; +uniform float u_sizeRatio; +uniform float u_correctionRatio; + +varying vec2 v_diffVector; +varying float v_radius; + +`.concat("attribute"in t?`attribute float a_offset; +`:"",` +`).concat("attribute"in t?`varying float v_offset; +`:"",` + +#ifdef PICKING_MODE +varying vec4 v_color; +#else +`).concat(e.flatMap(function(n,o){var a=n.value;return"attribute"in a?["attribute float a_sliceValue_".concat(o+1,";"),"varying float v_sliceValue_".concat(o+1,";")]:[]}).join(` +`),` +`).concat(e.flatMap(function(n,o){var a=n.color;return"attribute"in a?["attribute vec4 a_sliceColor_".concat(o+1,";"),"varying vec4 v_sliceColor_".concat(o+1,";")]:[]}).join(` +`),` +#endif + +const vec4 transparent = vec4(0.0, 0.0, 0.0, 0.0); + +void main() { + float size = a_size * u_correctionRatio / u_sizeRatio * 4.0; + vec2 diffVector = size * vec2(cos(a_angle), sin(a_angle)); + vec2 position = a_position + diffVector; + gl_Position = vec4( + (u_matrix * vec3(position, 1)).xy, + 0, + 1 + ); + + v_radius = size / 2.0; + v_diffVector = diffVector; + `).concat("attribute"in t?`v_offset = a_offset; +`:"",` + + #ifdef PICKING_MODE + v_color = a_id; + #else +`).concat(e.flatMap(function(n,o){var a=n.value;return"attribute"in a?[" v_sliceValue_".concat(o+1," = a_sliceValue_").concat(o+1,";")]:[]}).join(` +`),` +`).concat(e.flatMap(function(n,o){var a=n.color;return"attribute"in a?[" v_sliceColor_".concat(o+1," = a_sliceColor_").concat(o+1,";")]:[]}).join(` +`),` + #endif +} +`);return i}var It="#000000",xa={drawLabel:void 0,drawHover:void 0,defaultColor:It,offset:{value:0}},Ci=WebGLRenderingContext,wi=Ci.UNSIGNED_BYTE,Fe=Ci.FLOAT;function Fa(r){var e,t=Ri(Ri({},xa),r),i=t.slices,n=t.offset,o=t.drawHover,a=t.drawLabel,s=["u_sizeRatio","u_correctionRatio","u_cameraAngle","u_matrix","u_defaultColor"].concat(xe("value"in n?["u_offset"]:[]),xe(i.flatMap(function(c,l){var u=c.color;return"value"in u?["u_sliceColor_".concat(l+1)]:[]})));return e=function(c){Ta(l,c);function l(){var u;va(this,l);for(var h=arguments.length,d=new Array(h),m=0;mr,Si=r=>r*r,xi=r=>r*(2-r),Fi=r=>(r*=2)<1?.5*r*r:-.5*(--r*(r-2)-1),Li=r=>r*r*r,Ni=r=>--r*r*r+1,Pi=r=>(r*=2)<1?.5*r*r*r:.5*((r-=2)*r*r+2),zt={linear:Ai,quadraticIn:Si,quadraticOut:xi,quadraticInOut:Fi,cubicIn:Li,cubicOut:Ni,cubicInOut:Pi},Gt={easing:"quadraticInOut",duration:150};function La(r,e,t,i){const n=Object.assign({},Gt,t),o=typeof n.easing=="function"?n.easing:zt[n.easing],a=Date.now(),s={};for(const u in e){const h=e[u];s[u]={};for(const d in h)s[u][d]=r.getNodeAttribute(u,d)}let c=null;const l=()=>{c=null;let u=(Date.now()-a)/n.duration;if(u>=1){for(const h in e){const d=e[h];for(const m in d)r.setNodeAttribute(h,m,d[m])}typeof i=="function"&&i();return}u=o(u);for(const h in e){const d=e[h],m=s[h];for(const f in d)r.setNodeAttribute(h,f,d[f]*u+m[f]*(1-u))}c=requestAnimationFrame(l)};return l(),()=>{c&&cancelAnimationFrame(c)}}const Ye={black:"#000000",silver:"#C0C0C0",gray:"#808080",grey:"#808080",white:"#FFFFFF",maroon:"#800000",red:"#FF0000",purple:"#800080",fuchsia:"#FF00FF",green:"#008000",lime:"#00FF00",olive:"#808000",yellow:"#FFFF00",navy:"#000080",blue:"#0000FF",teal:"#008080",aqua:"#00FFFF",darkblue:"#00008B",mediumblue:"#0000CD",darkgreen:"#006400",darkcyan:"#008B8B",deepskyblue:"#00BFFF",darkturquoise:"#00CED1",mediumspringgreen:"#00FA9A",springgreen:"#00FF7F",cyan:"#00FFFF",midnightblue:"#191970",dodgerblue:"#1E90FF",lightseagreen:"#20B2AA",forestgreen:"#228B22",seagreen:"#2E8B57",darkslategray:"#2F4F4F",darkslategrey:"#2F4F4F",limegreen:"#32CD32",mediumseagreen:"#3CB371",turquoise:"#40E0D0",royalblue:"#4169E1",steelblue:"#4682B4",darkslateblue:"#483D8B",mediumturquoise:"#48D1CC",indigo:"#4B0082",darkolivegreen:"#556B2F",cadetblue:"#5F9EA0",cornflowerblue:"#6495ED",rebeccapurple:"#663399",mediumaquamarine:"#66CDAA",dimgray:"#696969",dimgrey:"#696969",slateblue:"#6A5ACD",olivedrab:"#6B8E23",slategray:"#708090",slategrey:"#708090",lightslategray:"#778899",lightslategrey:"#778899",mediumslateblue:"#7B68EE",lawngreen:"#7CFC00",chartreuse:"#7FFF00",aquamarine:"#7FFFD4",skyblue:"#87CEEB",lightskyblue:"#87CEFA",blueviolet:"#8A2BE2",darkred:"#8B0000",darkmagenta:"#8B008B",saddlebrown:"#8B4513",darkseagreen:"#8FBC8F",lightgreen:"#90EE90",mediumpurple:"#9370DB",darkviolet:"#9400D3",palegreen:"#98FB98",darkorchid:"#9932CC",yellowgreen:"#9ACD32",sienna:"#A0522D",brown:"#A52A2A",darkgray:"#A9A9A9",darkgrey:"#A9A9A9",lightblue:"#ADD8E6",greenyellow:"#ADFF2F",paleturquoise:"#AFEEEE",lightsteelblue:"#B0C4DE",powderblue:"#B0E0E6",firebrick:"#B22222",darkgoldenrod:"#B8860B",mediumorchid:"#BA55D3",rosybrown:"#BC8F8F",darkkhaki:"#BDB76B",mediumvioletred:"#C71585",indianred:"#CD5C5C",peru:"#CD853F",chocolate:"#D2691E",tan:"#D2B48C",lightgray:"#D3D3D3",lightgrey:"#D3D3D3",thistle:"#D8BFD8",orchid:"#DA70D6",goldenrod:"#DAA520",palevioletred:"#DB7093",crimson:"#DC143C",gainsboro:"#DCDCDC",plum:"#DDA0DD",burlywood:"#DEB887",lightcyan:"#E0FFFF",lavender:"#E6E6FA",darksalmon:"#E9967A",violet:"#EE82EE",palegoldenrod:"#EEE8AA",lightcoral:"#F08080",khaki:"#F0E68C",aliceblue:"#F0F8FF",honeydew:"#F0FFF0",azure:"#F0FFFF",sandybrown:"#F4A460",wheat:"#F5DEB3",beige:"#F5F5DC",whitesmoke:"#F5F5F5",mintcream:"#F5FFFA",ghostwhite:"#F8F8FF",salmon:"#FA8072",antiquewhite:"#FAEBD7",linen:"#FAF0E6",lightgoldenrodyellow:"#FAFAD2",oldlace:"#FDF5E6",magenta:"#FF00FF",deeppink:"#FF1493",orangered:"#FF4500",tomato:"#FF6347",hotpink:"#FF69B4",coral:"#FF7F50",darkorange:"#FF8C00",lightsalmon:"#FFA07A",orange:"#FFA500",lightpink:"#FFB6C1",pink:"#FFC0CB",gold:"#FFD700",peachpuff:"#FFDAB9",navajowhite:"#FFDEAD",moccasin:"#FFE4B5",bisque:"#FFE4C4",mistyrose:"#FFE4E1",blanchedalmond:"#FFEBCD",papayawhip:"#FFEFD5",lavenderblush:"#FFF0F5",seashell:"#FFF5EE",cornsilk:"#FFF8DC",lemonchiffon:"#FFFACD",floralwhite:"#FFFAF0",snow:"#FFFAFA",lightyellow:"#FFFFE0",ivory:"#FFFFF0"};function Na(r,e,t,i){const n=i||new Uint8Array(4);return r.readPixels(e,t,1,1,r.RGBA,r.UNSIGNED_BYTE,n),n}const Di=new Int8Array(4),Xe=new Int32Array(Di.buffer,0,1),Oi=new Float32Array(Di.buffer,0,1),Pa=/^\s*rgba?\s*\(/,Da=/^\s*rgba?\s*\(\s*([0-9]*)\s*,\s*([0-9]*)\s*,\s*([0-9]*)(?:\s*,\s*(.*)?)?\)\s*$/;function ki(r){let e=0,t=0,i=0,n=1;if(r[0]==="#")r.length===4?(e=parseInt(r.charAt(1)+r.charAt(1),16),t=parseInt(r.charAt(2)+r.charAt(2),16),i=parseInt(r.charAt(3)+r.charAt(3),16)):(e=parseInt(r.charAt(1)+r.charAt(2),16),t=parseInt(r.charAt(3)+r.charAt(4),16),i=parseInt(r.charAt(5)+r.charAt(6),16)),r.length===9&&(n=parseInt(r.charAt(7)+r.charAt(8),16)/255);else if(Pa.test(r)){const o=r.match(Da);o&&(e=+o[1],t=+o[2],i=+o[3],o[4]&&(n=+o[4]))}return{r:e,g:t,b:i,a:n}}const ye={};for(const r in Ye)ye[r]=Y(Ye[r]),ye[Ye[r]]=ye[r];function Mt(r,e,t,i,n){return Xe[0]=i<<24|t<<16|e<<8|r,n&&(Xe[0]=Xe[0]&4278190079),Oi[0]}function Y(r){if(r=r.toLowerCase(),typeof ye[r]<"u")return ye[r];const e=ki(r),{r:t,g:i,b:n}=e;let{a:o}=e;o=o*255|0;const a=Mt(t,i,n,o,!0);return ye[r]=a,a}function Oa(r,e){Oi[0]=Y(r);let t=Xe[0];e&&(t=t|16777216);const i=t&255,n=t>>8&255,o=t>>16&255,a=t>>24&255;return[i,n,o,a]}const Ut={};function Bt(r){if(typeof Ut[r]<"u")return Ut[r];const e=(r&16711680)>>>16,t=(r&65280)>>>8,i=r&255,o=Mt(e,t,i,255,!0);return Ut[r]=o,o}function Ht(r,e,t,i){return t+(e<<8)+(r<<16)}function $t(r,e,t,i,n,o){const a=Math.floor(t/o*n),s=Math.floor(r.drawingBufferHeight/o-i/o*n),c=new Uint8Array(4);r.bindFramebuffer(r.FRAMEBUFFER,e),r.readPixels(a,s,1,1,r.RGBA,r.UNSIGNED_BYTE,c);const[l,u,h,d]=c;return[l,u,h,d]}function V(){return Float32Array.of(1,0,0,0,1,0,0,0,1)}function Le(r,e,t){return r[0]=e,r[4]=typeof t=="number"?t:e,r}function jt(r,e){const t=Math.sin(e),i=Math.cos(e);return r[0]=i,r[1]=t,r[3]=-t,r[4]=i,r}function Vt(r,e,t){return r[6]=e,r[7]=t,r}function Z(r,e){const t=r[0],i=r[1],n=r[2],o=r[3],a=r[4],s=r[5],c=r[6],l=r[7],u=r[8],h=e[0],d=e[1],m=e[2],f=e[3],p=e[4],b=e[5],g=e[6],_=e[7],v=e[8];return r[0]=h*t+d*o+m*c,r[1]=h*i+d*a+m*l,r[2]=h*n+d*s+m*u,r[3]=f*t+p*o+b*c,r[4]=f*i+p*a+b*l,r[5]=f*n+p*s+b*u,r[6]=g*t+_*o+v*c,r[7]=g*i+_*a+v*l,r[8]=g*n+_*s+v*u,r}function qe(r,e,t=1){const i=r[0],n=r[1],o=r[3],a=r[4],s=r[6],c=r[7],l=e.x,u=e.y;return{x:l*i+u*o+s*t,y:l*n+u*a+c*t}}function Ii(r,e){const t=r.height/r.width,i=e.height/e.width;return t<1&&i>1||t>1&&i<1?1:Math.min(Math.max(i,1/i),Math.max(1/t,t))}function Ee(r,e,t,i,n){const{angle:o,ratio:a,x:s,y:c}=r,{width:l,height:u}=e,h=V(),d=Math.min(l,u)-2*i,m=Ii(e,t);return n?(Z(h,Vt(V(),s,c)),Z(h,Le(V(),a)),Z(h,jt(V(),o)),Z(h,Le(V(),l/d/2/m,u/d/2/m))):(Z(h,Le(V(),2*(d/l)*m,2*(d/u)*m)),Z(h,jt(V(),-o)),Z(h,Le(V(),1/a)),Z(h,Vt(V(),-s,-c))),h}function zi(r,e,t){const{x:i,y:n}=qe(r,{x:Math.cos(e.angle),y:Math.sin(e.angle)},0);return 1/Math.sqrt(Math.pow(i,2)+Math.pow(n,2))/t.width}function Wt(r,e){const t=e.size;if(t===0)return;const i=r.length;r.length+=t;let n=0;e.forEach(o=>{r[i+n]=o,n++})}function Gi(r){return typeof r=="object"&&r!==null&&r.constructor===Object}function Ke(r,...e){r=r||{};for(let t=0,i=e.length;t{const{x:s,y:c}=a;st&&(t=s),cn&&(n=c)}),{x:[e,t],y:[i,n]}}function Bi(r){if(!eo(r))throw new Error("Sigma: invalid graph instance.");r.forEachNode((e,t)=>{if(!Number.isFinite(t.x)||!Number.isFinite(t.y))throw new Error(`Sigma: Coordinates of node ${e} are invalid. A node must have a numeric 'x' and 'y' attribute.`)})}function Hi(r,e,t){const i=document.createElement(r);if(e)for(const n in e)i.style[n]=e[n];if(t)for(const n in t)i.setAttribute(n,t[n]);return i}function Yt(){return typeof window.devicePixelRatio<"u"?window.devicePixelRatio:1}function Xt(r,e,t){return t.sort(function(i,n){const o=e(i)||0,a=e(n)||0;return oa?1:0})}function qt(r){const{x:[e,t],y:[i,n]}=r;let o=Math.max(t-e,n-i),a=(t+e)/2,s=(n+i)/2;(o===0||Math.abs(o)===1/0||isNaN(o))&&(o=1),isNaN(a)&&(a=0),isNaN(s)&&(s=0);const c=l=>({x:.5+(l.x-a)/o,y:.5+(l.y-s)/o});return c.applyTo=l=>{l.x=.5+(l.x-a)/o,l.y=.5+(l.y-s)/o},c.inverse=l=>({x:a+o*(l.x-.5),y:s+o*(l.y-.5)}),c.ratio=o,c}const ka=Object.freeze(Object.defineProperty({__proto__:null,ANIMATE_DEFAULTS:Gt,HTML_COLORS:Ye,animateNodes:La,assign:Ke,assignDeep:Mi,colorToArray:Oa,colorToIndex:Ht,createElement:Hi,createNormalizationFunction:qt,cubicIn:Li,cubicInOut:Pi,cubicOut:Ni,easings:zt,extend:Wt,extractPixel:Na,floatColor:Y,getCorrectionRatio:Ii,getMatrixImpact:zi,getPixelColor:$t,getPixelRatio:Yt,graphExtent:Ui,identity:V,indexToColor:Bt,isPlainObject:Gi,linear:Ai,matrixFromCamera:Ee,multiply:Z,multiplyVec2:qe,parseColor:ki,quadraticIn:Si,quadraticInOut:Fi,quadraticOut:xi,rgbaToFloat:Mt,rotate:jt,scale:Le,translate:Vt,validateGraph:Bi,zIndexOrdering:Xt},Symbol.toStringTag,{value:"Module"}));function $i(r){return r.normalized?1:r.size}function Ze(r){let e=0;return r.forEach(t=>e+=$i(t)),e}function ji(r,e,t){const i=r==="VERTEX"?e.VERTEX_SHADER:e.FRAGMENT_SHADER,n=e.createShader(i);if(n===null)throw new Error("loadShader: error while creating the shader");if(e.shaderSource(n,t),e.compileShader(n),!e.getShaderParameter(n,e.COMPILE_STATUS)){const a=e.getShaderInfoLog(n);throw e.deleteShader(n),new Error(`loadShader: error while compiling the shader: +${a} +${t}`)}return n}function Vi(r,e){return ji("VERTEX",r,e)}function Wi(r,e){return ji("FRAGMENT",r,e)}function Yi(r,e){const t=r.createProgram();if(t===null)throw new Error("loadProgram: error while creating the program.");let i,n;for(i=0,n=e.length;i{const p=t.getUniformLocation(u,f);p&&(h[f]=p)});const d={};a.ATTRIBUTES.forEach(f=>{d[f.name]=t.getAttribLocation(u,f.name)});let m;if("CONSTANT_ATTRIBUTES"in a&&(a.CONSTANT_ATTRIBUTES.forEach(f=>{d[f.name]=t.getAttribLocation(u,f.name)}),m=t.createBuffer(),m===null))throw new Error("Program: error while creating the WebGL constant buffer.");return{name:e,program:u,gl:t,frameBuffer:o,buffer:s,constantBuffer:m||{},uniformLocations:h,attributeLocations:d,isPicking:e==="pick",vertexShader:c,fragmentShader:l}}bindProgram(e){let t=0;const{gl:i,buffer:n}=e;this.isInstanced?(i.bindBuffer(i.ARRAY_BUFFER,e.constantBuffer),t=0,this.CONSTANT_ATTRIBUTES.forEach(o=>t+=this.bindAttribute(o,e,t,!1)),i.bufferData(i.ARRAY_BUFFER,this.constantArray,i.STATIC_DRAW),i.bindBuffer(i.ARRAY_BUFFER,e.buffer),t=0,this.ATTRIBUTES.forEach(o=>t+=this.bindAttribute(o,e,t,!0)),i.bufferData(i.ARRAY_BUFFER,this.array,i.DYNAMIC_DRAW)):(i.bindBuffer(i.ARRAY_BUFFER,n),t=0,this.ATTRIBUTES.forEach(o=>t+=this.bindAttribute(o,e,t)),i.bufferData(i.ARRAY_BUFFER,this.array,i.DYNAMIC_DRAW)),i.bindBuffer(i.ARRAY_BUFFER,null)}unbindProgram(e){this.isInstanced?(this.CONSTANT_ATTRIBUTES.forEach(t=>this.unbindAttribute(t,e,!1)),this.ATTRIBUTES.forEach(t=>this.unbindAttribute(t,e,!0))):this.ATTRIBUTES.forEach(t=>this.unbindAttribute(t,e))}bindAttribute(e,t,i,n){const o=za[e.type];if(typeof o!="number")throw new Error(`Program.bind: yet unsupported attribute type "${e.type}"`);const a=t.attributeLocations[e.name],s=t.gl;if(a!==-1){s.enableVertexAttribArray(a);const c=this.isInstanced?(n?this.ATTRIBUTES_ITEMS_COUNT:Ze(this.CONSTANT_ATTRIBUTES))*Float32Array.BYTES_PER_ELEMENT:this.ATTRIBUTES_ITEMS_COUNT*Float32Array.BYTES_PER_ELEMENT;if(s.vertexAttribPointer(a,e.size,e.type,e.normalized||!1,c,i),this.isInstanced&&n)if(s instanceof WebGL2RenderingContext)s.vertexAttribDivisor(a,1);else{const l=s.getExtension("ANGLE_instanced_arrays");l&&l.vertexAttribDivisorANGLE(a,1)}}return e.size*o}unbindAttribute(e,t,i){const n=t.attributeLocations[e.name],o=t.gl;if(n!==-1&&(o.disableVertexAttribArray(n),this.isInstanced&&i))if(o instanceof WebGL2RenderingContext)o.vertexAttribDivisor(n,0);else{const a=o.getExtension("ANGLE_instanced_arrays");a&&a.vertexAttribDivisorANGLE(n,0)}}reallocate(e){e!==this.capacity&&(this.capacity=e,this.verticesCount=this.VERTICES*e,this.array=new Float32Array(this.isInstanced?this.capacity*this.ATTRIBUTES_ITEMS_COUNT:this.verticesCount*this.ATTRIBUTES_ITEMS_COUNT))}hasNothingToRender(){return this.verticesCount===0}renderProgram(e,t){const{gl:i,program:n}=t;i.enable(i.BLEND),i.useProgram(n),this.setUniforms(e,t),this.drawWebGL(this.METHOD,t)}render(e){this.hasNothingToRender()||(this.pickProgram&&(this.pickProgram.gl.viewport(0,0,e.width*e.pixelRatio/e.downSizingRatio,e.height*e.pixelRatio/e.downSizingRatio),this.bindProgram(this.pickProgram),this.renderProgram({...e,pixelRatio:e.pixelRatio/e.downSizingRatio},this.pickProgram),this.unbindProgram(this.pickProgram)),this.normalProgram.gl.viewport(0,0,e.width*e.pixelRatio,e.height*e.pixelRatio),this.bindProgram(this.normalProgram),this.renderProgram(e,this.normalProgram),this.unbindProgram(this.normalProgram))}drawWebGL(e,{gl:t,frameBuffer:i}){if(t.bindFramebuffer(t.FRAMEBUFFER,i),!this.isInstanced)t.drawArrays(e,0,this.verticesCount);else if(t instanceof WebGL2RenderingContext)t.drawArraysInstanced(e,0,this.VERTICES,this.capacity);else{const n=t.getExtension("ANGLE_instanced_arrays");n&&n.drawArraysInstancedANGLE(e,0,this.VERTICES,this.capacity)}}}class Ga extends Zt{}class Jt extends Qt{kill(){super.kill()}process(e,t,i){let n=t*this.STRIDE;if(i.hidden){for(let o=n+this.STRIDE;nnew s(n,o,a))}reallocate(n){this.programs.forEach(o=>o.reallocate(n))}process(n,o,a){this.programs.forEach(s=>s.process(n,o,a))}render(n){this.programs.forEach(o=>o.render(n))}kill(){this.programs.forEach(n=>n.kill())}}}class Ua extends Zt{}class le extends Qt{constructor(){super(...arguments),this.drawLabel=void 0}kill(){super.kill()}process(e,t,i,n,o){let a=t*this.STRIDE;if(o.hidden||i.hidden||n.hidden){for(let s=a+this.STRIDE;anew a(i,n,o))}reallocate(i){this.programs.forEach(n=>n.reallocate(i))}process(i,n,o,a,s){this.programs.forEach(c=>c.process(i,n,o,a,s))}render(i){this.programs.forEach(n=>n.render(i))}kill(){this.programs.forEach(i=>i.kill())}}}function qi(r,e,t,i,n){const o=n.edgeLabelSize,a=n.edgeLabelFont,s=n.edgeLabelWeight,c=n.edgeLabelColor.attribute?e[n.edgeLabelColor.attribute]||n.edgeLabelColor.color||"#000":n.edgeLabelColor.color;let l=e.label;if(!l)return;r.fillStyle=c,r.font=`${s} ${o}px ${a}`;const u=t.size,h=i.size;let d=t.x,m=t.y,f=i.x,p=i.y,b=(d+f)/2,g=(m+p)/2,_=f-d,v=p-m,y=Math.sqrt(_*_+v*v);if(yy){const w="…";for(l=l+w,C=r.measureText(l).width;C>y&&l.length>1;)l=l.slice(0,-2)+w,C=r.measureText(l).width;if(l.length<4)return}let S;_>0?v>0?S=Math.acos(_/y):S=Math.asin(v/y):v>0?S=Math.acos(_/y)+Math.PI:S=Math.asin(_/y)+Math.PI/2,r.save(),r.translate(b,g),r.rotate(S),r.fillText(l,-C/2,e.size/2+o),r.restore()}function tr(r,e,t){if(!e.label)return;const i=t.labelSize,n=t.labelFont,o=t.labelWeight,a=t.labelColor.attribute?e[t.labelColor.attribute]||t.labelColor.color||"#000":t.labelColor.color;r.fillStyle=a,r.font=`${o} ${i}px ${n}`,r.fillText(e.label,e.x+e.size+3,e.y+i/3)}function Ki(r,e,t){const i=t.labelSize,n=t.labelFont,o=t.labelWeight;r.font=`${o} ${i}px ${n}`,r.fillStyle="#FFF",r.shadowOffsetX=0,r.shadowOffsetY=0,r.shadowBlur=8,r.shadowColor="#000";const a=2;if(typeof e.label=="string"){const s=r.measureText(e.label).width,c=Math.round(s+5),l=Math.round(i+2*a),u=Math.max(e.size,i/2)+a,h=Math.asin(l/2/u),d=Math.sqrt(Math.abs(Math.pow(u,2)-Math.pow(l/2,2)));r.beginPath(),r.moveTo(e.x+d,e.y+l/2),r.lineTo(e.x+u+c,e.y+l/2),r.lineTo(e.x+u+c,e.y-l/2),r.lineTo(e.x+d,e.y-l/2),r.arc(e.x,e.y,u,h,-h),r.closePath(),r.fill()}else r.beginPath(),r.arc(e.x,e.y,e.size+a,0,Math.PI*2),r.closePath(),r.fill();r.shadowOffsetX=0,r.shadowOffsetY=0,r.shadowBlur=0,tr(r,e,t)}const Ba=` +precision highp float; + +varying vec4 v_color; +varying vec2 v_diffVector; +varying float v_radius; + +uniform float u_correctionRatio; + +const vec4 transparent = vec4(0.0, 0.0, 0.0, 0.0); + +void main(void) { + float border = u_correctionRatio * 2.0; + float dist = length(v_diffVector) - v_radius + border; + + // No antialiasing for picking mode: + #ifdef PICKING_MODE + if (dist > border) + gl_FragColor = transparent; + else + gl_FragColor = v_color; + + #else + float t = 0.0; + if (dist > border) + t = 1.0; + else if (dist > 0.0) + t = dist / border; + + gl_FragColor = mix(v_color, transparent, t); + #endif +} +`,Ha=` +attribute vec4 a_id; +attribute vec4 a_color; +attribute vec2 a_position; +attribute float a_size; +attribute float a_angle; + +uniform mat3 u_matrix; +uniform float u_sizeRatio; +uniform float u_correctionRatio; + +varying vec4 v_color; +varying vec2 v_diffVector; +varying float v_radius; +varying float v_border; + +const float bias = 255.0 / 254.0; + +void main() { + float size = a_size * u_correctionRatio / u_sizeRatio * 4.0; + vec2 diffVector = size * vec2(cos(a_angle), sin(a_angle)); + vec2 position = a_position + diffVector; + gl_Position = vec4( + (u_matrix * vec3(position, 1)).xy, + 0, + 1 + ); + + v_diffVector = diffVector; + v_radius = size / 2.0; + + #ifdef PICKING_MODE + // For picking mode, we use the ID as the color: + v_color = a_id; + #else + // For normal mode, we use the color: + v_color = a_color; + #endif + + v_color.a *= bias; +} +`,{UNSIGNED_BYTE:Zi,FLOAT:rr}=WebGLRenderingContext,$a=["u_sizeRatio","u_correctionRatio","u_matrix"],ie=class ie extends Jt{getDefinition(){return{VERTICES:3,VERTEX_SHADER_SOURCE:Ha,FRAGMENT_SHADER_SOURCE:Ba,METHOD:WebGLRenderingContext.TRIANGLES,UNIFORMS:$a,ATTRIBUTES:[{name:"a_position",size:2,type:rr},{name:"a_size",size:1,type:rr},{name:"a_color",size:4,type:Zi,normalized:!0},{name:"a_id",size:4,type:Zi,normalized:!0}],CONSTANT_ATTRIBUTES:[{name:"a_angle",size:1,type:rr}],CONSTANT_DATA:[[ie.ANGLE_1],[ie.ANGLE_2],[ie.ANGLE_3]]}}processVisibleItem(e,t,i){const n=this.array,o=Y(i.color);n[t++]=i.x,n[t++]=i.y,n[t++]=i.size,n[t++]=o,n[t++]=e}setUniforms(e,{gl:t,uniformLocations:i}){const{u_sizeRatio:n,u_correctionRatio:o,u_matrix:a}=i;t.uniform1f(o,e.correctionRatio),t.uniform1f(n,e.sizeRatio),t.uniformMatrix3fv(a,!1,e.matrix)}};ie.ANGLE_1=0,ie.ANGLE_2=2*Math.PI/3,ie.ANGLE_3=4*Math.PI/3;let Qe=ie;const ja=` +precision mediump float; + +varying vec4 v_color; +varying float v_border; + +const float radius = 0.5; +const vec4 transparent = vec4(0.0, 0.0, 0.0, 0.0); + +void main(void) { + vec2 m = gl_PointCoord - vec2(0.5, 0.5); + float dist = radius - length(m); + + // No antialiasing for picking mode: + #ifdef PICKING_MODE + if (dist > v_border) + gl_FragColor = v_color; + else + gl_FragColor = transparent; + + #else + float t = 0.0; + if (dist > v_border) + t = 1.0; + else if (dist > 0.0) + t = dist / v_border; + + gl_FragColor = mix(transparent, v_color, t); + #endif +} +`,Va=` +attribute vec4 a_id; +attribute vec4 a_color; +attribute vec2 a_position; +attribute float a_size; + +uniform float u_sizeRatio; +uniform float u_pixelRatio; +uniform mat3 u_matrix; + +varying vec4 v_color; +varying float v_border; + +const float bias = 255.0 / 254.0; + +void main() { + gl_Position = vec4( + (u_matrix * vec3(a_position, 1)).xy, + 0, + 1 + ); + + // Multiply the point size twice: + // - x SCALING_RATIO to correct the canvas scaling + // - x 2 to correct the formulae + gl_PointSize = a_size / u_sizeRatio * u_pixelRatio * 2.0; + + v_border = (0.5 / a_size) * u_sizeRatio; + + #ifdef PICKING_MODE + // For picking mode, we use the ID as the color: + v_color = a_id; + #else + // For normal mode, we use the color: + v_color = a_color; + #endif + + v_color.a *= bias; +} +`,{UNSIGNED_BYTE:Qi,FLOAT:Ji}=WebGLRenderingContext,Wa=["u_sizeRatio","u_pixelRatio","u_matrix"];class Ya extends Jt{getDefinition(){return{VERTICES:1,VERTEX_SHADER_SOURCE:Va,FRAGMENT_SHADER_SOURCE:ja,METHOD:WebGLRenderingContext.POINTS,UNIFORMS:Wa,ATTRIBUTES:[{name:"a_position",size:2,type:Ji},{name:"a_size",size:1,type:Ji},{name:"a_color",size:4,type:Qi,normalized:!0},{name:"a_id",size:4,type:Qi,normalized:!0}]}}processVisibleItem(e,t,i){const n=this.array;n[t++]=i.x,n[t++]=i.y,n[t++]=i.size,n[t++]=Y(i.color),n[t++]=e}setUniforms({sizeRatio:e,pixelRatio:t,matrix:i},{gl:n,uniformLocations:o}){const{u_sizeRatio:a,u_pixelRatio:s,u_matrix:c}=o;n.uniform1f(s,t),n.uniform1f(a,e),n.uniformMatrix3fv(c,!1,i)}}const Xa=` +precision mediump float; + +varying vec4 v_color; + +void main(void) { + gl_FragColor = v_color; +} +`,qa=` +attribute vec2 a_position; +attribute vec2 a_normal; +attribute float a_radius; +attribute vec3 a_barycentric; + +#ifdef PICKING_MODE +attribute vec4 a_id; +#else +attribute vec4 a_color; +#endif + +uniform mat3 u_matrix; +uniform float u_sizeRatio; +uniform float u_correctionRatio; +uniform float u_minEdgeThickness; +uniform float u_lengthToThicknessRatio; +uniform float u_widenessToThicknessRatio; + +varying vec4 v_color; + +const float bias = 255.0 / 254.0; + +void main() { + float minThickness = u_minEdgeThickness; + + float normalLength = length(a_normal); + vec2 unitNormal = a_normal / normalLength; + + // These first computations are taken from edge.vert.glsl and + // edge.clamped.vert.glsl. Please read it to get better comments on what's + // happening: + float pixelsThickness = max(normalLength / u_sizeRatio, minThickness); + float webGLThickness = pixelsThickness * u_correctionRatio; + float webGLNodeRadius = a_radius * 2.0 * u_correctionRatio / u_sizeRatio; + float webGLArrowHeadLength = webGLThickness * u_lengthToThicknessRatio * 2.0; + float webGLArrowHeadThickness = webGLThickness * u_widenessToThicknessRatio; + + float da = a_barycentric.x; + float db = a_barycentric.y; + float dc = a_barycentric.z; + + vec2 delta = vec2( + da * (webGLNodeRadius * unitNormal.y) + + db * ((webGLNodeRadius + webGLArrowHeadLength) * unitNormal.y + webGLArrowHeadThickness * unitNormal.x) + + dc * ((webGLNodeRadius + webGLArrowHeadLength) * unitNormal.y - webGLArrowHeadThickness * unitNormal.x), + + da * (-webGLNodeRadius * unitNormal.x) + + db * (-(webGLNodeRadius + webGLArrowHeadLength) * unitNormal.x + webGLArrowHeadThickness * unitNormal.y) + + dc * (-(webGLNodeRadius + webGLArrowHeadLength) * unitNormal.x - webGLArrowHeadThickness * unitNormal.y) + ); + + vec2 position = (u_matrix * vec3(a_position + delta, 1)).xy; + + gl_Position = vec4(position, 0, 1); + + #ifdef PICKING_MODE + // For picking mode, we use the ID as the color: + v_color = a_id; + #else + // For normal mode, we use the color: + v_color = a_color; + #endif + + v_color.a *= bias; +} +`,{UNSIGNED_BYTE:en,FLOAT:Je}=WebGLRenderingContext,Ka=["u_matrix","u_sizeRatio","u_correctionRatio","u_minEdgeThickness","u_lengthToThicknessRatio","u_widenessToThicknessRatio"],et={extremity:"target",lengthToThicknessRatio:2.5,widenessToThicknessRatio:2};function Ne(r){const e={...et,...r||{}};return class extends le{getDefinition(){return{VERTICES:3,VERTEX_SHADER_SOURCE:qa,FRAGMENT_SHADER_SOURCE:Xa,METHOD:WebGLRenderingContext.TRIANGLES,UNIFORMS:Ka,ATTRIBUTES:[{name:"a_position",size:2,type:Je},{name:"a_normal",size:2,type:Je},{name:"a_radius",size:1,type:Je},{name:"a_color",size:4,type:en,normalized:!0},{name:"a_id",size:4,type:en,normalized:!0}],CONSTANT_ATTRIBUTES:[{name:"a_barycentric",size:3,type:Je}],CONSTANT_DATA:[[1,0,0],[0,1,0],[0,0,1]]}}processVisibleItem(i,n,o,a,s){e.extremity==="source"&&([o,a]=[a,o]);const c=s.size||1,l=a.size||1,u=o.x,h=o.y,d=a.x,m=a.y,f=Y(s.color),p=d-u,b=m-h;let g=p*p+b*b,_=0,v=0;g&&(g=1/Math.sqrt(g),_=-b*g*c,v=p*g*c);const y=this.array;y[n++]=d,y[n++]=m,y[n++]=-_,y[n++]=-v,y[n++]=l,y[n++]=f,y[n++]=i}setUniforms(i,{gl:n,uniformLocations:o}){const{u_matrix:a,u_sizeRatio:s,u_correctionRatio:c,u_minEdgeThickness:l,u_lengthToThicknessRatio:u,u_widenessToThicknessRatio:h}=o;n.uniformMatrix3fv(a,!1,i.matrix),n.uniform1f(s,i.sizeRatio),n.uniform1f(c,i.correctionRatio),n.uniform1f(l,i.minEdgeThickness),n.uniform1f(u,e.lengthToThicknessRatio),n.uniform1f(h,e.widenessToThicknessRatio)}}}const Za=Ne(),ir=` +precision mediump float; + +varying vec4 v_color; +varying vec2 v_normal; +varying float v_thickness; +varying float v_feather; + +const vec4 transparent = vec4(0.0, 0.0, 0.0, 0.0); + +void main(void) { + // We only handle antialiasing for normal mode: + #ifdef PICKING_MODE + gl_FragColor = v_color; + #else + float dist = length(v_normal) * v_thickness; + + float t = smoothstep( + v_thickness - v_feather, + v_thickness, + dist + ); + + gl_FragColor = mix(v_color, transparent, t); + #endif +} +`,Qa=` +attribute vec4 a_id; +attribute vec4 a_color; +attribute vec2 a_normal; +attribute float a_normalCoef; +attribute vec2 a_positionStart; +attribute vec2 a_positionEnd; +attribute float a_positionCoef; +attribute float a_radius; +attribute float a_radiusCoef; + +uniform mat3 u_matrix; +uniform float u_zoomRatio; +uniform float u_sizeRatio; +uniform float u_pixelRatio; +uniform float u_correctionRatio; +uniform float u_minEdgeThickness; +uniform float u_lengthToThicknessRatio; +uniform float u_feather; + +varying vec4 v_color; +varying vec2 v_normal; +varying float v_thickness; +varying float v_feather; + +const float bias = 255.0 / 254.0; + +void main() { + float minThickness = u_minEdgeThickness; + + float radius = a_radius * a_radiusCoef; + vec2 normal = a_normal * a_normalCoef; + vec2 position = a_positionStart * (1.0 - a_positionCoef) + a_positionEnd * a_positionCoef; + + float normalLength = length(normal); + vec2 unitNormal = normal / normalLength; + + // These first computations are taken from edge.vert.glsl. Please read it to + // get better comments on what's happening: + float pixelsThickness = max(normalLength, minThickness * u_sizeRatio); + float webGLThickness = pixelsThickness * u_correctionRatio / u_sizeRatio; + + // Here, we move the point to leave space for the arrow head: + float direction = sign(radius); + float webGLNodeRadius = direction * radius * 2.0 * u_correctionRatio / u_sizeRatio; + float webGLArrowHeadLength = webGLThickness * u_lengthToThicknessRatio * 2.0; + + vec2 compensationVector = vec2(-direction * unitNormal.y, direction * unitNormal.x) * (webGLNodeRadius + webGLArrowHeadLength); + + // Here is the proper position of the vertex + gl_Position = vec4((u_matrix * vec3(position + unitNormal * webGLThickness + compensationVector, 1)).xy, 0, 1); + + v_thickness = webGLThickness / u_zoomRatio; + + v_normal = unitNormal; + + v_feather = u_feather * u_correctionRatio / u_zoomRatio / u_pixelRatio * 2.0; + + #ifdef PICKING_MODE + // For picking mode, we use the ID as the color: + v_color = a_id; + #else + // For normal mode, we use the color: + v_color = a_color; + #endif + + v_color.a *= bias; +} +`,{UNSIGNED_BYTE:tn,FLOAT:ue}=WebGLRenderingContext,Ja=["u_matrix","u_zoomRatio","u_sizeRatio","u_correctionRatio","u_pixelRatio","u_feather","u_minEdgeThickness","u_lengthToThicknessRatio"],rn={lengthToThicknessRatio:et.lengthToThicknessRatio};function nr(r){const e={...rn,...r||{}};return class extends le{getDefinition(){return{VERTICES:6,VERTEX_SHADER_SOURCE:Qa,FRAGMENT_SHADER_SOURCE:ir,METHOD:WebGLRenderingContext.TRIANGLES,UNIFORMS:Ja,ATTRIBUTES:[{name:"a_positionStart",size:2,type:ue},{name:"a_positionEnd",size:2,type:ue},{name:"a_normal",size:2,type:ue},{name:"a_color",size:4,type:tn,normalized:!0},{name:"a_id",size:4,type:tn,normalized:!0},{name:"a_radius",size:1,type:ue}],CONSTANT_ATTRIBUTES:[{name:"a_positionCoef",size:1,type:ue},{name:"a_normalCoef",size:1,type:ue},{name:"a_radiusCoef",size:1,type:ue}],CONSTANT_DATA:[[0,1,0],[0,-1,0],[1,1,1],[1,1,1],[0,-1,0],[1,-1,-1]]}}processVisibleItem(i,n,o,a,s){const c=s.size||1,l=o.x,u=o.y,h=a.x,d=a.y,m=Y(s.color),f=h-l,p=d-u,b=a.size||1;let g=f*f+p*p,_=0,v=0;g&&(g=1/Math.sqrt(g),_=-p*g*c,v=f*g*c);const y=this.array;y[n++]=l,y[n++]=u,y[n++]=h,y[n++]=d,y[n++]=_,y[n++]=v,y[n++]=m,y[n++]=i,y[n++]=b}setUniforms(i,{gl:n,uniformLocations:o}){const{u_matrix:a,u_zoomRatio:s,u_feather:c,u_pixelRatio:l,u_correctionRatio:u,u_sizeRatio:h,u_minEdgeThickness:d,u_lengthToThicknessRatio:m}=o;n.uniformMatrix3fv(a,!1,i.matrix),n.uniform1f(s,i.zoomRatio),n.uniform1f(h,i.sizeRatio),n.uniform1f(u,i.correctionRatio),n.uniform1f(l,i.pixelRatio),n.uniform1f(c,i.antiAliasingFeather),n.uniform1f(d,i.minEdgeThickness),n.uniform1f(m,e.lengthToThicknessRatio)}}}const es=nr(),ts=` +attribute vec4 a_id; +attribute vec4 a_color; +attribute vec2 a_normal; +attribute float a_normalCoef; +attribute vec2 a_positionStart; +attribute vec2 a_positionEnd; +attribute float a_positionCoef; +attribute float a_sourceRadius; +attribute float a_targetRadius; +attribute float a_sourceRadiusCoef; +attribute float a_targetRadiusCoef; + +uniform mat3 u_matrix; +uniform float u_zoomRatio; +uniform float u_sizeRatio; +uniform float u_pixelRatio; +uniform float u_correctionRatio; +uniform float u_minEdgeThickness; +uniform float u_lengthToThicknessRatio; +uniform float u_feather; + +varying vec4 v_color; +varying vec2 v_normal; +varying float v_thickness; +varying float v_feather; + +const float bias = 255.0 / 254.0; + +void main() { + float minThickness = u_minEdgeThickness; + + vec2 normal = a_normal * a_normalCoef; + vec2 position = a_positionStart * (1.0 - a_positionCoef) + a_positionEnd * a_positionCoef; + + float normalLength = length(normal); + vec2 unitNormal = normal / normalLength; + + // These first computations are taken from edge.vert.glsl. Please read it to + // get better comments on what's happening: + float pixelsThickness = max(normalLength, minThickness * u_sizeRatio); + float webGLThickness = pixelsThickness * u_correctionRatio / u_sizeRatio; + + // Here, we move the point to leave space for the arrow heads: + // Source arrow head + float sourceRadius = a_sourceRadius * a_sourceRadiusCoef; + float sourceDirection = sign(sourceRadius); + float webGLSourceRadius = sourceDirection * sourceRadius * 2.0 * u_correctionRatio / u_sizeRatio; + float webGLSourceArrowHeadLength = webGLThickness * u_lengthToThicknessRatio * 2.0; + vec2 sourceCompensationVector = + vec2(-sourceDirection * unitNormal.y, sourceDirection * unitNormal.x) + * (webGLSourceRadius + webGLSourceArrowHeadLength); + + // Target arrow head + float targetRadius = a_targetRadius * a_targetRadiusCoef; + float targetDirection = sign(targetRadius); + float webGLTargetRadius = targetDirection * targetRadius * 2.0 * u_correctionRatio / u_sizeRatio; + float webGLTargetArrowHeadLength = webGLThickness * u_lengthToThicknessRatio * 2.0; + vec2 targetCompensationVector = + vec2(-targetDirection * unitNormal.y, targetDirection * unitNormal.x) + * (webGLTargetRadius + webGLTargetArrowHeadLength); + + // Here is the proper position of the vertex + gl_Position = vec4((u_matrix * vec3(position + unitNormal * webGLThickness + sourceCompensationVector + targetCompensationVector, 1)).xy, 0, 1); + + v_thickness = webGLThickness / u_zoomRatio; + + v_normal = unitNormal; + + v_feather = u_feather * u_correctionRatio / u_zoomRatio / u_pixelRatio * 2.0; + + #ifdef PICKING_MODE + // For picking mode, we use the ID as the color: + v_color = a_id; + #else + // For normal mode, we use the color: + v_color = a_color; + #endif + + v_color.a *= bias; +} +`,{UNSIGNED_BYTE:nn,FLOAT:Q}=WebGLRenderingContext,rs=["u_matrix","u_zoomRatio","u_sizeRatio","u_correctionRatio","u_pixelRatio","u_feather","u_minEdgeThickness","u_lengthToThicknessRatio"],on={lengthToThicknessRatio:et.lengthToThicknessRatio};function or(r){const e={...on,...r||{}};return class extends le{getDefinition(){return{VERTICES:6,VERTEX_SHADER_SOURCE:ts,FRAGMENT_SHADER_SOURCE:ir,METHOD:WebGLRenderingContext.TRIANGLES,UNIFORMS:rs,ATTRIBUTES:[{name:"a_positionStart",size:2,type:Q},{name:"a_positionEnd",size:2,type:Q},{name:"a_normal",size:2,type:Q},{name:"a_color",size:4,type:nn,normalized:!0},{name:"a_id",size:4,type:nn,normalized:!0},{name:"a_sourceRadius",size:1,type:Q},{name:"a_targetRadius",size:1,type:Q}],CONSTANT_ATTRIBUTES:[{name:"a_positionCoef",size:1,type:Q},{name:"a_normalCoef",size:1,type:Q},{name:"a_sourceRadiusCoef",size:1,type:Q},{name:"a_targetRadiusCoef",size:1,type:Q}],CONSTANT_DATA:[[0,1,-1,0],[0,-1,1,0],[1,1,0,1],[1,1,0,1],[0,-1,1,0],[1,-1,0,-1]]}}processVisibleItem(i,n,o,a,s){const c=s.size||1,l=o.x,u=o.y,h=a.x,d=a.y,m=Y(s.color),f=h-l,p=d-u,b=o.size||1,g=a.size||1;let _=f*f+p*p,v=0,y=0;_&&(_=1/Math.sqrt(_),v=-p*_*c,y=f*_*c);const C=this.array;C[n++]=l,C[n++]=u,C[n++]=h,C[n++]=d,C[n++]=v,C[n++]=y,C[n++]=m,C[n++]=i,C[n++]=b,C[n++]=g}setUniforms(i,{gl:n,uniformLocations:o}){const{u_matrix:a,u_zoomRatio:s,u_feather:c,u_pixelRatio:l,u_correctionRatio:u,u_sizeRatio:h,u_minEdgeThickness:d,u_lengthToThicknessRatio:m}=o;n.uniformMatrix3fv(a,!1,i.matrix),n.uniform1f(s,i.zoomRatio),n.uniform1f(h,i.sizeRatio),n.uniform1f(u,i.correctionRatio),n.uniform1f(l,i.pixelRatio),n.uniform1f(c,i.antiAliasingFeather),n.uniform1f(d,i.minEdgeThickness),n.uniform1f(m,e.lengthToThicknessRatio)}}}const is=or();function an(r){return er([nr(r),Ne(r)])}const sn=an();function cn(r){return er([or(r),Ne(r),Ne({...r,extremity:"source"})])}const ns=cn(),os=` +precision mediump float; + +varying vec4 v_color; + +void main(void) { + gl_FragColor = v_color; +} +`,as=` +attribute vec4 a_id; +attribute vec4 a_color; +attribute vec2 a_position; + +uniform mat3 u_matrix; + +varying vec4 v_color; + +const float bias = 255.0 / 254.0; + +void main() { + // Scale from [[-1 1] [-1 1]] to the container: + gl_Position = vec4( + (u_matrix * vec3(a_position, 1)).xy, + 0, + 1 + ); + + #ifdef PICKING_MODE + // For picking mode, we use the ID as the color: + v_color = a_id; + #else + // For normal mode, we use the color: + v_color = a_color; + #endif + + v_color.a *= bias; +} +`,{UNSIGNED_BYTE:ln,FLOAT:ss}=WebGLRenderingContext,cs=["u_matrix"];class ls extends le{getDefinition(){return{VERTICES:2,VERTEX_SHADER_SOURCE:as,FRAGMENT_SHADER_SOURCE:os,METHOD:WebGLRenderingContext.LINES,UNIFORMS:cs,ATTRIBUTES:[{name:"a_position",size:2,type:ss},{name:"a_color",size:4,type:ln,normalized:!0},{name:"a_id",size:4,type:ln,normalized:!0}]}}processVisibleItem(e,t,i,n,o){const a=this.array,s=i.x,c=i.y,l=n.x,u=n.y,h=Y(o.color);a[t++]=s,a[t++]=c,a[t++]=h,a[t++]=e,a[t++]=l,a[t++]=u,a[t++]=h,a[t++]=e}setUniforms(e,{gl:t,uniformLocations:i}){const{u_matrix:n}=i;t.uniformMatrix3fv(n,!1,e.matrix)}}const us=` +attribute vec4 a_id; +attribute vec4 a_color; +attribute vec2 a_normal; +attribute float a_normalCoef; +attribute vec2 a_positionStart; +attribute vec2 a_positionEnd; +attribute float a_positionCoef; + +uniform mat3 u_matrix; +uniform float u_sizeRatio; +uniform float u_zoomRatio; +uniform float u_pixelRatio; +uniform float u_correctionRatio; +uniform float u_minEdgeThickness; +uniform float u_feather; + +varying vec4 v_color; +varying vec2 v_normal; +varying float v_thickness; +varying float v_feather; + +const float bias = 255.0 / 254.0; + +void main() { + float minThickness = u_minEdgeThickness; + + vec2 normal = a_normal * a_normalCoef; + vec2 position = a_positionStart * (1.0 - a_positionCoef) + a_positionEnd * a_positionCoef; + + float normalLength = length(normal); + vec2 unitNormal = normal / normalLength; + + // We require edges to be at least "minThickness" pixels thick *on screen* + // (so we need to compensate the size ratio): + float pixelsThickness = max(normalLength, minThickness * u_sizeRatio); + + // Then, we need to retrieve the normalized thickness of the edge in the WebGL + // referential (in a ([0, 1], [0, 1]) space), using our "magic" correction + // ratio: + float webGLThickness = pixelsThickness * u_correctionRatio / u_sizeRatio; + + // Here is the proper position of the vertex + gl_Position = vec4((u_matrix * vec3(position + unitNormal * webGLThickness, 1)).xy, 0, 1); + + // For the fragment shader though, we need a thickness that takes the "magic" + // correction ratio into account (as in webGLThickness), but so that the + // antialiasing effect does not depend on the zoom level. So here's yet + // another thickness version: + v_thickness = webGLThickness / u_zoomRatio; + + v_normal = unitNormal; + + v_feather = u_feather * u_correctionRatio / u_zoomRatio / u_pixelRatio * 2.0; + + #ifdef PICKING_MODE + // For picking mode, we use the ID as the color: + v_color = a_id; + #else + // For normal mode, we use the color: + v_color = a_color; + #endif + + v_color.a *= bias; +} +`,{UNSIGNED_BYTE:un,FLOAT:Pe}=WebGLRenderingContext,hs=["u_matrix","u_zoomRatio","u_sizeRatio","u_correctionRatio","u_pixelRatio","u_feather","u_minEdgeThickness"];class hn extends le{getDefinition(){return{VERTICES:6,VERTEX_SHADER_SOURCE:us,FRAGMENT_SHADER_SOURCE:ir,METHOD:WebGLRenderingContext.TRIANGLES,UNIFORMS:hs,ATTRIBUTES:[{name:"a_positionStart",size:2,type:Pe},{name:"a_positionEnd",size:2,type:Pe},{name:"a_normal",size:2,type:Pe},{name:"a_color",size:4,type:un,normalized:!0},{name:"a_id",size:4,type:un,normalized:!0}],CONSTANT_ATTRIBUTES:[{name:"a_positionCoef",size:1,type:Pe},{name:"a_normalCoef",size:1,type:Pe}],CONSTANT_DATA:[[0,1],[0,-1],[1,1],[1,1],[0,-1],[1,-1]]}}processVisibleItem(e,t,i,n,o){const a=o.size||1,s=i.x,c=i.y,l=n.x,u=n.y,h=Y(o.color),d=l-s,m=u-c;let f=d*d+m*m,p=0,b=0;f&&(f=1/Math.sqrt(f),p=-m*f*a,b=d*f*a);const g=this.array;g[t++]=s,g[t++]=c,g[t++]=l,g[t++]=u,g[t++]=p,g[t++]=b,g[t++]=h,g[t++]=e}setUniforms(e,{gl:t,uniformLocations:i}){const{u_matrix:n,u_zoomRatio:o,u_feather:a,u_pixelRatio:s,u_correctionRatio:c,u_sizeRatio:l,u_minEdgeThickness:u}=i;t.uniformMatrix3fv(n,!1,e.matrix),t.uniform1f(o,e.zoomRatio),t.uniform1f(l,e.sizeRatio),t.uniform1f(c,e.correctionRatio),t.uniform1f(s,e.pixelRatio),t.uniform1f(a,e.antiAliasingFeather),t.uniform1f(u,e.minEdgeThickness)}}const ds=` +precision mediump float; + +varying vec4 v_color; + +void main(void) { + gl_FragColor = v_color; +} +`,fs=` +attribute vec4 a_id; +attribute vec4 a_color; +attribute vec2 a_normal; +attribute float a_normalCoef; +attribute vec2 a_positionStart; +attribute vec2 a_positionEnd; +attribute float a_positionCoef; + +uniform mat3 u_matrix; +uniform float u_sizeRatio; +uniform float u_correctionRatio; + +varying vec4 v_color; + +const float minThickness = 1.7; +const float bias = 255.0 / 254.0; + +void main() { + vec2 normal = a_normal * a_normalCoef; + vec2 position = a_positionStart * (1.0 - a_positionCoef) + a_positionEnd * a_positionCoef; + + // The only different here with edge.vert.glsl is that we need to handle null + // input normal vector. Apart from that, you can read edge.vert.glsl more info + // on how it works: + float normalLength = length(normal); + vec2 unitNormal = normal / normalLength; + if (normalLength <= 0.0) unitNormal = normal; + float pixelsThickness = max(normalLength, minThickness * u_sizeRatio); + float webGLThickness = pixelsThickness * u_correctionRatio / u_sizeRatio; + + gl_Position = vec4((u_matrix * vec3(position + unitNormal * webGLThickness, 1)).xy, 0, 1); + + #ifdef PICKING_MODE + // For picking mode, we use the ID as the color: + v_color = a_id; + #else + // For normal mode, we use the color: + v_color = a_color; + #endif + + v_color.a *= bias; +} +`,{UNSIGNED_BYTE:dn,FLOAT:De}=WebGLRenderingContext,gs=["u_matrix","u_sizeRatio","u_correctionRatio","u_minEdgeThickness"];class ms extends le{getDefinition(){return{VERTICES:3,VERTEX_SHADER_SOURCE:fs,FRAGMENT_SHADER_SOURCE:ds,METHOD:WebGLRenderingContext.TRIANGLES,UNIFORMS:gs,ATTRIBUTES:[{name:"a_positionStart",size:2,type:De},{name:"a_positionEnd",size:2,type:De},{name:"a_normal",size:2,type:De},{name:"a_color",size:4,type:dn,normalized:!0},{name:"a_id",size:4,type:dn,normalized:!0}],CONSTANT_ATTRIBUTES:[{name:"a_positionCoef",size:1,type:De},{name:"a_normalCoef",size:1,type:De}],CONSTANT_DATA:[[0,1],[0,-1],[1,0]]}}processVisibleItem(e,t,i,n,o){const a=o.size||1,s=i.x,c=i.y,l=n.x,u=n.y,h=Y(o.color),d=l-s,m=u-c;let f=d*d+m*m,p=0,b=0;f&&(f=1/Math.sqrt(f),p=-m*f*a,b=d*f*a);const g=this.array;g[t++]=s,g[t++]=c,g[t++]=l,g[t++]=u,g[t++]=p,g[t++]=b,g[t++]=h,g[t++]=e}setUniforms(e,{gl:t,uniformLocations:i}){const{u_matrix:n,u_sizeRatio:o,u_correctionRatio:a,u_minEdgeThickness:s}=i;t.uniformMatrix3fv(n,!1,e.matrix),t.uniform1f(o,e.sizeRatio),t.uniform1f(a,e.correctionRatio),t.uniform1f(s,e.minEdgeThickness)}}const vs=Object.freeze(Object.defineProperty({__proto__:null,AbstractEdgeProgram:Ua,AbstractNodeProgram:Ga,AbstractProgram:Zt,DEFAULT_EDGE_ARROW_HEAD_PROGRAM_OPTIONS:et,DEFAULT_EDGE_CLAMPED_PROGRAM_OPTIONS:rn,DEFAULT_EDGE_DOUBLE_CLAMPED_PROGRAM_OPTIONS:on,EdgeArrowHeadProgram:Za,EdgeArrowProgram:sn,EdgeClampedProgram:es,EdgeDoubleArrowProgram:ns,EdgeDoubleClampedProgram:is,EdgeLineProgram:ls,EdgeProgram:le,EdgeRectangleProgram:hn,EdgeTriangleProgram:ms,NodeCircleProgram:Qe,NodePointProgram:Ya,NodeProgram:Jt,Program:Qt,createEdgeArrowHeadProgram:Ne,createEdgeArrowProgram:an,createEdgeClampedProgram:nr,createEdgeCompoundProgram:er,createEdgeDoubleArrowProgram:cn,createEdgeDoubleClampedProgram:or,createNodeCompoundProgram:Ma,drawDiscNodeHover:Ki,drawDiscNodeLabel:tr,drawStraightEdgeLabel:qi,getAttributeItemsCount:$i,getAttributesItemsCount:Ze,killProgram:Kt,loadFragmentShader:Wi,loadProgram:Yi,loadVertexShader:Vi,numberToGLSLFloat:Ia},Symbol.toStringTag,{value:"Module"}));class ar extends ci.EventEmitter{constructor(){super(),this.rawEmitter=this}}const tt=1.5;class Te extends ar{constructor(){super(),this.x=.5,this.y=.5,this.angle=0,this.ratio=1,this.minRatio=null,this.maxRatio=null,this.enabledZooming=!0,this.enabledPanning=!0,this.enabledRotation=!0,this.clean=null,this.nextFrame=null,this.previousState=null,this.enabled=!0,this.previousState=this.getState()}static from(e){return new Te().setState(e)}enable(){return this.enabled=!0,this}disable(){return this.enabled=!1,this}getState(){return{x:this.x,y:this.y,angle:this.angle,ratio:this.ratio}}hasState(e){return this.x===e.x&&this.y===e.y&&this.ratio===e.ratio&&this.angle===e.angle}getPreviousState(){const e=this.previousState;return e?{x:e.x,y:e.y,angle:e.angle,ratio:e.ratio}:null}getBoundedRatio(e){let t=e;return typeof this.minRatio=="number"&&(t=Math.max(t,this.minRatio)),typeof this.maxRatio=="number"&&(t=Math.min(t,this.maxRatio)),t}validateState(e){const t={};return this.enabledPanning&&typeof e.x=="number"&&(t.x=e.x),this.enabledPanning&&typeof e.y=="number"&&(t.y=e.y),this.enabledZooming&&typeof e.ratio=="number"&&(t.ratio=this.getBoundedRatio(e.ratio)),this.enabledRotation&&typeof e.angle=="number"&&(t.angle=e.angle),this.clean?this.clean({...this.getState(),...t}):t}isAnimated(){return!!this.nextFrame}setState(e){if(!this.enabled)return this;this.previousState=this.getState();const t=this.validateState(e);return typeof t.x=="number"&&(this.x=t.x),typeof t.y=="number"&&(this.y=t.y),typeof t.ratio=="number"&&(this.ratio=t.ratio),typeof t.angle=="number"&&(this.angle=t.angle),this.hasState(this.previousState)||this.emit("updated",this.getState()),this}updateState(e){return this.setState(e(this.getState())),this}animate(e,t={},i){if(!i)return new Promise(u=>this.animate(e,t,u));if(!this.enabled)return;const n={...Gt,...t},o=this.validateState(e),a=typeof n.easing=="function"?n.easing:zt[n.easing],s=Date.now(),c=this.getState(),l=()=>{const u=(Date.now()-s)/n.duration;if(u>=1){this.nextFrame=null,this.setState(o),this.animationCallback&&(this.animationCallback.call(null),this.animationCallback=void 0);return}const h=a(u),d={};typeof o.x=="number"&&(d.x=c.x+(o.x-c.x)*h),typeof o.y=="number"&&(d.y=c.y+(o.y-c.y)*h),this.enabledRotation&&typeof o.angle=="number"&&(d.angle=c.angle+(o.angle-c.angle)*h),typeof o.ratio=="number"&&(d.ratio=c.ratio+(o.ratio-c.ratio)*h),this.setState(d),this.nextFrame=requestAnimationFrame(l)};this.nextFrame?(cancelAnimationFrame(this.nextFrame),this.animationCallback&&this.animationCallback.call(null),this.nextFrame=requestAnimationFrame(l)):l(),this.animationCallback=i}animatedZoom(e){return e?typeof e=="number"?this.animate({ratio:this.ratio/e}):this.animate({ratio:this.ratio/(e.factor||tt)},e):this.animate({ratio:this.ratio/tt})}animatedUnzoom(e){return e?typeof e=="number"?this.animate({ratio:this.ratio*e}):this.animate({ratio:this.ratio*(e.factor||tt)},e):this.animate({ratio:this.ratio*tt})}animatedReset(e){return this.animate({x:.5,y:.5,ratio:1,angle:0},e)}copy(){return Te.from(this.getState())}}const sr={hideEdgesOnMove:!1,hideLabelsOnMove:!1,renderLabels:!0,renderEdgeLabels:!1,enableEdgeEvents:!1,defaultNodeColor:"#999",defaultNodeType:"circle",defaultEdgeColor:"#ccc",defaultEdgeType:"line",labelFont:"Arial",labelSize:14,labelWeight:"normal",labelColor:{color:"#000"},edgeLabelFont:"Arial",edgeLabelSize:14,edgeLabelWeight:"normal",edgeLabelColor:{attribute:"color"},stagePadding:30,defaultDrawEdgeLabel:qi,defaultDrawNodeLabel:tr,defaultDrawNodeHover:Ki,minEdgeThickness:1.7,antiAliasingFeather:1,dragTimeout:100,draggedEventsTolerance:3,inertiaDuration:200,inertiaRatio:3,zoomDuration:250,zoomingRatio:1.7,doubleClickTimeout:300,doubleClickZoomingRatio:2.2,doubleClickZoomingDuration:200,tapMoveTolerance:10,zoomToSizeRatioFunction:Math.sqrt,itemSizesReference:"screen",autoRescale:!0,autoCenter:!0,labelDensity:1,labelGridCellSize:100,labelRenderedSizeThreshold:6,nodeReducer:null,edgeReducer:null,zIndex:!1,minCameraRatio:null,maxCameraRatio:null,enableCameraZooming:!0,enableCameraPanning:!0,enableCameraRotation:!0,cameraPanBoundaries:null,allowInvalidContainer:!1,nodeProgramClasses:{},nodeHoverProgramClasses:{},edgeProgramClasses:{}},ps={circle:Qe},_s={arrow:sn,line:hn};function cr(r){if(typeof r.labelDensity!="number"||r.labelDensity<0)throw new Error("Settings: invalid `labelDensity`. Expecting a positive number.");const{minCameraRatio:e,maxCameraRatio:t}=r;if(typeof e=="number"&&typeof t=="number"&&t{r.sigmaDefaultPrevented=!0,e.sigmaDefaultPrevented=!0}};return e}function ys(r,e){return{...J(r,e),delta:fn(r)}}const Es=2;function rt(r){const e=[];for(let t=0,i=Math.min(r.length,Es);tX(n,t)),previousTouches:e.map(n=>X(n,t)),sigmaDefaultPrevented:!1,preventSigmaDefault(){i.sigmaDefaultPrevented=!0},original:r};return i}function fn(r){if(typeof r.deltaY<"u")return r.deltaY*-3/360;if(typeof r.detail<"u")return r.detail/-9;throw new Error("Captor: could not extract delta from event.")}class gn extends ar{constructor(e,t){super(),this.container=e,this.renderer=t}}const Ts=["doubleClickTimeout","doubleClickZoomingDuration","doubleClickZoomingRatio","dragTimeout","draggedEventsTolerance","inertiaDuration","inertiaRatio","zoomDuration","zoomingRatio"].reduce((r,e)=>({...r,[e]:sr[e]}),{});class mn extends gn{constructor(e,t){super(e,t),this.enabled=!0,this.draggedEvents=0,this.downStartTime=null,this.lastMouseX=null,this.lastMouseY=null,this.isMouseDown=!1,this.isMoving=!1,this.movingTimeout=null,this.startCameraState=null,this.clicks=0,this.doubleClickTimeout=null,this.currentWheelDirection=0,this.settings=Ts,this.handleClick=this.handleClick.bind(this),this.handleRightClick=this.handleRightClick.bind(this),this.handleDown=this.handleDown.bind(this),this.handleUp=this.handleUp.bind(this),this.handleMove=this.handleMove.bind(this),this.handleWheel=this.handleWheel.bind(this),this.handleLeave=this.handleLeave.bind(this),this.handleEnter=this.handleEnter.bind(this),e.addEventListener("click",this.handleClick,{capture:!1}),e.addEventListener("contextmenu",this.handleRightClick,{capture:!1}),e.addEventListener("mousedown",this.handleDown,{capture:!1}),e.addEventListener("wheel",this.handleWheel,{capture:!1}),e.addEventListener("mouseleave",this.handleLeave,{capture:!1}),e.addEventListener("mouseenter",this.handleEnter,{capture:!1}),document.addEventListener("mousemove",this.handleMove,{capture:!1}),document.addEventListener("mouseup",this.handleUp,{capture:!1})}kill(){const e=this.container;e.removeEventListener("click",this.handleClick),e.removeEventListener("contextmenu",this.handleRightClick),e.removeEventListener("mousedown",this.handleDown),e.removeEventListener("wheel",this.handleWheel),e.removeEventListener("mouseleave",this.handleLeave),e.removeEventListener("mouseenter",this.handleEnter),document.removeEventListener("mousemove",this.handleMove),document.removeEventListener("mouseup",this.handleUp)}handleClick(e){if(this.enabled){if(this.clicks++,this.clicks===2)return this.clicks=0,typeof this.doubleClickTimeout=="number"&&(clearTimeout(this.doubleClickTimeout),this.doubleClickTimeout=null),this.handleDoubleClick(e);setTimeout(()=>{this.clicks=0,this.doubleClickTimeout=null},this.settings.doubleClickTimeout),this.draggedEvents{const s=this.draggedEvents>0;this.draggedEvents=0,s&&this.renderer.refresh()},0),this.emit("mouseup",J(e,this.container))}handleMove(e){if(!this.enabled)return;const t=J(e,this.container);if(this.emit("mousemovebody",t),(e.target===this.container||e.composedPath()[0]===this.container)&&this.emit("mousemove",t),!t.sigmaDefaultPrevented&&this.isMouseDown){this.isMoving=!0,this.draggedEvents++,typeof this.movingTimeout=="number"&&clearTimeout(this.movingTimeout),this.movingTimeout=window.setTimeout(()=>{this.movingTimeout=null,this.isMoving=!1},this.settings.dragTimeout);const i=this.renderer.getCamera(),{x:n,y:o}=X(e,this.container),a=this.renderer.viewportToFramedGraph({x:this.lastMouseX,y:this.lastMouseY}),s=this.renderer.viewportToFramedGraph({x:n,y:o}),c=a.x-s.x,l=a.y-s.y,u=i.getState(),h=u.x+c,d=u.y+l;i.setState({x:h,y:d}),this.lastMouseX=n,this.lastMouseY=o,e.preventDefault(),e.stopPropagation()}}handleLeave(e){this.emit("mouseleave",J(e,this.container))}handleEnter(e){this.emit("mouseenter",J(e,this.container))}handleWheel(e){const t=this.renderer.getCamera();if(!this.enabled||!t.enabledZooming)return;const i=fn(e);if(!i)return;const n=ys(e,this.container);if(this.emit("wheel",n),n.sigmaDefaultPrevented){e.preventDefault(),e.stopPropagation();return}const o=t.getState().ratio,a=i>0?1/this.settings.zoomingRatio:this.settings.zoomingRatio,s=t.getBoundedRatio(o*a),c=i>0?1:-1,l=Date.now();o!==s&&(e.preventDefault(),e.stopPropagation(),!(this.currentWheelDirection===c&&this.lastWheelTriggerTime&&l-this.lastWheelTriggerTime{this.currentWheelDirection=0}),this.currentWheelDirection=c,this.lastWheelTriggerTime=l))}setSettings(e){this.settings=e}}const Rs=["dragTimeout","inertiaDuration","inertiaRatio","doubleClickTimeout","doubleClickZoomingRatio","doubleClickZoomingDuration","tapMoveTolerance"].reduce((r,e)=>({...r,[e]:sr[e]}),{});class Cs extends gn{constructor(e,t){super(e,t),this.enabled=!0,this.isMoving=!1,this.hasMoved=!1,this.touchMode=0,this.startTouchesPositions=[],this.lastTouches=[],this.lastTap=null,this.settings=Rs,this.handleStart=this.handleStart.bind(this),this.handleLeave=this.handleLeave.bind(this),this.handleMove=this.handleMove.bind(this),e.addEventListener("touchstart",this.handleStart,{capture:!1}),e.addEventListener("touchcancel",this.handleLeave,{capture:!1}),document.addEventListener("touchend",this.handleLeave,{capture:!1,passive:!1}),document.addEventListener("touchmove",this.handleMove,{capture:!1,passive:!1})}kill(){const e=this.container;e.removeEventListener("touchstart",this.handleStart),e.removeEventListener("touchcancel",this.handleLeave),document.removeEventListener("touchend",this.handleLeave),document.removeEventListener("touchmove",this.handleMove)}getDimensions(){return{width:this.container.offsetWidth,height:this.container.offsetHeight}}handleStart(e){if(!this.enabled)return;e.preventDefault();const t=rt(e.touches);if(this.touchMode=t.length,this.startCameraState=this.renderer.getCamera().getState(),this.startTouchesPositions=t.map(i=>X(i,this.container)),this.touchMode===2){const[{x:i,y:n},{x:o,y:a}]=this.startTouchesPositions;this.startTouchesAngle=Math.atan2(a-n,o-i),this.startTouchesDistance=Math.sqrt(Math.pow(o-i,2)+Math.pow(a-n,2))}this.emit("touchdown",ke(e,this.lastTouches,this.container)),this.lastTouches=t,this.lastTouchesPositions=this.startTouchesPositions}handleLeave(e){if(!(!this.enabled||!this.startTouchesPositions.length)){switch(e.cancelable&&e.preventDefault(),this.movingTimeout&&(this.isMoving=!1,clearTimeout(this.movingTimeout)),this.touchMode){case 2:if(e.touches.length===1){this.handleStart(e),e.preventDefault();break}case 1:if(this.isMoving){const t=this.renderer.getCamera(),i=t.getState(),n=t.getPreviousState()||{x:0,y:0};t.animate({x:i.x+this.settings.inertiaRatio*(i.x-n.x),y:i.y+this.settings.inertiaRatio*(i.y-n.y)},{duration:this.settings.inertiaDuration,easing:"quadraticOut"})}this.hasMoved=!1,this.isMoving=!1,this.touchMode=0;break}if(this.emit("touchup",ke(e,this.lastTouches,this.container)),!e.touches.length){const t=X(this.lastTouches[0],this.container),i=this.startTouchesPositions[0],n=(t.x-i.x)**2+(t.y-i.y)**2;if(!e.touches.length&&nX(l,this.container)),n=this.lastTouches;this.lastTouches=t,this.lastTouchesPositions=i;const o=ke(e,n,this.container);if(this.emit("touchmove",o),o.sigmaDefaultPrevented||(this.hasMoved||(this.hasMoved=i.some((l,u)=>{const h=this.startTouchesPositions[u];return h&&(l.x!==h.x||l.y!==h.y)})),!this.hasMoved))return;this.isMoving=!0,this.movingTimeout&&clearTimeout(this.movingTimeout),this.movingTimeout=window.setTimeout(()=>{this.isMoving=!1},this.settings.dragTimeout);const a=this.renderer.getCamera(),s=this.startCameraState,c=this.renderer.getSetting("stagePadding");switch(this.touchMode){case 1:{const{x:l,y:u}=this.renderer.viewportToFramedGraph((this.startTouchesPositions||[])[0]),{x:h,y:d}=this.renderer.viewportToFramedGraph(i[0]);a.setState({x:s.x+l-h,y:s.y+u-d});break}case 2:{const l={x:.5,y:.5,angle:0,ratio:1},{x:u,y:h}=i[0],{x:d,y:m}=i[1],f=Math.atan2(m-h,d-u)-this.startTouchesAngle,p=Math.hypot(m-h,d-u)/this.startTouchesDistance,b=a.getBoundedRatio(s.ratio/p);l.ratio=b,l.angle=s.angle+f;const g=this.getDimensions(),_=this.renderer.viewportToFramedGraph((this.startTouchesPositions||[])[0],{cameraState:s}),v=Math.min(g.width,g.height)-2*c,y=v/g.width,C=v/g.height,S=b/v;let w=u-v/2/y,P=h-v/2/C;[w,P]=[w*Math.cos(-l.angle)-P*Math.sin(-l.angle),P*Math.cos(-l.angle)+w*Math.sin(-l.angle)],l.x=_.x-w*S,l.y=_.y+P*S,a.setState(l);break}}}setSettings(e){this.settings=e}}class vn{constructor(e,t){this.key=e,this.size=t}static compare(e,t){return e.size>t.size?-1:e.sizet.key?1:-1}}class pn{constructor(){this.width=0,this.height=0,this.cellSize=0,this.columns=0,this.rows=0,this.cells={}}resizeAndClear(e,t){this.width=e.width,this.height=e.height,this.cellSize=t,this.columns=Math.ceil(e.width/t),this.rows=Math.ceil(e.height/t),this.cells={}}getIndex(e){const t=Math.floor(e.x/this.cellSize);return Math.floor(e.y/this.cellSize)*this.columns+t}add(e,t,i){const n=new vn(e,t),o=this.getIndex(i);let a=this.cells[o];a||(a=[],this.cells[o]=a),a.push(n)}organize(){for(const e in this.cells)this.cells[e].sort(vn.compare)}getLabelsToDisplay(e,t){const i=this.cellSize*this.cellSize,o=i/e/e*t/i,a=Math.ceil(o),s=[];for(const c in this.cells){const l=this.cells[c];for(let u=0;u{(c===t||l===t||i.has(c)||i.has(l)||n.has(c)&&n.has(l))&&o.push(a)}),o}const _n=150,bn=50,ee=Object.prototype.hasOwnProperty;function As(r,e,t){if(!ee.call(t,"x")||!ee.call(t,"y"))throw new Error(`Sigma: could not find a valid position (x, y) for node "${e}". All your nodes must have a number "x" and "y". Maybe your forgot to apply a layout or your "nodeReducer" is not returning the correct data?`);return t.color||(t.color=r.defaultNodeColor),!t.label&&t.label!==""&&(t.label=null),t.label!==void 0&&t.label!==null?t.label=""+t.label:t.label=null,t.size||(t.size=2),ee.call(t,"hidden")||(t.hidden=!1),ee.call(t,"highlighted")||(t.highlighted=!1),ee.call(t,"forceLabel")||(t.forceLabel=!1),(!t.type||t.type==="")&&(t.type=r.defaultNodeType),t.zIndex||(t.zIndex=0),t}function Ss(r,e,t){return t.color||(t.color=r.defaultEdgeColor),t.label||(t.label=""),t.size||(t.size=.5),ee.call(t,"hidden")||(t.hidden=!1),ee.call(t,"forceLabel")||(t.forceLabel=!1),(!t.type||t.type==="")&&(t.type=r.defaultEdgeType),t.zIndex||(t.zIndex=0),t}let yn=class extends ar{constructor(e,t,i={}){if(super(),this.elements={},this.canvasContexts={},this.webGLContexts={},this.pickingLayers=new Set,this.textures={},this.frameBuffers={},this.activeListeners={},this.labelGrid=new pn,this.nodeDataCache={},this.edgeDataCache={},this.nodeProgramIndex={},this.edgeProgramIndex={},this.nodesWithForcedLabels=new Set,this.edgesWithForcedLabels=new Set,this.nodeExtent={x:[0,1],y:[0,1]},this.nodeZExtent=[1/0,-1/0],this.edgeZExtent=[1/0,-1/0],this.matrix=V(),this.invMatrix=V(),this.correctionRatio=1,this.customBBox=null,this.normalizationFunction=qt({x:[0,1],y:[0,1]}),this.graphToViewportRatio=1,this.itemIDsIndex={},this.nodeIndices={},this.edgeIndices={},this.width=0,this.height=0,this.pixelRatio=Yt(),this.pickingDownSizingRatio=2*this.pixelRatio,this.displayedNodeLabels=new Set,this.displayedEdgeLabels=new Set,this.highlightedNodes=new Set,this.hoveredNode=null,this.hoveredEdge=null,this.renderFrame=null,this.renderHighlightedNodesFrame=null,this.needToProcess=!1,this.checkEdgesEventsFrame=null,this.nodePrograms={},this.nodeHoverPrograms={},this.edgePrograms={},this.settings=bs(i),cr(this.settings),Bi(e),!(t instanceof HTMLElement))throw new Error("Sigma: container should be an html element.");this.graph=e,this.container=t,this.createWebGLContext("edges",{picking:i.enableEdgeEvents}),this.createCanvasContext("edgeLabels"),this.createWebGLContext("nodes",{picking:!0}),this.createCanvasContext("labels"),this.createCanvasContext("hovers"),this.createWebGLContext("hoverNodes"),this.createCanvasContext("mouse",{style:{touchAction:"none",userSelect:"none"}}),this.resize();for(const n in this.settings.nodeProgramClasses)this.registerNodeProgram(n,this.settings.nodeProgramClasses[n],this.settings.nodeHoverProgramClasses[n]);for(const n in this.settings.edgeProgramClasses)this.registerEdgeProgram(n,this.settings.edgeProgramClasses[n]);this.camera=new Te,this.bindCameraHandlers(),this.mouseCaptor=new mn(this.elements.mouse,this),this.mouseCaptor.setSettings(this.settings),this.touchCaptor=new Cs(this.elements.mouse,this),this.touchCaptor.setSettings(this.settings),this.bindEventHandlers(),this.bindGraphHandlers(),this.handleSettingsUpdate(),this.refresh()}registerNodeProgram(e,t,i){return this.nodePrograms[e]&&this.nodePrograms[e].kill(),this.nodeHoverPrograms[e]&&this.nodeHoverPrograms[e].kill(),this.nodePrograms[e]=new t(this.webGLContexts.nodes,this.frameBuffers.nodes,this),this.nodeHoverPrograms[e]=new(i||t)(this.webGLContexts.hoverNodes,null,this),this}registerEdgeProgram(e,t){return this.edgePrograms[e]&&this.edgePrograms[e].kill(),this.edgePrograms[e]=new t(this.webGLContexts.edges,this.frameBuffers.edges,this),this}unregisterNodeProgram(e){if(this.nodePrograms[e]){const{[e]:t,...i}=this.nodePrograms;t.kill(),this.nodePrograms=i}if(this.nodeHoverPrograms[e]){const{[e]:t,...i}=this.nodeHoverPrograms;t.kill(),this.nodePrograms=i}return this}unregisterEdgeProgram(e){if(this.edgePrograms[e]){const{[e]:t,...i}=this.edgePrograms;t.kill(),this.edgePrograms=i}return this}resetWebGLTexture(e){const t=this.webGLContexts[e],i=this.frameBuffers[e],n=this.textures[e];n&&t.deleteTexture(n);const o=t.createTexture();return t.bindFramebuffer(t.FRAMEBUFFER,i),t.bindTexture(t.TEXTURE_2D,o),t.texImage2D(t.TEXTURE_2D,0,t.RGBA,this.width,this.height,0,t.RGBA,t.UNSIGNED_BYTE,null),t.framebufferTexture2D(t.FRAMEBUFFER,t.COLOR_ATTACHMENT0,t.TEXTURE_2D,o,0),this.textures[e]=o,this}bindCameraHandlers(){return this.activeListeners.camera=()=>{this.scheduleRender()},this.camera.on("updated",this.activeListeners.camera),this}unbindCameraHandlers(){return this.camera.removeListener("updated",this.activeListeners.camera),this}getNodeAtPosition(e){const{x:t,y:i}=e,n=$t(this.webGLContexts.nodes,this.frameBuffers.nodes,t,i,this.pixelRatio,this.pickingDownSizingRatio),o=Ht(...n),a=this.itemIDsIndex[o];return a&&a.type==="node"?a.id:null}bindEventHandlers(){this.activeListeners.handleResize=()=>{this.scheduleRefresh()},window.addEventListener("resize",this.activeListeners.handleResize),this.activeListeners.handleMove=t=>{const i=Oe(t),n={event:i,preventSigmaDefault(){i.preventSigmaDefault()}},o=this.getNodeAtPosition(i);if(o&&this.hoveredNode!==o&&!this.nodeDataCache[o].hidden){this.hoveredNode&&this.emit("leaveNode",{...n,node:this.hoveredNode}),this.hoveredNode=o,this.emit("enterNode",{...n,node:o}),this.scheduleHighlightedNodesRender();return}if(this.hoveredNode&&this.getNodeAtPosition(i)!==this.hoveredNode){const a=this.hoveredNode;this.hoveredNode=null,this.emit("leaveNode",{...n,node:a}),this.scheduleHighlightedNodesRender();return}if(this.settings.enableEdgeEvents){const a=this.hoveredNode?null:this.getEdgeAtPoint(n.event.x,n.event.y);a!==this.hoveredEdge&&(this.hoveredEdge&&this.emit("leaveEdge",{...n,edge:this.hoveredEdge}),a&&this.emit("enterEdge",{...n,edge:a}),this.hoveredEdge=a)}},this.activeListeners.handleMoveBody=t=>{const i=Oe(t);this.emit("moveBody",{event:i,preventSigmaDefault(){i.preventSigmaDefault()}})},this.activeListeners.handleLeave=t=>{const i=Oe(t),n={event:i,preventSigmaDefault(){i.preventSigmaDefault()}};this.hoveredNode&&(this.emit("leaveNode",{...n,node:this.hoveredNode}),this.scheduleHighlightedNodesRender()),this.settings.enableEdgeEvents&&this.hoveredEdge&&(this.emit("leaveEdge",{...n,edge:this.hoveredEdge}),this.scheduleHighlightedNodesRender()),this.emit("leaveStage",{...n})},this.activeListeners.handleEnter=t=>{const i=Oe(t),n={event:i,preventSigmaDefault(){i.preventSigmaDefault()}};this.emit("enterStage",{...n})};const e=t=>i=>{const n=Oe(i),o={event:n,preventSigmaDefault:()=>{n.preventSigmaDefault()}},a=this.getNodeAtPosition(n);if(a)return this.emit(`${t}Node`,{...o,node:a});if(this.settings.enableEdgeEvents){const s=this.getEdgeAtPoint(n.x,n.y);if(s)return this.emit(`${t}Edge`,{...o,edge:s})}return this.emit(`${t}Stage`,o)};return this.activeListeners.handleClick=e("click"),this.activeListeners.handleRightClick=e("rightClick"),this.activeListeners.handleDoubleClick=e("doubleClick"),this.activeListeners.handleWheel=e("wheel"),this.activeListeners.handleDown=e("down"),this.activeListeners.handleUp=e("up"),this.mouseCaptor.on("mousemove",this.activeListeners.handleMove),this.mouseCaptor.on("mousemovebody",this.activeListeners.handleMoveBody),this.mouseCaptor.on("click",this.activeListeners.handleClick),this.mouseCaptor.on("rightClick",this.activeListeners.handleRightClick),this.mouseCaptor.on("doubleClick",this.activeListeners.handleDoubleClick),this.mouseCaptor.on("wheel",this.activeListeners.handleWheel),this.mouseCaptor.on("mousedown",this.activeListeners.handleDown),this.mouseCaptor.on("mouseup",this.activeListeners.handleUp),this.mouseCaptor.on("mouseleave",this.activeListeners.handleLeave),this.mouseCaptor.on("mouseenter",this.activeListeners.handleEnter),this.touchCaptor.on("touchdown",this.activeListeners.handleDown),this.touchCaptor.on("touchdown",this.activeListeners.handleMove),this.touchCaptor.on("touchup",this.activeListeners.handleUp),this.touchCaptor.on("touchmove",this.activeListeners.handleMove),this.touchCaptor.on("tap",this.activeListeners.handleClick),this.touchCaptor.on("doubletap",this.activeListeners.handleDoubleClick),this.touchCaptor.on("touchmove",this.activeListeners.handleMoveBody),this}bindGraphHandlers(){const e=this.graph,t=new Set(["x","y","zIndex","type"]);return this.activeListeners.eachNodeAttributesUpdatedGraphUpdate=i=>{var a;const n=(a=i.hints)==null?void 0:a.attributes;this.graph.forEachNode(s=>this.updateNode(s));const o=!n||n.some(s=>t.has(s));this.refresh({partialGraph:{nodes:e.nodes()},skipIndexation:!o,schedule:!0})},this.activeListeners.eachEdgeAttributesUpdatedGraphUpdate=i=>{var a;const n=(a=i.hints)==null?void 0:a.attributes;this.graph.forEachEdge(s=>this.updateEdge(s));const o=n&&["zIndex","type"].some(s=>n==null?void 0:n.includes(s));this.refresh({partialGraph:{edges:e.edges()},skipIndexation:!o,schedule:!0})},this.activeListeners.addNodeGraphUpdate=i=>{const n=i.key;this.addNode(n),this.refresh({partialGraph:{nodes:[n]},skipIndexation:!1,schedule:!0})},this.activeListeners.updateNodeGraphUpdate=i=>{const n=i.key;this.refresh({partialGraph:{nodes:[n]},skipIndexation:!1,schedule:!0})},this.activeListeners.dropNodeGraphUpdate=i=>{const n=i.key;this.removeNode(n),this.refresh({schedule:!0})},this.activeListeners.addEdgeGraphUpdate=i=>{const n=i.key;this.addEdge(n),this.refresh({partialGraph:{edges:[n]},schedule:!0})},this.activeListeners.updateEdgeGraphUpdate=i=>{const n=i.key;this.refresh({partialGraph:{edges:[n]},skipIndexation:!1,schedule:!0})},this.activeListeners.dropEdgeGraphUpdate=i=>{const n=i.key;this.removeEdge(n),this.refresh({schedule:!0})},this.activeListeners.clearEdgesGraphUpdate=()=>{this.clearEdgeState(),this.clearEdgeIndices(),this.refresh({schedule:!0})},this.activeListeners.clearGraphUpdate=()=>{this.clearEdgeState(),this.clearNodeState(),this.clearEdgeIndices(),this.clearNodeIndices(),this.refresh({schedule:!0})},e.on("nodeAdded",this.activeListeners.addNodeGraphUpdate),e.on("nodeDropped",this.activeListeners.dropNodeGraphUpdate),e.on("nodeAttributesUpdated",this.activeListeners.updateNodeGraphUpdate),e.on("eachNodeAttributesUpdated",this.activeListeners.eachNodeAttributesUpdatedGraphUpdate),e.on("edgeAdded",this.activeListeners.addEdgeGraphUpdate),e.on("edgeDropped",this.activeListeners.dropEdgeGraphUpdate),e.on("edgeAttributesUpdated",this.activeListeners.updateEdgeGraphUpdate),e.on("eachEdgeAttributesUpdated",this.activeListeners.eachEdgeAttributesUpdatedGraphUpdate),e.on("edgesCleared",this.activeListeners.clearEdgesGraphUpdate),e.on("cleared",this.activeListeners.clearGraphUpdate),this}unbindGraphHandlers(){const e=this.graph;e.removeListener("nodeAdded",this.activeListeners.addNodeGraphUpdate),e.removeListener("nodeDropped",this.activeListeners.dropNodeGraphUpdate),e.removeListener("nodeAttributesUpdated",this.activeListeners.updateNodeGraphUpdate),e.removeListener("eachNodeAttributesUpdated",this.activeListeners.eachNodeAttributesUpdatedGraphUpdate),e.removeListener("edgeAdded",this.activeListeners.addEdgeGraphUpdate),e.removeListener("edgeDropped",this.activeListeners.dropEdgeGraphUpdate),e.removeListener("edgeAttributesUpdated",this.activeListeners.updateEdgeGraphUpdate),e.removeListener("eachEdgeAttributesUpdated",this.activeListeners.eachEdgeAttributesUpdatedGraphUpdate),e.removeListener("edgesCleared",this.activeListeners.clearEdgesGraphUpdate),e.removeListener("cleared",this.activeListeners.clearGraphUpdate)}getEdgeAtPoint(e,t){const i=$t(this.webGLContexts.edges,this.frameBuffers.edges,e,t,this.pixelRatio,this.pickingDownSizingRatio),n=Ht(...i),o=this.itemIDsIndex[n];return o&&o.type==="edge"?o.id:null}process(){this.emit("beforeProcess");const e=this.graph,t=this.settings,i=this.getDimensions();if(this.nodeExtent=Ui(this.graph),!this.settings.autoRescale){const{width:f,height:p}=i,{x:b,y:g}=this.nodeExtent;this.nodeExtent={x:[(b[0]+b[1])/2-f/2,(b[0]+b[1])/2+f/2],y:[(g[0]+g[1])/2-p/2,(g[0]+g[1])/2+p/2]}}this.normalizationFunction=qt(this.customBBox||this.nodeExtent);const n=new Te,o=Ee(n.getState(),i,this.getGraphDimensions(),this.getStagePadding());this.labelGrid.resizeAndClear(i,t.labelGridCellSize);const a={},s={},c={},l={};let u=1,h=e.nodes();for(let f=0,p=h.length;fthis.nodeDataCache[f].zIndex,h));for(let f=0,p=h.length;fthis.edgeDataCache[f].zIndex,m));for(const f in this.edgePrograms){if(!ee.call(this.edgePrograms,f))throw new Error(`Sigma: could not find a suitable program for edge type "${f}"!`);this.edgePrograms[f].reallocate(d[f]||0),d[f]=0}for(let f=0,p=m.length;fthis.cleanCameraState(i,t.cameraPanBoundaries&&typeof t.cameraPanBoundaries=="object"?t.cameraPanBoundaries:{}):this.camera.clean=null,this.camera.setState(this.camera.validateState(this.camera.getState())),e){if(e.edgeProgramClasses!==t.edgeProgramClasses){for(const i in t.edgeProgramClasses)t.edgeProgramClasses[i]!==e.edgeProgramClasses[i]&&this.registerEdgeProgram(i,t.edgeProgramClasses[i]);for(const i in e.edgeProgramClasses)t.edgeProgramClasses[i]||this.unregisterEdgeProgram(i)}if(e.nodeProgramClasses!==t.nodeProgramClasses||e.nodeHoverProgramClasses!==t.nodeHoverProgramClasses){for(const i in t.nodeProgramClasses)(t.nodeProgramClasses[i]!==e.nodeProgramClasses[i]||t.nodeHoverProgramClasses[i]!==e.nodeHoverProgramClasses[i])&&this.registerNodeProgram(i,t.nodeProgramClasses[i],t.nodeHoverProgramClasses[i]);for(const i in e.nodeProgramClasses)t.nodeProgramClasses[i]||this.unregisterNodeProgram(i)}}return this.mouseCaptor.setSettings(this.settings),this.touchCaptor.setSettings(this.settings),this}cleanCameraState(e,{tolerance:t=0,boundaries:i}={}){const n={...e},{x:[o,a],y:[s,c]}=i||this.nodeExtent,l=[this.graphToViewport({x:o,y:s},{cameraState:e}),this.graphToViewport({x:a,y:s},{cameraState:e}),this.graphToViewport({x:o,y:c},{cameraState:e}),this.graphToViewport({x:a,y:c},{cameraState:e})];let u=1/0,h=-1/0,d=1/0,m=-1/0;l.forEach(({x:y,y:C})=>{u=Math.min(u,y),h=Math.max(h,y),d=Math.min(d,C),m=Math.max(m,C)});const f=h-u,p=m-d,{width:b,height:g}=this.getDimensions();let _=0,v=0;if(f>=b?ht&&(_=u-t):h>b+t?_=h-(b+t):u<-t&&(_=u+t),p>=g?mt&&(v=d-t):m>g+t?v=m-(g+t):d<-t&&(v=d+t),_||v){const y=this.viewportToFramedGraph({x:0,y:0},{cameraState:e}),C=this.viewportToFramedGraph({x:_,y:v},{cameraState:e});_=C.x-y.x,v=C.y-y.y,n.x+=_,n.y+=v}return n}renderLabels(){if(!this.settings.renderLabels)return this;const e=this.camera.getState(),t=this.labelGrid.getLabelsToDisplay(e.ratio,this.settings.labelDensity);Wt(t,this.nodesWithForcedLabels),this.displayedNodeLabels=new Set;const i=this.canvasContexts.labels;for(let n=0,o=t.length;nthis.width+_n||l<-bn||l>this.height+bn)continue;this.displayedNodeLabels.add(a);const{defaultDrawNodeLabel:h}=this.settings,d=this.nodePrograms[s.type];((d==null?void 0:d.drawLabel)||h)(i,{key:a,...s,size:u,x:c,y:l},this.settings)}return this}renderEdgeLabels(){if(!this.settings.renderEdgeLabels)return this;const e=this.canvasContexts.edgeLabels;e.clearRect(0,0,this.width,this.height);const t=ws({graph:this.graph,hoveredNode:this.hoveredNode,displayedNodeLabels:this.displayedNodeLabels,highlightedNodes:this.highlightedNodes});Wt(t,this.edgesWithForcedLabels);const i=new Set;for(let n=0,o=t.length;n{const s=this.nodeDataCache[a],{x:c,y:l}=this.framedGraphToViewport(s),u=this.scaleSize(s.size),{defaultDrawNodeHover:h}=this.settings,d=this.nodePrograms[s.type];((d==null?void 0:d.drawHover)||h)(e,{key:a,...s,size:u,x:c,y:l},this.settings)},i=[];this.hoveredNode&&!this.nodeDataCache[this.hoveredNode].hidden&&i.push(this.hoveredNode),this.highlightedNodes.forEach(a=>{a!==this.hoveredNode&&i.push(a)}),i.forEach(a=>t(a));const n={};i.forEach(a=>{const s=this.nodeDataCache[a].type;n[s]=(n[s]||0)+1});for(const a in this.nodeHoverPrograms)this.nodeHoverPrograms[a].reallocate(n[a]||0),n[a]=0;i.forEach(a=>{const s=this.nodeDataCache[a];this.nodeHoverPrograms[s.type].process(0,n[s.type]++,s)}),this.webGLContexts.hoverNodes.clear(this.webGLContexts.hoverNodes.COLOR_BUFFER_BIT);const o=this.getRenderParams();for(const a in this.nodeHoverPrograms)this.nodeHoverPrograms[a].render(o)}scheduleHighlightedNodesRender(){this.renderHighlightedNodesFrame||this.renderFrame||(this.renderHighlightedNodesFrame=requestAnimationFrame(()=>{this.renderHighlightedNodesFrame=null,this.renderHighlightedNodes(),this.renderEdgeLabels()}))}render(){this.emit("beforeRender");const e=()=>(this.emit("afterRender"),this);if(this.renderFrame&&(cancelAnimationFrame(this.renderFrame),this.renderFrame=null),this.resize(),this.needToProcess&&this.process(),this.needToProcess=!1,this.clear(),this.pickingLayers.forEach(l=>this.resetWebGLTexture(l)),!this.graph.order)return e();const t=this.mouseCaptor,i=this.camera.isAnimated()||t.isMoving||t.draggedEvents||t.currentWheelDirection,n=this.camera.getState(),o=this.getDimensions(),a=this.getGraphDimensions(),s=this.getStagePadding();this.matrix=Ee(n,o,a,s),this.invMatrix=Ee(n,o,a,s,!0),this.correctionRatio=zi(this.matrix,n,o),this.graphToViewportRatio=this.getGraphToViewportRatio();const c=this.getRenderParams();for(const l in this.nodePrograms)this.nodePrograms[l].render(c);if(!this.settings.hideEdgesOnMove||!i)for(const l in this.edgePrograms)this.edgePrograms[l].render(c);return this.settings.hideLabelsOnMove&&i||(this.renderLabels(),this.renderEdgeLabels(),this.renderHighlightedNodes()),e()}addNode(e){let t=Object.assign({},this.graph.getNodeAttributes(e));this.settings.nodeReducer&&(t=this.settings.nodeReducer(e,t));const i=As(this.settings,e,t);this.nodeDataCache[e]=i,this.nodesWithForcedLabels.delete(e),i.forceLabel&&!i.hidden&&this.nodesWithForcedLabels.add(e),this.highlightedNodes.delete(e),i.highlighted&&!i.hidden&&this.highlightedNodes.add(e),this.settings.zIndex&&(i.zIndexthis.nodeZExtent[1]&&(this.nodeZExtent[1]=i.zIndex))}updateNode(e){this.addNode(e);const t=this.nodeDataCache[e];this.normalizationFunction.applyTo(t)}removeNode(e){delete this.nodeDataCache[e],delete this.nodeProgramIndex[e],this.highlightedNodes.delete(e),this.hoveredNode===e&&(this.hoveredNode=null),this.nodesWithForcedLabels.delete(e)}addEdge(e){let t=Object.assign({},this.graph.getEdgeAttributes(e));this.settings.edgeReducer&&(t=this.settings.edgeReducer(e,t));const i=Ss(this.settings,e,t);this.edgeDataCache[e]=i,this.edgesWithForcedLabels.delete(e),i.forceLabel&&!i.hidden&&this.edgesWithForcedLabels.add(e),this.settings.zIndex&&(i.zIndexthis.edgeZExtent[1]&&(this.edgeZExtent[1]=i.zIndex))}updateEdge(e){this.addEdge(e)}removeEdge(e){delete this.edgeDataCache[e],delete this.edgeProgramIndex[e],this.hoveredEdge===e&&(this.hoveredEdge=null),this.edgesWithForcedLabels.delete(e)}clearNodeIndices(){this.labelGrid=new pn,this.nodeExtent={x:[0,1],y:[0,1]},this.nodeDataCache={},this.edgeProgramIndex={},this.nodesWithForcedLabels=new Set,this.nodeZExtent=[1/0,-1/0]}clearEdgeIndices(){this.edgeDataCache={},this.edgeProgramIndex={},this.edgesWithForcedLabels=new Set,this.edgeZExtent=[1/0,-1/0]}clearIndices(){this.clearEdgeIndices(),this.clearNodeIndices()}clearNodeState(){this.displayedNodeLabels=new Set,this.highlightedNodes=new Set,this.hoveredNode=null}clearEdgeState(){this.displayedEdgeLabels=new Set,this.highlightedNodes=new Set,this.hoveredEdge=null}clearState(){this.clearEdgeState(),this.clearNodeState()}addNodeToProgram(e,t,i){const n=this.nodeDataCache[e],o=this.nodePrograms[n.type];if(!o)throw new Error(`Sigma: could not find a suitable program for node type "${n.type}"!`);o.process(t,i,n),this.nodeProgramIndex[e]=i}addEdgeToProgram(e,t,i){const n=this.edgeDataCache[e],o=this.edgePrograms[n.type];if(!o)throw new Error(`Sigma: could not find a suitable program for edge type "${n.type}"!`);const a=this.graph.extremities(e),s=this.nodeDataCache[a[0]],c=this.nodeDataCache[a[1]];o.process(t,i,s,c,n),this.edgeProgramIndex[e]=i}getRenderParams(){return{matrix:this.matrix,invMatrix:this.invMatrix,width:this.width,height:this.height,pixelRatio:this.pixelRatio,zoomRatio:this.camera.ratio,cameraAngle:this.camera.angle,sizeRatio:1/this.scaleSize(),correctionRatio:this.correctionRatio,downSizingRatio:this.pickingDownSizingRatio,minEdgeThickness:this.settings.minEdgeThickness,antiAliasingFeather:this.settings.antiAliasingFeather}}getStagePadding(){const{stagePadding:e,autoRescale:t}=this.settings;return t&&e||0}createLayer(e,t,i={}){if(this.elements[e])throw new Error(`Sigma: a layer named "${e}" already exists`);const n=Hi(t,{position:"absolute"},{class:`sigma-${e}`});return i.style&&Object.assign(n.style,i.style),this.elements[e]=n,"beforeLayer"in i&&i.beforeLayer?this.elements[i.beforeLayer].before(n):"afterLayer"in i&&i.afterLayer?this.elements[i.afterLayer].after(n):this.container.appendChild(n),n}createCanvas(e,t={}){return this.createLayer(e,"canvas",t)}createCanvasContext(e,t={}){const i=this.createCanvas(e,t),n={preserveDrawingBuffer:!1,antialias:!1};return this.canvasContexts[e]=i.getContext("2d",n),this}createWebGLContext(e,t={}){const i=(t==null?void 0:t.canvas)||this.createCanvas(e,t);t.hidden&&i.remove();const n={preserveDrawingBuffer:!1,antialias:!1,...t};let o;o=i.getContext("webgl2",n),o||(o=i.getContext("webgl",n)),o||(o=i.getContext("experimental-webgl",n));const a=o;if(this.webGLContexts[e]=a,a.blendFunc(a.ONE,a.ONE_MINUS_SRC_ALPHA),t.picking){this.pickingLayers.add(e);const s=a.createFramebuffer();if(!s)throw new Error(`Sigma: cannot create a new frame buffer for layer ${e}`);this.frameBuffers[e]=s}return a}killLayer(e){var i;const t=this.elements[e];if(!t)throw new Error(`Sigma: cannot kill layer ${e}, which does not exist`);return this.webGLContexts[e]?((i=this.webGLContexts[e].getExtension("WEBGL_lose_context"))==null||i.loseContext(),delete this.webGLContexts[e]):this.canvasContexts[e]&&delete this.canvasContexts[e],t.remove(),delete this.elements[e],this}getCamera(){return this.camera}setCamera(e){this.unbindCameraHandlers(),this.camera=e,this.bindCameraHandlers()}getContainer(){return this.container}getGraph(){return this.graph}setGraph(e){e!==this.graph&&(this.hoveredNode&&!e.hasNode(this.hoveredNode)&&(this.hoveredNode=null),this.hoveredEdge&&!e.hasEdge(this.hoveredEdge)&&(this.hoveredEdge=null),this.unbindGraphHandlers(),this.checkEdgesEventsFrame!==null&&(cancelAnimationFrame(this.checkEdgesEventsFrame),this.checkEdgesEventsFrame=null),this.graph=e,this.bindGraphHandlers(),this.refresh())}getMouseCaptor(){return this.mouseCaptor}getTouchCaptor(){return this.touchCaptor}getDimensions(){return{width:this.width,height:this.height}}getGraphDimensions(){const e=this.customBBox||this.nodeExtent;return{width:e.x[1]-e.x[0]||1,height:e.y[1]-e.y[0]||1}}getNodeDisplayData(e){const t=this.nodeDataCache[e];return t?Object.assign({},t):void 0}getEdgeDisplayData(e){const t=this.edgeDataCache[e];return t?Object.assign({},t):void 0}getNodeDisplayedLabels(){return new Set(this.displayedNodeLabels)}getEdgeDisplayedLabels(){return new Set(this.displayedEdgeLabels)}getSettings(){return{...this.settings}}getSetting(e){return this.settings[e]}setSetting(e,t){const i={...this.settings};return this.settings[e]=t,cr(this.settings),this.handleSettingsUpdate(i),this.scheduleRefresh(),this}updateSetting(e,t){return this.setSetting(e,t(this.settings[e])),this}setSettings(e){const t={...this.settings};return this.settings={...this.settings,...e},cr(this.settings),this.handleSettingsUpdate(t),this.scheduleRefresh(),this}resize(e){const t=this.width,i=this.height;if(this.width=this.container.offsetWidth,this.height=this.container.offsetHeight,this.pixelRatio=Yt(),this.width===0)if(this.settings.allowInvalidContainer)this.width=1;else throw new Error("Sigma: Container has no width. You can set the allowInvalidContainer setting to true to stop seeing this error.");if(this.height===0)if(this.settings.allowInvalidContainer)this.height=1;else throw new Error("Sigma: Container has no height. You can set the allowInvalidContainer setting to true to stop seeing this error.");if(!e&&t===this.width&&i===this.height)return this;for(const n in this.elements){const o=this.elements[n];o.style.width=this.width+"px",o.style.height=this.height+"px"}for(const n in this.canvasContexts)this.elements[n].setAttribute("width",this.width*this.pixelRatio+"px"),this.elements[n].setAttribute("height",this.height*this.pixelRatio+"px"),this.pixelRatio!==1&&this.canvasContexts[n].scale(this.pixelRatio,this.pixelRatio);for(const n in this.webGLContexts){this.elements[n].setAttribute("width",this.width*this.pixelRatio+"px"),this.elements[n].setAttribute("height",this.height*this.pixelRatio+"px");const o=this.webGLContexts[n];if(o.viewport(0,0,this.width*this.pixelRatio,this.height*this.pixelRatio),this.pickingLayers.has(n)){const a=this.textures[n];a&&o.deleteTexture(a)}}return this.emit("resize"),this}clear(){return this.emit("beforeClear"),this.webGLContexts.nodes.bindFramebuffer(WebGLRenderingContext.FRAMEBUFFER,null),this.webGLContexts.nodes.clear(WebGLRenderingContext.COLOR_BUFFER_BIT),this.webGLContexts.edges.bindFramebuffer(WebGLRenderingContext.FRAMEBUFFER,null),this.webGLContexts.edges.clear(WebGLRenderingContext.COLOR_BUFFER_BIT),this.webGLContexts.hoverNodes.clear(WebGLRenderingContext.COLOR_BUFFER_BIT),this.canvasContexts.labels.clearRect(0,0,this.width,this.height),this.canvasContexts.hovers.clearRect(0,0,this.width,this.height),this.canvasContexts.edgeLabels.clearRect(0,0,this.width,this.height),this.emit("afterClear"),this}refresh(e){var o,a;const t=(e==null?void 0:e.skipIndexation)!==void 0?e==null?void 0:e.skipIndexation:!1,i=(e==null?void 0:e.schedule)!==void 0?e.schedule:!1,n=!e||!e.partialGraph;if(n)this.clearEdgeIndices(),this.clearNodeIndices(),this.graph.forEachNode(s=>this.addNode(s)),this.graph.forEachEdge(s=>this.addEdge(s));else{const s=((o=e.partialGraph)==null?void 0:o.nodes)||[];for(let l=0,u=(s==null?void 0:s.length)||0;l{this.render()})),this}scheduleRefresh(e){return this.refresh({...e,schedule:!0})}getViewportZoomedState(e,t){const{ratio:i,angle:n,x:o,y:a}=this.camera.getState(),{minCameraRatio:s,maxCameraRatio:c}=this.settings;typeof c=="number"&&(t=Math.min(t,c)),typeof s=="number"&&(t=Math.max(t,s));const l=t/i,u={x:this.width/2,y:this.height/2},h=this.viewportToFramedGraph(e),d=this.viewportToFramedGraph(u);return{angle:n,x:(h.x-d.x)*(1-l)+o,y:(h.y-d.y)*(1-l)+a,ratio:t}}viewRectangle(){const e=this.viewportToFramedGraph({x:0,y:0}),t=this.viewportToFramedGraph({x:this.width,y:0}),i=this.viewportToFramedGraph({x:0,y:this.height});return{x1:e.x,y1:e.y,x2:t.x,y2:t.y,height:t.y-i.y}}framedGraphToViewport(e,t={}){const i=!!t.cameraState||!!t.viewportDimensions||!!t.graphDimensions,n=t.matrix?t.matrix:i?Ee(t.cameraState||this.camera.getState(),t.viewportDimensions||this.getDimensions(),t.graphDimensions||this.getGraphDimensions(),t.padding||this.getStagePadding()):this.matrix,o=qe(n,e);return{x:(1+o.x)*this.width/2,y:(1-o.y)*this.height/2}}viewportToFramedGraph(e,t={}){const i=!!t.cameraState||!!t.viewportDimensions||!t.graphDimensions,n=t.matrix?t.matrix:i?Ee(t.cameraState||this.camera.getState(),t.viewportDimensions||this.getDimensions(),t.graphDimensions||this.getGraphDimensions(),t.padding||this.getStagePadding(),!0):this.invMatrix,o=qe(n,{x:e.x/this.width*2-1,y:1-e.y/this.height*2});return isNaN(o.x)&&(o.x=0),isNaN(o.y)&&(o.y=0),o}viewportToGraph(e,t={}){return this.normalizationFunction.inverse(this.viewportToFramedGraph(e,t))}graphToViewport(e,t={}){return this.framedGraphToViewport(this.normalizationFunction(e),t)}getGraphToViewportRatio(){const e={x:0,y:0},t={x:1,y:1},i=Math.sqrt(Math.pow(e.x-t.x,2)+Math.pow(e.y-t.y,2)),n=this.graphToViewport(e),o=this.graphToViewport(t);return Math.sqrt(Math.pow(n.x-o.x,2)+Math.pow(n.y-o.y,2))/i}getBBox(){return this.nodeExtent}getCustomBBox(){return this.customBBox}setCustomBBox(e){return this.customBBox=e,this.scheduleRender(),this}kill(){this.emit("kill"),this.removeAllListeners(),this.unbindCameraHandlers(),window.removeEventListener("resize",this.activeListeners.handleResize),this.mouseCaptor.kill(),this.touchCaptor.kill(),this.unbindGraphHandlers(),this.clearIndices(),this.clearState(),this.nodeDataCache={},this.edgeDataCache={},this.highlightedNodes.clear(),this.renderFrame&&(cancelAnimationFrame(this.renderFrame),this.renderFrame=null),this.renderHighlightedNodesFrame&&(cancelAnimationFrame(this.renderHighlightedNodesFrame),this.renderHighlightedNodesFrame=null);const e=this.container;for(;e.firstChild;)e.removeChild(e.firstChild);this.canvasContexts={},this.webGLContexts={},this.elements={};for(const t in this.nodePrograms)this.nodePrograms[t].kill();for(const t in this.nodeHoverPrograms)this.nodeHoverPrograms[t].kill();for(const t in this.edgePrograms)this.edgePrograms[t].kill();this.nodePrograms={},this.nodeHoverPrograms={},this.edgePrograms={};for(const t in this.elements)this.killLayer(t)}scaleSize(e=1,t=this.camera.ratio){return e/this.settings.zoomToSizeRatioFunction(t)*(this.getSetting("itemSizesReference")==="positions"?t*this.graphToViewportRatio:1)}getCanvases(){const e={};for(const t in this.elements)this.elements[t]instanceof HTMLCanvasElement&&(e[t]=this.elements[t]);return e}};const he=class he extends yn{};he.Camera=Te,he.MouseCaptor=mn,he.Sigma=yn,he.rendering={...vs,createNodeBorderProgram:Zr,createNodeImageProgram:Pt,createNodePiechartProgram:Fa,EdgeCurveProgram:_o},he.utils=ka;let lr=he;return lr}); diff --git a/playground/static/vis-network.min.js b/playground/static/vis-network.min.js new file mode 100644 index 0000000..e9a4003 --- /dev/null +++ b/playground/static/vis-network.min.js @@ -0,0 +1,34 @@ +/** + * vis-network + * https://visjs.github.io/vis-network/ + * + * A dynamic, browser-based visualization library. + * + * @version 9.1.9 + * @date 2023-11-03T01:42:27.418Z + * + * @copyright (c) 2011-2017 Almende B.V, http://almende.com + * @copyright (c) 2017-2019 visjs contributors, https://github.com/visjs + * + * @license + * vis.js is dual licensed under both + * + * 1. The Apache 2.0 License + * http://www.apache.org/licenses/LICENSE-2.0 + * + * and + * + * 2. The MIT License + * http://opensource.org/licenses/MIT + * + * vis.js may be distributed under either license. + */ +!function(g,t){"object"==typeof exports&&"undefined"!=typeof module?t(exports):"function"==typeof define&&define.amd?define(["exports"],t):t((g="undefined"!=typeof globalThis?globalThis:g||self).vis=g.vis||{})}(this,(function(g){var t="undefined"!=typeof globalThis?globalThis:"undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof self?self:{};function A(g){return g&&g.__esModule&&Object.prototype.hasOwnProperty.call(g,"default")?g.default:g}var e=function(g){return g&&g.Math===Math&&g},C=e("object"==typeof globalThis&&globalThis)||e("object"==typeof window&&window)||e("object"==typeof self&&self)||e("object"==typeof t&&t)||function(){return this}()||t||Function("return this")(),I=function(g){try{return!!g()}catch(g){return!0}},i=!I((function(){var g=function(){}.bind();return"function"!=typeof g||g.hasOwnProperty("prototype")})),o=i,n=Function.prototype,r=n.apply,s=n.call,a="object"==typeof Reflect&&Reflect.apply||(o?s.bind(r):function(){return s.apply(r,arguments)}),d=i,h=Function.prototype,l=h.call,c=d&&h.bind.bind(l,l),u=d?c:function(g){return function(){return l.apply(g,arguments)}},p=u,f=p({}.toString),v=p("".slice),y=function(g){return v(f(g),8,-1)},m=y,b=u,w=function(g){if("Function"===m(g))return b(g)},k="object"==typeof document&&document.all,x={all:k,IS_HTMLDDA:void 0===k&&void 0!==k},E=x.all,O=x.IS_HTMLDDA?function(g){return"function"==typeof g||g===E}:function(g){return"function"==typeof g},T={},D=!I((function(){return 7!==Object.defineProperty({},1,{get:function(){return 7}})[1]})),N=i,R=Function.prototype.call,P=N?R.bind(R):function(){return R.apply(R,arguments)},M={},B={}.propertyIsEnumerable,z=Object.getOwnPropertyDescriptor,S=z&&!B.call({1:2},1);M.f=S?function(g){var t=z(this,g);return!!t&&t.enumerable}:B;var Z,F,G=function(g,t){return{enumerable:!(1&g),configurable:!(2&g),writable:!(4&g),value:t}},j=I,L=y,V=Object,Y=u("".split),W=j((function(){return!V("z").propertyIsEnumerable(0)}))?function(g){return"String"===L(g)?Y(g,""):V(g)}:V,Q=function(g){return null==g},U=Q,_=TypeError,K=function(g){if(U(g))throw new _("Can't call method on "+g);return g},H=W,X=K,J=function(g){return H(X(g))},q=O,$=x.all,gg=x.IS_HTMLDDA?function(g){return"object"==typeof g?null!==g:q(g)||g===$}:function(g){return"object"==typeof g?null!==g:q(g)},tg={},Ag=tg,eg=C,Cg=O,Ig=function(g){return Cg(g)?g:void 0},ig=function(g,t){return arguments.length<2?Ig(Ag[g])||Ig(eg[g]):Ag[g]&&Ag[g][t]||eg[g]&&eg[g][t]},og=u({}.isPrototypeOf),ng="undefined"!=typeof navigator&&String(navigator.userAgent)||"",rg=C,sg=ng,ag=rg.process,dg=rg.Deno,hg=ag&&ag.versions||dg&&dg.version,lg=hg&&hg.v8;lg&&(F=(Z=lg.split("."))[0]>0&&Z[0]<4?1:+(Z[0]+Z[1])),!F&&sg&&(!(Z=sg.match(/Edge\/(\d+)/))||Z[1]>=74)&&(Z=sg.match(/Chrome\/(\d+)/))&&(F=+Z[1]);var cg=F,ug=cg,pg=I,fg=C.String,vg=!!Object.getOwnPropertySymbols&&!pg((function(){var g=Symbol("symbol detection");return!fg(g)||!(Object(g)instanceof Symbol)||!Symbol.sham&&ug&&ug<41})),yg=vg&&!Symbol.sham&&"symbol"==typeof Symbol.iterator,mg=ig,bg=O,wg=og,kg=Object,xg=yg?function(g){return"symbol"==typeof g}:function(g){var t=mg("Symbol");return bg(t)&&wg(t.prototype,kg(g))},Eg=String,Og=function(g){try{return Eg(g)}catch(g){return"Object"}},Tg=O,Dg=Og,Ng=TypeError,Rg=function(g){if(Tg(g))return g;throw new Ng(Dg(g)+" is not a function")},Pg=Rg,Mg=Q,Bg=function(g,t){var A=g[t];return Mg(A)?void 0:Pg(A)},zg=P,Sg=O,Zg=gg,Fg=TypeError,Gg={exports:{}},jg=C,Lg=Object.defineProperty,Vg=function(g,t){try{Lg(jg,g,{value:t,configurable:!0,writable:!0})}catch(A){jg[g]=t}return t},Yg="__core-js_shared__",Wg=C[Yg]||Vg(Yg,{}),Qg=Wg;(Gg.exports=function(g,t){return Qg[g]||(Qg[g]=void 0!==t?t:{})})("versions",[]).push({version:"3.33.0",mode:"pure",copyright:"© 2014-2023 Denis Pushkarev (zloirock.ru)",license:"https://github.com/zloirock/core-js/blob/v3.33.0/LICENSE",source:"https://github.com/zloirock/core-js"});var Ug=Gg.exports,_g=K,Kg=Object,Hg=function(g){return Kg(_g(g))},Xg=Hg,Jg=u({}.hasOwnProperty),qg=Object.hasOwn||function(g,t){return Jg(Xg(g),t)},$g=u,gt=0,tt=Math.random(),At=$g(1..toString),et=function(g){return"Symbol("+(void 0===g?"":g)+")_"+At(++gt+tt,36)},Ct=Ug,It=qg,it=et,ot=vg,nt=yg,rt=C.Symbol,st=Ct("wks"),at=nt?rt.for||rt:rt&&rt.withoutSetter||it,dt=function(g){return It(st,g)||(st[g]=ot&&It(rt,g)?rt[g]:at("Symbol."+g)),st[g]},ht=P,lt=gg,ct=xg,ut=Bg,pt=function(g,t){var A,e;if("string"===t&&Sg(A=g.toString)&&!Zg(e=zg(A,g)))return e;if(Sg(A=g.valueOf)&&!Zg(e=zg(A,g)))return e;if("string"!==t&&Sg(A=g.toString)&&!Zg(e=zg(A,g)))return e;throw new Fg("Can't convert object to primitive value")},ft=TypeError,vt=dt("toPrimitive"),yt=function(g,t){if(!lt(g)||ct(g))return g;var A,e=ut(g,vt);if(e){if(void 0===t&&(t="default"),A=ht(e,g,t),!lt(A)||ct(A))return A;throw new ft("Can't convert object to primitive value")}return void 0===t&&(t="number"),pt(g,t)},mt=xg,bt=function(g){var t=yt(g,"string");return mt(t)?t:t+""},wt=gg,kt=C.document,xt=wt(kt)&&wt(kt.createElement),Et=function(g){return xt?kt.createElement(g):{}},Ot=Et,Tt=!D&&!I((function(){return 7!==Object.defineProperty(Ot("div"),"a",{get:function(){return 7}}).a})),Dt=D,Nt=P,Rt=M,Pt=G,Mt=J,Bt=bt,zt=qg,St=Tt,Zt=Object.getOwnPropertyDescriptor;T.f=Dt?Zt:function(g,t){if(g=Mt(g),t=Bt(t),St)try{return Zt(g,t)}catch(g){}if(zt(g,t))return Pt(!Nt(Rt.f,g,t),g[t])};var Ft=I,Gt=O,jt=/#|\.prototype\./,Lt=function(g,t){var A=Yt[Vt(g)];return A===Qt||A!==Wt&&(Gt(t)?Ft(t):!!t)},Vt=Lt.normalize=function(g){return String(g).replace(jt,".").toLowerCase()},Yt=Lt.data={},Wt=Lt.NATIVE="N",Qt=Lt.POLYFILL="P",Ut=Lt,_t=Rg,Kt=i,Ht=w(w.bind),Xt=function(g,t){return _t(g),void 0===t?g:Kt?Ht(g,t):function(){return g.apply(t,arguments)}},Jt={},qt=D&&I((function(){return 42!==Object.defineProperty((function(){}),"prototype",{value:42,writable:!1}).prototype})),$t=gg,gA=String,tA=TypeError,AA=function(g){if($t(g))return g;throw new tA(gA(g)+" is not an object")},eA=D,CA=Tt,IA=qt,iA=AA,oA=bt,nA=TypeError,rA=Object.defineProperty,sA=Object.getOwnPropertyDescriptor,aA="enumerable",dA="configurable",hA="writable";Jt.f=eA?IA?function(g,t,A){if(iA(g),t=oA(t),iA(A),"function"==typeof g&&"prototype"===t&&"value"in A&&hA in A&&!A[hA]){var e=sA(g,t);e&&e[hA]&&(g[t]=A.value,A={configurable:dA in A?A[dA]:e[dA],enumerable:aA in A?A[aA]:e[aA],writable:!1})}return rA(g,t,A)}:rA:function(g,t,A){if(iA(g),t=oA(t),iA(A),CA)try{return rA(g,t,A)}catch(g){}if("get"in A||"set"in A)throw new nA("Accessors not supported");return"value"in A&&(g[t]=A.value),g};var lA=Jt,cA=G,uA=D?function(g,t,A){return lA.f(g,t,cA(1,A))}:function(g,t,A){return g[t]=A,g},pA=C,fA=a,vA=w,yA=O,mA=T.f,bA=Ut,wA=tg,kA=Xt,xA=uA,EA=qg,OA=function(g){var t=function(A,e,C){if(this instanceof t){switch(arguments.length){case 0:return new g;case 1:return new g(A);case 2:return new g(A,e)}return new g(A,e,C)}return fA(g,this,arguments)};return t.prototype=g.prototype,t},TA=function(g,t){var A,e,C,I,i,o,n,r,s,a=g.target,d=g.global,h=g.stat,l=g.proto,c=d?pA:h?pA[a]:(pA[a]||{}).prototype,u=d?wA:wA[a]||xA(wA,a,{})[a],p=u.prototype;for(I in t)e=!(A=bA(d?I:a+(h?".":"#")+I,g.forced))&&c&&EA(c,I),o=u[I],e&&(n=g.dontCallGetSet?(s=mA(c,I))&&s.value:c[I]),i=e&&n?n:t[I],e&&typeof o==typeof i||(r=g.bind&&e?kA(i,pA):g.wrap&&e?OA(i):l&&yA(i)?vA(i):i,(g.sham||i&&i.sham||o&&o.sham)&&xA(r,"sham",!0),xA(u,I,r),l&&(EA(wA,C=a+"Prototype")||xA(wA,C,{}),xA(wA[C],I,i),g.real&&p&&(A||!p[I])&&xA(p,I,i)))},DA=Math.ceil,NA=Math.floor,RA=Math.trunc||function(g){var t=+g;return(t>0?NA:DA)(t)},PA=function(g){var t=+g;return t!=t||0===t?0:RA(t)},MA=PA,BA=Math.max,zA=Math.min,SA=function(g,t){var A=MA(g);return A<0?BA(A+t,0):zA(A,t)},ZA=PA,FA=Math.min,GA=function(g){return g>0?FA(ZA(g),9007199254740991):0},jA=function(g){return GA(g.length)},LA=J,VA=SA,YA=jA,WA=function(g){return function(t,A,e){var C,I=LA(t),i=YA(I),o=VA(e,i);if(g&&A!=A){for(;i>o;)if((C=I[o++])!=C)return!0}else for(;i>o;o++)if((g||o in I)&&I[o]===A)return g||o||0;return!g&&-1}},QA={includes:WA(!0),indexOf:WA(!1)},UA={},_A=qg,KA=J,HA=QA.indexOf,XA=UA,JA=u([].push),qA=function(g,t){var A,e=KA(g),C=0,I=[];for(A in e)!_A(XA,A)&&_A(e,A)&&JA(I,A);for(;t.length>C;)_A(e,A=t[C++])&&(~HA(I,A)||JA(I,A));return I},$A=["constructor","hasOwnProperty","isPrototypeOf","propertyIsEnumerable","toLocaleString","toString","valueOf"],ge=qA,te=$A,Ae=Object.keys||function(g){return ge(g,te)},ee={};ee.f=Object.getOwnPropertySymbols;var Ce=D,Ie=u,ie=P,oe=I,ne=Ae,re=ee,se=M,ae=Hg,de=W,he=Object.assign,le=Object.defineProperty,ce=Ie([].concat),ue=!he||oe((function(){if(Ce&&1!==he({b:1},he(le({},"a",{enumerable:!0,get:function(){le(this,"b",{value:3,enumerable:!1})}}),{b:2})).b)return!0;var g={},t={},A=Symbol("assign detection"),e="abcdefghijklmnopqrst";return g[A]=7,e.split("").forEach((function(g){t[g]=g})),7!==he({},g)[A]||ne(he({},t)).join("")!==e}))?function(g,t){for(var A=ae(g),e=arguments.length,C=1,I=re.f,i=se.f;e>C;)for(var o,n=de(arguments[C++]),r=I?ce(ne(n),I(n)):ne(n),s=r.length,a=0;s>a;)o=r[a++],Ce&&!ie(i,n,o)||(A[o]=n[o]);return A}:he,pe=ue;TA({target:"Object",stat:!0,arity:2,forced:Object.assign!==pe},{assign:pe});var fe=A(tg.Object.assign),ve=u([].slice),ye=u,me=Rg,be=gg,we=qg,ke=ve,xe=i,Ee=Function,Oe=ye([].concat),Te=ye([].join),De={},Ne=xe?Ee.bind:function(g){var t=me(this),A=t.prototype,e=ke(arguments,1),C=function(){var A=Oe(e,ke(arguments));return this instanceof C?function(g,t,A){if(!we(De,t)){for(var e=[],C=0;C=.1;)(l=+I[a++%i])>s&&(l=s),h=Math.sqrt(l*l/(1+r*r)),t+=h=o<0?-h:h,A+=r*h,!0===d?g.lineTo(t,A):g.moveTo(t,A),s-=l,d=!d}var Ue={circle:Le,dashedLine:Qe,database:We,diamond:function(g,t,A,e){g.beginPath(),g.lineTo(t,A+e),g.lineTo(t+e,A),g.lineTo(t,A-e),g.lineTo(t-e,A),g.closePath()},ellipse:Ye,ellipse_vis:Ye,hexagon:function(g,t,A,e){g.beginPath();var C=2*Math.PI/6;g.moveTo(t+e,A);for(var I=1;I<6;I++)g.lineTo(t+e*Math.cos(C*I),A+e*Math.sin(C*I));g.closePath()},roundRect:Ve,square:function(g,t,A,e){g.beginPath(),g.rect(t-e,A-e,2*e,2*e),g.closePath()},star:function(g,t,A,e){g.beginPath(),A+=.1*(e*=.82);for(var C=0;C<10;C++){var I=C%2==0?1.3*e:.5*e;g.lineTo(t+I*Math.sin(2*C*Math.PI/10),A-I*Math.cos(2*C*Math.PI/10))}g.closePath()},triangle:function(g,t,A,e){g.beginPath(),A+=.275*(e*=1.15);var C=2*e,I=C/2,i=Math.sqrt(3)/6*C,o=Math.sqrt(C*C-I*I);g.moveTo(t,A-(o-i)),g.lineTo(t+I,A+i),g.lineTo(t-I,A+i),g.lineTo(t,A-(o-i)),g.closePath()},triangleDown:function(g,t,A,e){g.beginPath(),A-=.275*(e*=1.15);var C=2*e,I=C/2,i=Math.sqrt(3)/6*C,o=Math.sqrt(C*C-I*I);g.moveTo(t,A+(o-i)),g.lineTo(t+I,A-i),g.lineTo(t-I,A-i),g.lineTo(t,A+(o-i)),g.closePath()}};function _e(g,t){void 0===t&&(t={});var A=t.insertAt;if(g&&"undefined"!=typeof document){var e=document.head||document.getElementsByTagName("head")[0],C=document.createElement("style");C.type="text/css","top"===A&&e.firstChild?e.insertBefore(C,e.firstChild):e.appendChild(C),C.styleSheet?C.styleSheet.cssText=g:C.appendChild(document.createTextNode(g))}}_e(".vis-overlay{bottom:0;left:0;position:absolute;right:0;top:0;z-index:10}.vis-active{box-shadow:0 0 10px #86d5f8}");_e(".vis [class*=span]{min-height:0;width:auto}");_e('div.vis-color-picker{background-color:#fff;border-radius:15px;box-shadow:0 0 10px 0 rgba(0,0,0,.5);display:none;height:444px;left:30px;margin-left:30px;margin-top:-140px;padding:10px;position:absolute;top:0;width:310px;z-index:1}div.vis-color-picker div.vis-arrow{left:5px;position:absolute;top:147px}div.vis-color-picker div.vis-arrow:after,div.vis-color-picker div.vis-arrow:before{border:solid transparent;content:" ";height:0;pointer-events:none;position:absolute;right:100%;top:50%;width:0}div.vis-color-picker div.vis-arrow:after{border-color:hsla(0,0%,100%,0) #fff hsla(0,0%,100%,0) hsla(0,0%,100%,0);border-width:30px;margin-top:-30px}div.vis-color-picker div.vis-color{cursor:pointer;height:289px;position:absolute;width:289px}div.vis-color-picker div.vis-brightness{position:absolute;top:313px}div.vis-color-picker div.vis-opacity{position:absolute;top:350px}div.vis-color-picker div.vis-selector{background:#4c4c4c;background:-moz-linear-gradient(top,#4c4c4c 0,#595959 12%,#666 25%,#474747 39%,#2c2c2c 50%,#000 51%,#111 60%,#2b2b2b 76%,#1c1c1c 91%,#131313 100%);background:-webkit-gradient(linear,left top,left bottom,color-stop(0,#4c4c4c),color-stop(12%,#595959),color-stop(25%,#666),color-stop(39%,#474747),color-stop(50%,#2c2c2c),color-stop(51%,#000),color-stop(60%,#111),color-stop(76%,#2b2b2b),color-stop(91%,#1c1c1c),color-stop(100%,#131313));background:-webkit-linear-gradient(top,#4c4c4c,#595959 12%,#666 25%,#474747 39%,#2c2c2c 50%,#000 51%,#111 60%,#2b2b2b 76%,#1c1c1c 91%,#131313);background:-o-linear-gradient(top,#4c4c4c 0,#595959 12%,#666 25%,#474747 39%,#2c2c2c 50%,#000 51%,#111 60%,#2b2b2b 76%,#1c1c1c 91%,#131313 100%);background:-ms-linear-gradient(top,#4c4c4c 0,#595959 12%,#666 25%,#474747 39%,#2c2c2c 50%,#000 51%,#111 60%,#2b2b2b 76%,#1c1c1c 91%,#131313 100%);background:linear-gradient(180deg,#4c4c4c 0,#595959 12%,#666 25%,#474747 39%,#2c2c2c 50%,#000 51%,#111 60%,#2b2b2b 76%,#1c1c1c 91%,#131313);border:1px solid #fff;border-radius:15px;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr="#4c4c4c",endColorstr="#131313",GradientType=0);height:15px;left:137px;position:absolute;top:137px;width:15px}div.vis-color-picker div.vis-new-color{left:159px;padding-right:2px;text-align:right}div.vis-color-picker div.vis-initial-color,div.vis-color-picker div.vis-new-color{border:1px solid rgba(0,0,0,.1);border-radius:5px;color:rgba(0,0,0,.4);font-size:10px;height:20px;line-height:20px;position:absolute;top:380px;vertical-align:middle;width:140px}div.vis-color-picker div.vis-initial-color{left:10px;padding-left:2px;text-align:left}div.vis-color-picker div.vis-label{left:10px;position:absolute;width:300px}div.vis-color-picker div.vis-label.vis-brightness{top:300px}div.vis-color-picker div.vis-label.vis-opacity{top:338px}div.vis-color-picker div.vis-button{background-color:#f7f7f7;border:2px solid #d9d9d9;border-radius:10px;cursor:pointer;height:25px;line-height:25px;position:absolute;text-align:center;top:410px;vertical-align:middle;width:68px}div.vis-color-picker div.vis-button.vis-cancel{left:5px}div.vis-color-picker div.vis-button.vis-load{left:82px}div.vis-color-picker div.vis-button.vis-apply{left:159px}div.vis-color-picker div.vis-button.vis-save{left:236px}div.vis-color-picker input.vis-range{height:20px;width:290px}');_e('div.vis-configuration{display:block;float:left;font-size:12px;position:relative}div.vis-configuration-wrapper{display:block;width:700px}div.vis-configuration-wrapper:after{clear:both;content:"";display:block}div.vis-configuration.vis-config-option-container{background-color:#fff;border:2px solid #f7f8fa;border-radius:4px;display:block;left:10px;margin-top:20px;padding-left:5px;width:495px}div.vis-configuration.vis-config-button{background-color:#f7f8fa;border:2px solid #ceced0;border-radius:4px;cursor:pointer;display:block;height:25px;left:10px;line-height:25px;margin-bottom:30px;margin-top:20px;padding-left:5px;vertical-align:middle;width:495px}div.vis-configuration.vis-config-button.hover{background-color:#4588e6;border:2px solid #214373;color:#fff}div.vis-configuration.vis-config-item{display:block;float:left;height:25px;line-height:25px;vertical-align:middle;width:495px}div.vis-configuration.vis-config-item.vis-config-s2{background-color:#f7f8fa;border-radius:3px;left:10px;padding-left:5px}div.vis-configuration.vis-config-item.vis-config-s3{background-color:#e4e9f0;border-radius:3px;left:20px;padding-left:5px}div.vis-configuration.vis-config-item.vis-config-s4{background-color:#cfd8e6;border-radius:3px;left:30px;padding-left:5px}div.vis-configuration.vis-config-header{font-size:18px;font-weight:700}div.vis-configuration.vis-config-label{height:25px;line-height:25px;width:120px}div.vis-configuration.vis-config-label.vis-config-s3{width:110px}div.vis-configuration.vis-config-label.vis-config-s4{width:100px}div.vis-configuration.vis-config-colorBlock{border:1px solid #444;border-radius:2px;cursor:pointer;height:19px;margin:0;padding:0;top:1px;width:30px}input.vis-configuration.vis-config-checkbox{left:-5px}input.vis-configuration.vis-config-rangeinput{margin:0;padding:1px;pointer-events:none;position:relative;top:-5px;width:60px}input.vis-configuration.vis-config-range{-webkit-appearance:none;background-color:transparent;border:0 solid #fff;height:20px;width:300px}input.vis-configuration.vis-config-range::-webkit-slider-runnable-track{background:#dedede;background:-moz-linear-gradient(top,#dedede 0,#c8c8c8 99%);background:-webkit-gradient(linear,left top,left bottom,color-stop(0,#dedede),color-stop(99%,#c8c8c8));background:-webkit-linear-gradient(top,#dedede,#c8c8c8 99%);background:-o-linear-gradient(top,#dedede 0,#c8c8c8 99%);background:-ms-linear-gradient(top,#dedede 0,#c8c8c8 99%);background:linear-gradient(180deg,#dedede 0,#c8c8c8 99%);border:1px solid #999;border-radius:3px;box-shadow:0 0 3px 0 #aaa;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr="#dedede",endColorstr="#c8c8c8",GradientType=0);height:5px;width:300px}input.vis-configuration.vis-config-range::-webkit-slider-thumb{-webkit-appearance:none;background:#3876c2;background:-moz-linear-gradient(top,#3876c2 0,#385380 100%);background:-webkit-gradient(linear,left top,left bottom,color-stop(0,#3876c2),color-stop(100%,#385380));background:-webkit-linear-gradient(top,#3876c2,#385380);background:-o-linear-gradient(top,#3876c2 0,#385380 100%);background:-ms-linear-gradient(top,#3876c2 0,#385380 100%);background:linear-gradient(180deg,#3876c2 0,#385380);border:1px solid #14334b;border-radius:50%;box-shadow:0 0 1px 0 #111927;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr="#3876c2",endColorstr="#385380",GradientType=0);height:17px;margin-top:-7px;width:17px}input.vis-configuration.vis-config-range:focus{outline:none}input.vis-configuration.vis-config-range:focus::-webkit-slider-runnable-track{background:#9d9d9d;background:-moz-linear-gradient(top,#9d9d9d 0,#c8c8c8 99%);background:-webkit-gradient(linear,left top,left bottom,color-stop(0,#9d9d9d),color-stop(99%,#c8c8c8));background:-webkit-linear-gradient(top,#9d9d9d,#c8c8c8 99%);background:-o-linear-gradient(top,#9d9d9d 0,#c8c8c8 99%);background:-ms-linear-gradient(top,#9d9d9d 0,#c8c8c8 99%);background:linear-gradient(180deg,#9d9d9d 0,#c8c8c8 99%);filter:progid:DXImageTransform.Microsoft.gradient(startColorstr="#9d9d9d",endColorstr="#c8c8c8",GradientType=0)}input.vis-configuration.vis-config-range::-moz-range-track{background:#dedede;background:-moz-linear-gradient(top,#dedede 0,#c8c8c8 99%);background:-webkit-gradient(linear,left top,left bottom,color-stop(0,#dedede),color-stop(99%,#c8c8c8));background:-webkit-linear-gradient(top,#dedede,#c8c8c8 99%);background:-o-linear-gradient(top,#dedede 0,#c8c8c8 99%);background:-ms-linear-gradient(top,#dedede 0,#c8c8c8 99%);background:linear-gradient(180deg,#dedede 0,#c8c8c8 99%);border:1px solid #999;border-radius:3px;box-shadow:0 0 3px 0 #aaa;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr="#dedede",endColorstr="#c8c8c8",GradientType=0);height:10px;width:300px}input.vis-configuration.vis-config-range::-moz-range-thumb{background:#385380;border:none;border-radius:50%;height:16px;width:16px}input.vis-configuration.vis-config-range:-moz-focusring{outline:1px solid #fff;outline-offset:-1px}input.vis-configuration.vis-config-range::-ms-track{background:transparent;border-color:transparent;border-width:6px 0;color:transparent;height:5px;width:300px}input.vis-configuration.vis-config-range::-ms-fill-lower{background:#777;border-radius:10px}input.vis-configuration.vis-config-range::-ms-fill-upper{background:#ddd;border-radius:10px}input.vis-configuration.vis-config-range::-ms-thumb{background:#385380;border:none;border-radius:50%;height:16px;width:16px}input.vis-configuration.vis-config-range:focus::-ms-fill-lower{background:#888}input.vis-configuration.vis-config-range:focus::-ms-fill-upper{background:#ccc}.vis-configuration-popup{background:rgba(57,76,89,.85);border:2px solid #f2faff;border-radius:4px;color:#fff;font-size:14px;height:30px;line-height:30px;position:absolute;text-align:center;-webkit-transition:opacity .3s ease-in-out;-moz-transition:opacity .3s ease-in-out;transition:opacity .3s ease-in-out;width:150px}.vis-configuration-popup:after,.vis-configuration-popup:before{border:solid transparent;content:" ";height:0;left:100%;pointer-events:none;position:absolute;top:50%;width:0}.vis-configuration-popup:after{border-color:rgba(136,183,213,0) rgba(136,183,213,0) rgba(136,183,213,0) rgba(57,76,89,.85);border-width:8px;margin-top:-8px}.vis-configuration-popup:before{border-color:rgba(194,225,245,0) rgba(194,225,245,0) rgba(194,225,245,0) #f2faff;border-width:12px;margin-top:-12px}');_e("div.vis-tooltip{background-color:#f5f4ed;border:1px solid #808074;-moz-border-radius:3px;-webkit-border-radius:3px;border-radius:3px;box-shadow:3px 3px 10px rgba(0,0,0,.2);color:#000;font-family:verdana;font-size:14px;padding:5px;pointer-events:none;position:absolute;visibility:hidden;white-space:nowrap;z-index:5}");var Ke={exports:{}};!function(g){function t(g){if(g)return function(g){for(var A in t.prototype)g[A]=t.prototype[A];return g}(g)}g.exports=t,t.prototype.on=t.prototype.addEventListener=function(g,t){return this._callbacks=this._callbacks||{},(this._callbacks["$"+g]=this._callbacks["$"+g]||[]).push(t),this},t.prototype.once=function(g,t){function A(){this.off(g,A),t.apply(this,arguments)}return A.fn=t,this.on(g,A),this},t.prototype.off=t.prototype.removeListener=t.prototype.removeAllListeners=t.prototype.removeEventListener=function(g,t){if(this._callbacks=this._callbacks||{},0==arguments.length)return this._callbacks={},this;var A,e=this._callbacks["$"+g];if(!e)return this;if(1==arguments.length)return delete this._callbacks["$"+g],this;for(var C=0;C=o?g?"":void 0:(e=uC(I,i))<55296||e>56319||i+1===o||(C=uC(I,i+1))<56320||C>57343?g?cC(I,i):e:g?pC(I,i,i+2):C-56320+(e-55296<<10)+65536}},vC={codeAt:fC(!1),charAt:fC(!0)},yC=O,mC=C.WeakMap,bC=yC(mC)&&/native code/.test(String(mC)),wC=et,kC=Ug("keys"),xC=function(g){return kC[g]||(kC[g]=wC(g))},EC=bC,OC=C,TC=gg,DC=uA,NC=qg,RC=Wg,PC=xC,MC=UA,BC="Object already initialized",zC=OC.TypeError,SC=OC.WeakMap;if(EC||RC.state){var ZC=RC.state||(RC.state=new SC);ZC.get=ZC.get,ZC.has=ZC.has,ZC.set=ZC.set,Je=function(g,t){if(ZC.has(g))throw new zC(BC);return t.facade=g,ZC.set(g,t),t},qe=function(g){return ZC.get(g)||{}},$e=function(g){return ZC.has(g)}}else{var FC=PC("state");MC[FC]=!0,Je=function(g,t){if(NC(g,FC))throw new zC(BC);return t.facade=g,DC(g,FC,t),t},qe=function(g){return NC(g,FC)?g[FC]:{}},$e=function(g){return NC(g,FC)}}var GC={set:Je,get:qe,has:$e,enforce:function(g){return $e(g)?qe(g):Je(g,{})},getterFor:function(g){return function(t){var A;if(!TC(t)||(A=qe(t)).type!==g)throw new zC("Incompatible receiver, "+g+" required");return A}}},jC=D,LC=qg,VC=Function.prototype,YC=jC&&Object.getOwnPropertyDescriptor,WC=LC(VC,"name"),QC={EXISTS:WC,PROPER:WC&&"something"===function(){}.name,CONFIGURABLE:WC&&(!jC||jC&&YC(VC,"name").configurable)},UC={},_C=D,KC=qt,HC=Jt,XC=AA,JC=J,qC=Ae;UC.f=_C&&!KC?Object.defineProperties:function(g,t){XC(g);for(var A,e=JC(t),C=qC(t),I=C.length,i=0;I>i;)HC.f(g,A=C[i++],e[A]);return g};var $C,gI=ig("document","documentElement"),tI=AA,AI=UC,eI=$A,CI=UA,II=gI,iI=Et,oI="prototype",nI="script",rI=xC("IE_PROTO"),sI=function(){},aI=function(g){return"<"+nI+">"+g+""},dI=function(g){g.write(aI("")),g.close();var t=g.parentWindow.Object;return g=null,t},hI=function(){try{$C=new ActiveXObject("htmlfile")}catch(g){}var g,t,A;hI="undefined"!=typeof document?document.domain&&$C?dI($C):(t=iI("iframe"),A="java"+nI+":",t.style.display="none",II.appendChild(t),t.src=String(A),(g=t.contentWindow.document).open(),g.write(aI("document.F=Object")),g.close(),g.F):dI($C);for(var e=eI.length;e--;)delete hI[oI][eI[e]];return hI()};CI[rI]=!0;var lI,cI,uI,pI=Object.create||function(g,t){var A;return null!==g?(sI[oI]=tI(g),A=new sI,sI[oI]=null,A[rI]=g):A=hI(),void 0===t?A:AI.f(A,t)},fI=!I((function(){function g(){}return g.prototype.constructor=null,Object.getPrototypeOf(new g)!==g.prototype})),vI=qg,yI=O,mI=Hg,bI=fI,wI=xC("IE_PROTO"),kI=Object,xI=kI.prototype,EI=bI?kI.getPrototypeOf:function(g){var t=mI(g);if(vI(t,wI))return t[wI];var A=t.constructor;return yI(A)&&t instanceof A?A.prototype:t instanceof kI?xI:null},OI=uA,TI=function(g,t,A,e){return e&&e.enumerable?g[t]=A:OI(g,t,A),g},DI=I,NI=O,RI=gg,PI=pI,MI=EI,BI=TI,zI=dt("iterator"),SI=!1;[].keys&&("next"in(uI=[].keys())?(cI=MI(MI(uI)))!==Object.prototype&&(lI=cI):SI=!0);var ZI=!RI(lI)||DI((function(){var g={};return lI[zI].call(g)!==g}));NI((lI=ZI?{}:PI(lI))[zI])||BI(lI,zI,(function(){return this}));var FI={IteratorPrototype:lI,BUGGY_SAFARI_ITERATORS:SI},GI=oC,jI=gC?{}.toString:function(){return"[object "+GI(this)+"]"},LI=gC,VI=Jt.f,YI=uA,WI=qg,QI=jI,UI=dt("toStringTag"),_I=function(g,t,A,e){if(g){var C=A?g:g.prototype;WI(C,UI)||VI(C,UI,{configurable:!0,value:t}),e&&!LI&&YI(C,"toString",QI)}},KI={},HI=FI.IteratorPrototype,XI=pI,JI=G,qI=_I,$I=KI,gi=function(){return this},ti=u,Ai=Rg,ei=O,Ci=String,Ii=TypeError,ii=function(g,t,A){try{return ti(Ai(Object.getOwnPropertyDescriptor(g,t)[A]))}catch(g){}},oi=AA,ni=function(g){if("object"==typeof g||ei(g))return g;throw new Ii("Can't set "+Ci(g)+" as a prototype")},ri=Object.setPrototypeOf||("__proto__"in{}?function(){var g,t=!1,A={};try{(g=ii(Object.prototype,"__proto__","set"))(A,[]),t=A instanceof Array}catch(g){}return function(A,e){return oi(A),ni(e),t?g(A,e):A.__proto__=e,A}}():void 0),si=TA,ai=P,di=QC,hi=function(g,t,A,e){var C=t+" Iterator";return g.prototype=XI(HI,{next:JI(+!e,A)}),qI(g,C,!1,!0),$I[C]=gi,g},li=EI,ci=_I,ui=TI,pi=KI,fi=FI,vi=di.PROPER,yi=fi.BUGGY_SAFARI_ITERATORS,mi=dt("iterator"),bi="keys",wi="values",ki="entries",xi=function(){return this},Ei=function(g,t,A,e,C,I,i){hi(A,t,e);var o,n,r,s=function(g){if(g===C&&c)return c;if(!yi&&g&&g in h)return h[g];switch(g){case bi:case wi:case ki:return function(){return new A(this,g)}}return function(){return new A(this)}},a=t+" Iterator",d=!1,h=g.prototype,l=h[mi]||h["@@iterator"]||C&&h[C],c=!yi&&l||s(C),u="Array"===t&&h.entries||l;if(u&&(o=li(u.call(new g)))!==Object.prototype&&o.next&&(ci(o,a,!0,!0),pi[a]=xi),vi&&C===wi&&l&&l.name!==wi&&(d=!0,c=function(){return ai(l,this)}),C)if(n={values:s(wi),keys:I?c:s(bi),entries:s(ki)},i)for(r in n)(yi||d||!(r in h))&&ui(h,r,n[r]);else si({target:t,proto:!0,forced:yi||d},n);return i&&h[mi]!==c&&ui(h,mi,c,{name:C}),pi[t]=c,n},Oi=function(g,t){return{value:g,done:t}},Ti=vC.charAt,Di=sC,Ni=GC,Ri=Ei,Pi=Oi,Mi="String Iterator",Bi=Ni.set,zi=Ni.getterFor(Mi);Ri(String,"String",(function(g){Bi(this,{type:Mi,string:Di(g),index:0})}),(function(){var g,t=zi(this),A=t.string,e=t.index;return e>=A.length?Pi(void 0,!0):(g=Ti(A,e),t.index+=g.length,Pi(g,!1))}));var Si=P,Zi=AA,Fi=Bg,Gi=function(g,t,A){var e,C;Zi(g);try{if(!(e=Fi(g,"return"))){if("throw"===t)throw A;return A}e=Si(e,g)}catch(g){C=!0,e=g}if("throw"===t)throw A;if(C)throw e;return Zi(e),A},ji=AA,Li=Gi,Vi=KI,Yi=dt("iterator"),Wi=Array.prototype,Qi=function(g){return void 0!==g&&(Vi.Array===g||Wi[Yi]===g)},Ui=O,_i=Wg,Ki=u(Function.toString);Ui(_i.inspectSource)||(_i.inspectSource=function(g){return Ki(g)});var Hi=_i.inspectSource,Xi=u,Ji=I,qi=O,$i=oC,go=Hi,to=function(){},Ao=[],eo=ig("Reflect","construct"),Co=/^\s*(?:class|function)\b/,Io=Xi(Co.exec),io=!Co.test(to),oo=function(g){if(!qi(g))return!1;try{return eo(to,Ao,g),!0}catch(g){return!1}},no=function(g){if(!qi(g))return!1;switch($i(g)){case"AsyncFunction":case"GeneratorFunction":case"AsyncGeneratorFunction":return!1}try{return io||!!Io(Co,go(g))}catch(g){return!0}};no.sham=!0;var ro=!eo||Ji((function(){var g;return oo(oo.call)||!oo(Object)||!oo((function(){g=!0}))||g}))?no:oo,so=bt,ao=Jt,ho=G,lo=function(g,t,A){var e=so(t);e in g?ao.f(g,e,ho(0,A)):g[e]=A},co=oC,uo=Bg,po=Q,fo=KI,vo=dt("iterator"),yo=function(g){if(!po(g))return uo(g,vo)||uo(g,"@@iterator")||fo[co(g)]},mo=P,bo=Rg,wo=AA,ko=Og,xo=yo,Eo=TypeError,Oo=function(g,t){var A=arguments.length<2?xo(g):t;if(bo(A))return wo(mo(A,g));throw new Eo(ko(g)+" is not iterable")},To=Xt,Do=P,No=Hg,Ro=function(g,t,A,e){try{return e?t(ji(A)[0],A[1]):t(A)}catch(t){Li(g,"throw",t)}},Po=Qi,Mo=ro,Bo=jA,zo=lo,So=Oo,Zo=yo,Fo=Array,Go=dt("iterator"),jo=!1;try{var Lo=0,Vo={next:function(){return{done:!!Lo++}},return:function(){jo=!0}};Vo[Go]=function(){return this},Array.from(Vo,(function(){throw 2}))}catch(g){}var Yo=function(g,t){try{if(!t&&!jo)return!1}catch(g){return!1}var A=!1;try{var e={};e[Go]=function(){return{next:function(){return{done:A=!0}}}},g(e)}catch(g){}return A},Wo=function(g){var t=No(g),A=Mo(this),e=arguments.length,C=e>1?arguments[1]:void 0,I=void 0!==C;I&&(C=To(C,e>2?arguments[2]:void 0));var i,o,n,r,s,a,d=Zo(t),h=0;if(!d||this===Fo&&Po(d))for(i=Bo(t),o=A?new this(i):Fo(i);i>h;h++)a=I?C(t[h],h):t[h],zo(o,h,a);else for(s=(r=So(t,d)).next,o=A?new this:[];!(n=Do(s,r)).done;h++)a=I?Ro(r,C,[n.value,h],!0):n.value,zo(o,h,a);return o.length=h,o};TA({target:"Array",stat:!0,forced:!Yo((function(g){Array.from(g)}))},{from:Wo});var Qo=tg.Array.from,Uo=A(Qo),_o=J,Ko=KI,Ho=GC;Jt.f;var Xo=Ei,Jo=Oi,qo="Array Iterator",$o=Ho.set,gn=Ho.getterFor(qo);Xo(Array,"Array",(function(g,t){$o(this,{type:qo,target:_o(g),index:0,kind:t})}),(function(){var g=gn(this),t=g.target,A=g.kind,e=g.index++;if(!t||e>=t.length)return g.target=void 0,Jo(void 0,!0);switch(A){case"keys":return Jo(e,!1);case"values":return Jo(t[e],!1)}return Jo([e,t[e]],!1)}),"values"),Ko.Arguments=Ko.Array;var tn=yo,An={CSSRuleList:0,CSSStyleDeclaration:0,CSSValueList:0,ClientRectList:0,DOMRectList:0,DOMStringList:0,DOMTokenList:1,DataTransferItemList:0,FileList:0,HTMLAllCollection:0,HTMLCollection:0,HTMLFormElement:0,HTMLSelectElement:0,MediaList:0,MimeTypeArray:0,NamedNodeMap:0,NodeList:1,PaintRequestList:0,Plugin:0,PluginArray:0,SVGLengthList:0,SVGNumberList:0,SVGPathSegList:0,SVGPointList:0,SVGStringList:0,SVGTransformList:0,SourceBufferList:0,StyleSheetList:0,TextTrackCueList:0,TextTrackList:0,TouchList:0},en=C,Cn=oC,In=uA,on=KI,nn=dt("toStringTag");for(var rn in An){var sn=en[rn],an=sn&&sn.prototype;an&&Cn(an)!==nn&&In(an,nn,rn),on[rn]=on.Array}var dn=tn,hn=A(dn),ln=A(dn);function cn(g,t){if(!(g instanceof t))throw new TypeError("Cannot call a class as a function")}var un={exports:{}},pn=TA,fn=D,vn=Jt.f;pn({target:"Object",stat:!0,forced:Object.defineProperty!==vn,sham:!fn},{defineProperty:vn});var yn=tg.Object,mn=un.exports=function(g,t,A){return yn.defineProperty(g,t,A)};yn.defineProperty.sham&&(mn.sham=!0);var bn=un.exports,wn=bn,kn=A(wn),xn=y,En=Array.isArray||function(g){return"Array"===xn(g)},On=TypeError,Tn=function(g){if(g>9007199254740991)throw On("Maximum allowed index exceeded");return g},Dn=En,Nn=ro,Rn=gg,Pn=dt("species"),Mn=Array,Bn=function(g){var t;return Dn(g)&&(t=g.constructor,(Nn(t)&&(t===Mn||Dn(t.prototype))||Rn(t)&&null===(t=t[Pn]))&&(t=void 0)),void 0===t?Mn:t},zn=function(g,t){return new(Bn(g))(0===t?0:t)},Sn=I,Zn=cg,Fn=dt("species"),Gn=function(g){return Zn>=51||!Sn((function(){var t=[];return(t.constructor={})[Fn]=function(){return{foo:1}},1!==t[g](Boolean).foo}))},jn=TA,Ln=I,Vn=En,Yn=gg,Wn=Hg,Qn=jA,Un=Tn,_n=lo,Kn=zn,Hn=Gn,Xn=cg,Jn=dt("isConcatSpreadable"),qn=Xn>=51||!Ln((function(){var g=[];return g[Jn]=!1,g.concat()[0]!==g})),$n=function(g){if(!Yn(g))return!1;var t=g[Jn];return void 0!==t?!!t:Vn(g)};jn({target:"Array",proto:!0,arity:1,forced:!qn||!Hn("concat")},{concat:function(g){var t,A,e,C,I,i=Wn(this),o=Kn(i,0),n=0;for(t=-1,e=arguments.length;tf;f++)if((o||f in c)&&(h=u(d=c[f],f,l),g))if(t)y[f]=h;else if(h)switch(g){case 3:return!0;case 5:return d;case 6:return f;case 2:Br(y,d)}else switch(g){case 4:return!1;case 7:Br(y,d)}return I?-1:e||C?C:y}},Sr={forEach:zr(0),map:zr(1),filter:zr(2),some:zr(3),every:zr(4),find:zr(5),findIndex:zr(6),filterReject:zr(7)},Zr=TA,Fr=C,Gr=P,jr=u,Lr=D,Vr=vg,Yr=I,Wr=qg,Qr=og,Ur=AA,_r=J,Kr=bt,Hr=sC,Xr=G,Jr=pI,qr=Ae,$r=gr,gs=er,ts=ee,As=T,es=Jt,Cs=UC,Is=M,is=TI,os=ur,ns=Ug,rs=UA,ss=et,as=dt,ds=pr,hs=wr,ls=Tr,cs=_I,us=GC,ps=Sr.forEach,fs=xC("hidden"),vs="Symbol",ys="prototype",ms=us.set,bs=us.getterFor(vs),ws=Object[ys],ks=Fr.Symbol,xs=ks&&ks[ys],Es=Fr.RangeError,Os=Fr.TypeError,Ts=Fr.QObject,Ds=As.f,Ns=es.f,Rs=gs.f,Ps=Is.f,Ms=jr([].push),Bs=ns("symbols"),zs=ns("op-symbols"),Ss=ns("wks"),Zs=!Ts||!Ts[ys]||!Ts[ys].findChild,Fs=function(g,t,A){var e=Ds(ws,t);e&&delete ws[t],Ns(g,t,A),e&&g!==ws&&Ns(ws,t,e)},Gs=Lr&&Yr((function(){return 7!==Jr(Ns({},"a",{get:function(){return Ns(this,"a",{value:7}).a}})).a}))?Fs:Ns,js=function(g,t){var A=Bs[g]=Jr(xs);return ms(A,{type:vs,tag:g,description:t}),Lr||(A.description=t),A},Ls=function(g,t,A){g===ws&&Ls(zs,t,A),Ur(g);var e=Kr(t);return Ur(A),Wr(Bs,e)?(A.enumerable?(Wr(g,fs)&&g[fs][e]&&(g[fs][e]=!1),A=Jr(A,{enumerable:Xr(0,!1)})):(Wr(g,fs)||Ns(g,fs,Xr(1,{})),g[fs][e]=!0),Gs(g,e,A)):Ns(g,e,A)},Vs=function(g,t){Ur(g);var A=_r(t),e=qr(A).concat(Us(A));return ps(e,(function(t){Lr&&!Gr(Ys,A,t)||Ls(g,t,A[t])})),g},Ys=function(g){var t=Kr(g),A=Gr(Ps,this,t);return!(this===ws&&Wr(Bs,t)&&!Wr(zs,t))&&(!(A||!Wr(this,t)||!Wr(Bs,t)||Wr(this,fs)&&this[fs][t])||A)},Ws=function(g,t){var A=_r(g),e=Kr(t);if(A!==ws||!Wr(Bs,e)||Wr(zs,e)){var C=Ds(A,e);return!C||!Wr(Bs,e)||Wr(A,fs)&&A[fs][e]||(C.enumerable=!0),C}},Qs=function(g){var t=Rs(_r(g)),A=[];return ps(t,(function(g){Wr(Bs,g)||Wr(rs,g)||Ms(A,g)})),A},Us=function(g){var t=g===ws,A=Rs(t?zs:_r(g)),e=[];return ps(A,(function(g){!Wr(Bs,g)||t&&!Wr(ws,g)||Ms(e,Bs[g])})),e};Vr||(ks=function(){if(Qr(xs,this))throw new Os("Symbol is not a constructor");var g=arguments.length&&void 0!==arguments[0]?Hr(arguments[0]):void 0,t=ss(g),A=function(g){this===ws&&Gr(A,zs,g),Wr(this,fs)&&Wr(this[fs],t)&&(this[fs][t]=!1);var e=Xr(1,g);try{Gs(this,t,e)}catch(g){if(!(g instanceof Es))throw g;Fs(this,t,e)}};return Lr&&Zs&&Gs(ws,t,{configurable:!0,set:A}),js(t,g)},is(xs=ks[ys],"toString",(function(){return bs(this).tag})),is(ks,"withoutSetter",(function(g){return js(ss(g),g)})),Is.f=Ys,es.f=Ls,Cs.f=Vs,As.f=Ws,$r.f=gs.f=Qs,ts.f=Us,ds.f=function(g){return js(as(g),g)},Lr&&os(xs,"description",{configurable:!0,get:function(){return bs(this).description}})),Zr({global:!0,constructor:!0,wrap:!0,forced:!Vr,sham:!Vr},{Symbol:ks}),ps(qr(Ss),(function(g){hs(g)})),Zr({target:vs,stat:!0,forced:!Vr},{useSetter:function(){Zs=!0},useSimple:function(){Zs=!1}}),Zr({target:"Object",stat:!0,forced:!Vr,sham:!Lr},{create:function(g,t){return void 0===t?Jr(g):Vs(Jr(g),t)},defineProperty:Ls,defineProperties:Vs,getOwnPropertyDescriptor:Ws}),Zr({target:"Object",stat:!0,forced:!Vr},{getOwnPropertyNames:Qs}),ls(),cs(ks,vs),rs[fs]=!0;var _s=vg&&!!Symbol.for&&!!Symbol.keyFor,Ks=TA,Hs=ig,Xs=qg,Js=sC,qs=Ug,$s=_s,ga=qs("string-to-symbol-registry"),ta=qs("symbol-to-string-registry");Ks({target:"Symbol",stat:!0,forced:!$s},{for:function(g){var t=Js(g);if(Xs(ga,t))return ga[t];var A=Hs("Symbol")(t);return ga[t]=A,ta[A]=t,A}});var Aa=TA,ea=qg,Ca=xg,Ia=Og,ia=_s,oa=Ug("symbol-to-string-registry");Aa({target:"Symbol",stat:!0,forced:!ia},{keyFor:function(g){if(!Ca(g))throw new TypeError(Ia(g)+" is not a symbol");if(ea(oa,g))return oa[g]}});var na=En,ra=O,sa=y,aa=sC,da=u([].push),ha=TA,la=ig,ca=a,ua=P,pa=u,fa=I,va=O,ya=xg,ma=ve,ba=function(g){if(ra(g))return g;if(na(g)){for(var t=g.length,A=[],e=0;eg.length)&&(t=g.length);for(var A=0,e=new Array(t);A1?arguments[1]:void 0)}});var Mh=Me("Array").map,Bh=og,zh=Mh,Sh=Array.prototype,Zh=function(g){var t=g.map;return g===Sh||Bh(Sh,g)&&t===Sh.map?zh:t},Fh=A(Zh),Gh=Hg,jh=Ae;TA({target:"Object",stat:!0,forced:I((function(){jh(1)}))},{keys:function(g){return jh(Gh(g))}});var Lh=A(tg.Object.keys),Vh=TA,Yh=Date,Wh=u(Yh.prototype.getTime);Vh({target:"Date",stat:!0},{now:function(){return Wh(new Yh)}});var Qh=A(tg.Date.now),Uh=I,_h=function(g,t){var A=[][g];return!!A&&Uh((function(){A.call(null,t||function(){return 1},1)}))},Kh=Sr.forEach,Hh=_h("forEach")?[].forEach:function(g){return Kh(this,g,arguments.length>1?arguments[1]:void 0)};TA({target:"Array",proto:!0,forced:[].forEach!==Hh},{forEach:Hh});var Xh=Me("Array").forEach,Jh=oC,qh=qg,$h=og,gl=Xh,tl=Array.prototype,Al={DOMTokenList:!0,NodeList:!0},el=function(g){var t=g.forEach;return g===tl||$h(tl,g)&&t===tl.forEach||qh(Al,Jh(g))?gl:t},Cl=A(el),Il=TA,il=En,ol=u([].reverse),nl=[1,2];Il({target:"Array",proto:!0,forced:String(nl)===String(nl.reverse())},{reverse:function(){return il(this)&&(this.length=this.length),ol(this)}});var rl=Me("Array").reverse,sl=og,al=rl,dl=Array.prototype,hl=function(g){var t=g.reverse;return g===dl||sl(dl,g)&&t===dl.reverse?al:t},ll=hl,cl=A(ll),ul=Og,pl=TypeError,fl=function(g,t){if(!delete g[t])throw new pl("Cannot delete property "+ul(t)+" of "+ul(g))},vl=TA,yl=Hg,ml=SA,bl=PA,wl=jA,kl=Rd,xl=Tn,El=zn,Ol=lo,Tl=fl,Dl=Gn("splice"),Nl=Math.max,Rl=Math.min;vl({target:"Array",proto:!0,forced:!Dl},{splice:function(g,t){var A,e,C,I,i,o,n=yl(this),r=wl(n),s=ml(g,r),a=arguments.length;for(0===a?A=e=0:1===a?(A=0,e=r-s):(A=a-2,e=Rl(Nl(bl(t),0),r-s)),xl(r+A-e),C=El(n,e),I=0;Ir-e+A;I--)Tl(n,I-1)}else if(A>e)for(I=r-e;I>s;I--)o=I+A-1,(i=I+e-1)in n?n[o]=n[i]:Tl(n,o);for(I=0;I1?arguments[1]:void 0)}});var Gl=Me("Array").includes,jl=gg,Ll=y,Vl=dt("match"),Yl=function(g){var t;return jl(g)&&(void 0!==(t=g[Vl])?!!t:"RegExp"===Ll(g))},Wl=TypeError,Ql=dt("match"),Ul=TA,_l=function(g){if(Yl(g))throw new Wl("The method doesn't accept regular expressions");return g},Kl=K,Hl=sC,Xl=function(g){var t=/./;try{"/./"[g](t)}catch(A){try{return t[Ql]=!1,"/./"[g](t)}catch(g){}}return!1},Jl=u("".indexOf);Ul({target:"String",proto:!0,forced:!Xl("includes")},{includes:function(g){return!!~Jl(Hl(Kl(this)),Hl(_l(g)),arguments.length>1?arguments[1]:void 0)}});var ql=Me("String").includes,$l=og,gc=Gl,tc=ql,Ac=Array.prototype,ec=String.prototype,Cc=function(g){var t=g.includes;return g===Ac||$l(Ac,g)&&t===Ac.includes?gc:"string"==typeof g||g===ec||$l(ec,g)&&t===ec.includes?tc:t},Ic=A(Cc),ic=Hg,oc=EI,nc=fI;TA({target:"Object",stat:!0,forced:I((function(){oc(1)})),sham:!nc},{getPrototypeOf:function(g){return oc(ic(g))}});var rc=tg.Object.getPrototypeOf,sc=A(rc),ac=Sr.filter;TA({target:"Array",proto:!0,forced:!Gn("filter")},{filter:function(g){return ac(this,g,arguments.length>1?arguments[1]:void 0)}});var dc=Me("Array").filter,hc=og,lc=dc,cc=Array.prototype,uc=function(g){var t=g.filter;return g===cc||hc(cc,g)&&t===cc.filter?lc:t},pc=A(uc),fc="\t\n\v\f\r                　\u2028\u2029\ufeff",vc=K,yc=sC,mc=fc,bc=u("".replace),wc=RegExp("^["+mc+"]+"),kc=RegExp("(^|[^"+mc+"])["+mc+"]+$"),xc=function(g){return function(t){var A=yc(vc(t));return 1&g&&(A=bc(A,wc,"")),2&g&&(A=bc(A,kc,"$1")),A}},Ec={start:xc(1),end:xc(2),trim:xc(3)},Oc=C,Tc=I,Dc=u,Nc=sC,Rc=Ec.trim,Pc=fc,Mc=Oc.parseInt,Bc=Oc.Symbol,zc=Bc&&Bc.iterator,Sc=/^[+-]?0x/i,Zc=Dc(Sc.exec),Fc=8!==Mc(Pc+"08")||22!==Mc(Pc+"0x16")||zc&&!Tc((function(){Mc(Object(zc))}))?function(g,t){var A=Rc(Nc(g));return Mc(A,t>>>0||(Zc(Sc,A)?16:10))}:Mc;TA({global:!0,forced:parseInt!==Fc},{parseInt:Fc});var Gc=A(tg.parseInt),jc=TA,Lc=QA.indexOf,Vc=_h,Yc=w([].indexOf),Wc=!!Yc&&1/Yc([1],1,-0)<0;jc({target:"Array",proto:!0,forced:Wc||!Vc("indexOf")},{indexOf:function(g){var t=arguments.length>1?arguments[1]:void 0;return Wc?Yc(this,g,t)||0:Lc(this,g,t)}});var Qc=Me("Array").indexOf,Uc=og,_c=Qc,Kc=Array.prototype,Hc=function(g){var t=g.indexOf;return g===Kc||Uc(Kc,g)&&t===Kc.indexOf?_c:t},Xc=A(Hc);TA({target:"Object",stat:!0,sham:!D},{create:pI});var Jc=tg.Object,qc=function(g,t){return Jc.create(g,t)},$c=A(qc),gu=tg,tu=a;gu.JSON||(gu.JSON={stringify:JSON.stringify});var Au=function(g,t,A){return tu(gu.JSON.stringify,null,arguments)},eu=A(Au),Cu="function"==typeof Bun&&Bun&&"string"==typeof Bun.version,Iu=TypeError,iu=function(g,t){if(gA,i=ru(e)?e:lu(e),o=I?du(arguments,A):[],n=I?function(){nu(i,this,o)}:i;return t?g(n,C):g(n)}:g},pu=TA,fu=C,vu=uu(fu.setInterval,!0);pu({global:!0,bind:!0,forced:fu.setInterval!==vu},{setInterval:vu});var yu=TA,mu=C,bu=uu(mu.setTimeout,!0);yu({global:!0,bind:!0,forced:mu.setTimeout!==bu},{setTimeout:bu});var wu=A(tg.setTimeout),ku=Hg,xu=SA,Eu=jA,Ou=function(g){for(var t=ku(this),A=Eu(t),e=arguments.length,C=xu(e>1?arguments[1]:void 0,A),I=e>2?arguments[2]:void 0,i=void 0===I?A:xu(I,A);i>C;)t[C++]=g;return t};TA({target:"Array",proto:!0},{fill:Ou});var Tu,Du=Me("Array").fill,Nu=og,Ru=Du,Pu=Array.prototype,Mu=function(g){var t=g.fill;return g===Pu||Nu(Pu,g)&&t===Pu.fill?Ru:t},Bu=A(Mu); +/*! Hammer.JS - v2.0.17-rc - 2019-12-16 + * http://naver.github.io/egjs + * + * Forked By Naver egjs + * Copyright (c) hammerjs + * Licensed under the MIT license */ +function zu(){return zu=Object.assign||function(g){for(var t=1;t-1}var wp=function(){function g(g,t){this.manager=g,this.set(t)}var t=g.prototype;return t.set=function(g){g===Ku&&(g=this.compute()),_u&&this.manager.element.style&&gp[g]&&(this.manager.element.style[Uu]=g),this.actions=g.toLowerCase().trim()},t.update=function(){this.set(this.manager.options.touchAction)},t.compute=function(){var g=[];return yp(this.manager.recognizers,(function(t){mp(t.options.enable,[t])&&(g=g.concat(t.getTouchAction()))})),function(g){if(bp(g,Ju))return Ju;var t=bp(g,qu),A=bp(g,$u);return t&&A?Ju:t||A?t?qu:$u:bp(g,Xu)?Xu:Hu}(g.join(" "))},t.preventDefaults=function(g){var t=g.srcEvent,A=g.offsetDirection;if(this.manager.session.prevented)t.preventDefault();else{var e=this.actions,C=bp(e,Ju)&&!gp[Ju],I=bp(e,$u)&&!gp[$u],i=bp(e,qu)&&!gp[qu];if(C){var o=1===g.pointers.length,n=g.distance<2,r=g.deltaTime<250;if(o&&n&&r)return}if(!i||!I)return C||I&&A&cp||i&&A&up?this.preventSrc(t):void 0}},t.preventSrc=function(g){this.manager.session.prevented=!0,g.preventDefault()},g}();function kp(g,t){for(;g;){if(g===t)return!0;g=g.parentNode}return!1}function xp(g){var t=g.length;if(1===t)return{x:Vu(g[0].clientX),y:Vu(g[0].clientY)};for(var A=0,e=0,C=0;C=Yu(t)?g<0?ap:dp:t<0?hp:lp}function Np(g,t,A){return{x:t/g||0,y:A/g||0}}function Rp(g,t){var A=g.session,e=t.pointers,C=e.length;A.firstInput||(A.firstInput=Ep(t)),C>1&&!A.firstMultiple?A.firstMultiple=Ep(t):1===C&&(A.firstMultiple=!1);var I=A.firstInput,i=A.firstMultiple,o=i?i.center:I.center,n=t.center=xp(e);t.timeStamp=Wu(),t.deltaTime=t.timeStamp-I.timeStamp,t.angle=Tp(o,n),t.distance=Op(o,n),function(g,t){var A=t.center,e=g.offsetDelta||{},C=g.prevDelta||{},I=g.prevInput||{};t.eventType!==op&&I.eventType!==np||(C=g.prevDelta={x:I.deltaX||0,y:I.deltaY||0},e=g.offsetDelta={x:A.x,y:A.y}),t.deltaX=C.x+(A.x-e.x),t.deltaY=C.y+(A.y-e.y)}(A,t),t.offsetDirection=Dp(t.deltaX,t.deltaY);var r,s,a=Np(t.deltaTime,t.deltaX,t.deltaY);t.overallVelocityX=a.x,t.overallVelocityY=a.y,t.overallVelocity=Yu(a.x)>Yu(a.y)?a.x:a.y,t.scale=i?(r=i.pointers,Op((s=e)[0],s[1],vp)/Op(r[0],r[1],vp)):1,t.rotation=i?function(g,t){return Tp(t[1],t[0],vp)+Tp(g[1],g[0],vp)}(i.pointers,e):0,t.maxPointers=A.prevInput?t.pointers.length>A.prevInput.maxPointers?t.pointers.length:A.prevInput.maxPointers:t.pointers.length,function(g,t){var A,e,C,I,i=g.lastInterval||t,o=t.timeStamp-i.timeStamp;if(t.eventType!==rp&&(o>ip||void 0===i.velocity)){var n=t.deltaX-i.deltaX,r=t.deltaY-i.deltaY,s=Np(o,n,r);e=s.x,C=s.y,A=Yu(s.x)>Yu(s.y)?s.x:s.y,I=Dp(n,r),g.lastInterval=t}else A=i.velocity,e=i.velocityX,C=i.velocityY,I=i.direction;t.velocity=A,t.velocityX=e,t.velocityY=C,t.direction=I}(A,t);var d,h=g.element,l=t.srcEvent;kp(d=l.composedPath?l.composedPath()[0]:l.path?l.path[0]:l.target,h)&&(h=d),t.target=h}function Pp(g,t,A){var e=A.pointers.length,C=A.changedPointers.length,I=t&op&&e-C==0,i=t&(np|rp)&&e-C==0;A.isFirst=!!I,A.isFinal=!!i,I&&(g.session={}),A.eventType=t,Rp(g,A),g.emit("hammer.input",A),g.recognize(A),g.session.prevInput=A}function Mp(g){return g.trim().split(/\s+/g)}function Bp(g,t,A){yp(Mp(t),(function(t){g.addEventListener(t,A,!1)}))}function zp(g,t,A){yp(Mp(t),(function(t){g.removeEventListener(t,A,!1)}))}function Sp(g){var t=g.ownerDocument||g;return t.defaultView||t.parentWindow||window}var Zp=function(){function g(g,t){var A=this;this.manager=g,this.callback=t,this.element=g.element,this.target=g.options.inputTarget,this.domHandler=function(t){mp(g.options.enable,[g])&&A.handler(t)},this.init()}var t=g.prototype;return t.handler=function(){},t.init=function(){this.evEl&&Bp(this.element,this.evEl,this.domHandler),this.evTarget&&Bp(this.target,this.evTarget,this.domHandler),this.evWin&&Bp(Sp(this.element),this.evWin,this.domHandler)},t.destroy=function(){this.evEl&&zp(this.element,this.evEl,this.domHandler),this.evTarget&&zp(this.target,this.evTarget,this.domHandler),this.evWin&&zp(Sp(this.element),this.evWin,this.domHandler)},g}();function Fp(g,t,A){if(g.indexOf&&!A)return g.indexOf(t);for(var e=0;eA[t]})):e.sort()),e}var Up={touchstart:op,touchmove:2,touchend:np,touchcancel:rp},_p=function(g){function t(){var A;return t.prototype.evTarget="touchstart touchmove touchend touchcancel",(A=g.apply(this,arguments)||this).targetIds={},A}return Su(t,g),t.prototype.handler=function(g){var t=Up[g.type],A=Kp.call(this,g,t);A&&this.callback(this.manager,t,{pointers:A[0],changedPointers:A[1],pointerType:Cp,srcEvent:g})},t}(Zp);function Kp(g,t){var A,e,C=Wp(g.touches),I=this.targetIds;if(t&(2|op)&&1===C.length)return I[C[0].identifier]=!0,[C,C];var i=Wp(g.changedTouches),o=[],n=this.target;if(e=C.filter((function(g){return kp(g.target,n)})),t===op)for(A=0;A-1&&e.splice(g,1)}),Jp)}}function $p(g,t){g&op?(this.primaryTouch=t.changedPointers[0].identifier,qp.call(this,t)):g&(np|rp)&&qp.call(this,t)}function gf(g){for(var t=g.srcEvent.clientX,A=g.srcEvent.clientY,e=0;e-1&&this.requireFail.splice(t,1),this},t.hasRequireFailures=function(){return this.requireFail.length>0},t.canRecognizeWith=function(g){return!!this.simultaneous[g.id]},t.emit=function(g){var t=this,A=this.state;function e(A){t.manager.emit(A,g)}A<8&&e(t.options.event+of(A)),e(t.options.event),g.additionalEvent&&e(g.additionalEvent),A>=8&&e(t.options.event+of(A))},t.tryEmit=function(g){if(this.canEmit())return this.emit(g);this.state=ef},t.canEmit=function(){for(var g=0;gt.threshold&&C&t.direction},A.attrTest=function(g){return sf.prototype.attrTest.call(this,g)&&(2&this.state||!(2&this.state)&&this.directionTest(g))},A.emit=function(t){this.pX=t.deltaX,this.pY=t.deltaY;var A=af(t.direction);A&&(t.additionalEvent=this.options.event+A),g.prototype.emit.call(this,t)},t}(sf),hf=function(g){function t(t){return void 0===t&&(t={}),g.call(this,zu({event:"swipe",threshold:10,velocity:.3,direction:cp|up,pointers:1},t))||this}Su(t,g);var A=t.prototype;return A.getTouchAction=function(){return df.prototype.getTouchAction.call(this)},A.attrTest=function(t){var A,e=this.options.direction;return e&(cp|up)?A=t.overallVelocity:e&cp?A=t.overallVelocityX:e&up&&(A=t.overallVelocityY),g.prototype.attrTest.call(this,t)&&e&t.offsetDirection&&t.distance>this.options.threshold&&t.maxPointers===this.options.pointers&&Yu(A)>this.options.velocity&&t.eventType&np},A.emit=function(g){var t=af(g.offsetDirection);t&&this.manager.emit(this.options.event+t,g),this.manager.emit(this.options.event,g)},t}(sf),lf=function(g){function t(t){return void 0===t&&(t={}),g.call(this,zu({event:"pinch",threshold:0,pointers:2},t))||this}Su(t,g);var A=t.prototype;return A.getTouchAction=function(){return[Ju]},A.attrTest=function(t){return g.prototype.attrTest.call(this,t)&&(Math.abs(t.scale-1)>this.options.threshold||2&this.state)},A.emit=function(t){if(1!==t.scale){var A=t.scale<1?"in":"out";t.additionalEvent=this.options.event+A}g.prototype.emit.call(this,t)},t}(sf),cf=function(g){function t(t){return void 0===t&&(t={}),g.call(this,zu({event:"rotate",threshold:0,pointers:2},t))||this}Su(t,g);var A=t.prototype;return A.getTouchAction=function(){return[Ju]},A.attrTest=function(t){return g.prototype.attrTest.call(this,t)&&(Math.abs(t.rotation)>this.options.threshold||2&this.state)},t}(sf),uf=function(g){function t(t){var A;return void 0===t&&(t={}),(A=g.call(this,zu({event:"press",pointers:1,time:251,threshold:9},t))||this)._timer=null,A._input=null,A}Su(t,g);var A=t.prototype;return A.getTouchAction=function(){return[Hu]},A.process=function(g){var t=this,A=this.options,e=g.pointers.length===A.pointers,C=g.distanceA.time;if(this._input=g,!C||!e||g.eventType&(np|rp)&&!I)this.reset();else if(g.eventType&op)this.reset(),this._timer=setTimeout((function(){t.state=8,t.tryEmit()}),A.time);else if(g.eventType&np)return 8;return ef},A.reset=function(){clearTimeout(this._timer)},A.emit=function(g){8===this.state&&(g&&g.eventType&np?this.manager.emit(this.options.event+"up",g):(this._input.timeStamp=Wu(),this.manager.emit(this.options.event,this._input)))},t}(nf),pf={domEvents:!1,touchAction:Ku,enable:!0,inputTarget:null,inputClass:null,cssProps:{userSelect:"none",touchSelect:"none",touchCallout:"none",contentZooming:"none",userDrag:"none",tapHighlightColor:"rgba(0,0,0,0)"}},ff=[[cf,{enable:!1}],[lf,{enable:!1},["rotate"]],[hf,{direction:cp}],[df,{direction:cp},["swipe"]],[rf],[rf,{event:"doubletap",taps:2},["tap"]],[uf]];function vf(g,t){var A,e=g.element;e.style&&(yp(g.options.cssProps,(function(C,I){A=Qu(e.style,I),t?(g.oldCssProps[A]=e.style[A],e.style[A]=C):e.style[A]=g.oldCssProps[A]||""})),t||(g.oldCssProps={}))}var yf=function(){function g(g,t){var A,e=this;this.options=Gu({},pf,t||{}),this.options.inputTarget=this.options.inputTarget||g,this.handlers={},this.session={},this.recognizers=[],this.oldCssProps={},this.element=g,this.input=new((A=this).options.inputClass||(Ap?Yp:ep?_p:tp?tf:Xp))(A,Pp),this.touchAction=new wp(this,this.options.touchAction),vf(this,!0),yp(this.options.recognizers,(function(g){var t=e.add(new g[0](g[1]));g[2]&&t.recognizeWith(g[2]),g[3]&&t.requireFailure(g[3])}),this)}var t=g.prototype;return t.set=function(g){return Gu(this.options,g),g.touchAction&&this.touchAction.update(),g.inputTarget&&(this.input.destroy(),this.input.target=g.inputTarget,this.input.init()),this},t.stop=function(g){this.session.stopped=g?2:1},t.recognize=function(g){var t=this.session;if(!t.stopped){var A;this.touchAction.preventDefaults(g);var e=this.recognizers,C=t.curRecognizer;(!C||C&&8&C.state)&&(t.curRecognizer=null,C=null);for(var I=0;I\s*\(/gm,"{anonymous}()@"):"Unknown Stack Trace",C=window.console&&(window.console.warn||window.console.log);return C&&C.call(window.console,e,A),g.apply(this,arguments)}}var xf=kf((function(g,t,A){for(var e=Object.keys(t),C=0;C=g.length?{done:!0}:{done:!1,value:g[e++]}},e:function(g){throw g},f:C}}throw new TypeError("Invalid attempt to iterate non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.")}var I,i=!0,o=!1;return{s:function(){A=A.call(g)},n:function(){var g=A.next();return i=g.done,g},e:function(g){o=!0,I=g},f:function(){try{i||null==A.return||A.return()}finally{if(o)throw I}}}}function Pf(g,t){(null==t||t>g.length)&&(t=g.length);for(var A=0,e=new Array(t);A2)return zf.apply(void 0,bh(e=[Bf(t[0],t[1])]).call(e,ch(wh(t).call(t,2))));var C=t[0],I=t[1];if(C instanceof Date&&I instanceof Date)return C.setTime(I.getTime()),C;var i,o=Rf(Nh(I));try{for(o.s();!(i=o.n()).done;){var n=i.value;Object.prototype.propertyIsEnumerable.call(I,n)&&(I[n]===Mf?delete C[n]:null===C[n]||null===I[n]||"object"!==yd(C[n])||"object"!==yd(I[n])||Rh(C[n])||Rh(I[n])?C[n]=Sf(I[n]):C[n]=zf(C[n],I[n]))}}catch(g){o.e(g)}finally{o.f()}return C}function Sf(g){return Rh(g)?Fh(g).call(g,(function(g){return Sf(g)})):"object"===yd(g)&&null!==g?g instanceof Date?new Date(g.getTime()):zf({},g):g}function Zf(g){for(var t=0,A=Lh(g);t>>0,g=(C*=g)>>>0,g+=4294967296*(C-=g)}return 2.3283064365386963e-10*(g>>>0)}}(),t=g(" "),A=g(" "),e=g(" "),C=0;C2&&void 0!==arguments[2]&&arguments[2];for(var e in g)if(void 0!==t[e])if(null===t[e]||"object"!==yd(t[e]))Kf(g,t,e,A);else{var C=g[e],I=t[e];_f(C)&&_f(I)&&Hf(C,I,A)}}function Xf(g,t,A){var e=arguments.length>3&&void 0!==arguments[3]&&arguments[3];if(Rh(A))throw new TypeError("Arrays are not supported by deepExtend");for(var C=0;C3&&void 0!==arguments[3]&&arguments[3];if(Rh(A))throw new TypeError("Arrays are not supported by deepExtend");for(var C in A)if(Object.prototype.hasOwnProperty.call(A,C)&&!Ic(g).call(g,C))if(A[C]&&A[C].constructor===Object)void 0===t[C]&&(t[C]={}),t[C].constructor===Object?qf(t[C],A[C]):Kf(t,A,C,e);else if(Rh(A[C])){t[C]=[];for(var I=0;I2&&void 0!==arguments[2]&&arguments[2],e=arguments.length>3&&void 0!==arguments[3]&&arguments[3];for(var C in t)if(Object.prototype.hasOwnProperty.call(t,C)||!0===A)if("object"===yd(t[C])&&null!==t[C]&&sc(t[C])===Object.prototype)void 0===g[C]?g[C]=qf({},t[C],A):"object"===yd(g[C])&&null!==g[C]&&sc(g[C])===Object.prototype?qf(g[C],t[C],A):Kf(g,t,C,e);else if(Rh(t[C])){var I;g[C]=wh(I=t[C]).call(I)}else Kf(g,t,C,e);return g}function $f(g,t){var A;return bh(A=[]).call(A,ch(g),[t])}function gv(g){return g.getBoundingClientRect().top}function tv(g,t){if(Rh(g))for(var A=g.length,e=0;e3&&void 0!==arguments[3]?arguments[3]:{},C=function(g){return null!=g},I=function(g){return null!==g&&"object"===yd(g)};if(!I(g))throw new Error("Parameter mergeTarget must be an object");if(!I(t))throw new Error("Parameter options must be an object");if(!C(A))throw new Error("Parameter option must have a value");if(!I(e))throw new Error("Parameter globalOptions must be an object");var i=t[A],o=I(e)&&!function(g){for(var t in g)if(Object.prototype.hasOwnProperty.call(g,t))return!1;return!0}(e)?e[A]:void 0,n=o?o.enabled:void 0;if(void 0!==i){if("boolean"==typeof i)return I(g[A])||(g[A]={}),void(g[A].enabled=i);if(null===i&&!I(g[A])){if(!C(o))return;g[A]=$c(o)}if(I(i)){var r=!0;void 0!==i.enabled?r=i.enabled:void 0!==n&&(r=o.enabled),function(g,t,A){I(g[A])||(g[A]={});var e=t[A],C=g[A];for(var i in e)Object.prototype.hasOwnProperty.call(e,i)&&(C[i]=e[i])}(g,t,A),g[A].enabled=r}}}var hv={linear:function(g){return g},easeInQuad:function(g){return g*g},easeOutQuad:function(g){return g*(2-g)},easeInOutQuad:function(g){return g<.5?2*g*g:(4-2*g)*g-1},easeInCubic:function(g){return g*g*g},easeOutCubic:function(g){return--g*g*g+1},easeInOutCubic:function(g){return g<.5?4*g*g*g:(g-1)*(2*g-2)*(2*g-2)+1},easeInQuart:function(g){return g*g*g*g},easeOutQuart:function(g){return 1- --g*g*g*g},easeInOutQuart:function(g){return g<.5?8*g*g*g*g:1-8*--g*g*g*g},easeInQuint:function(g){return g*g*g*g*g},easeOutQuint:function(g){return 1+--g*g*g*g*g},easeInOutQuint:function(g){return g<.5?16*g*g*g*g*g:1+16*--g*g*g*g*g}};function lv(g,t){var A;Rh(t)||(t=[t]);var e,C=Rf(g);try{for(C.s();!(e=C.n()).done;){var I=e.value;if(I){A=I[t[0]];for(var i=1;i0&&void 0!==arguments[0]?arguments[0]:1;cn(this,g),this.pixelRatio=t,this.generated=!1,this.centerCoordinates={x:144.5,y:144.5},this.r=289*.49,this.color={r:255,g:255,b:255,a:1},this.hueCircle=void 0,this.initialColor={r:255,g:255,b:255,a:1},this.previousColor=void 0,this.applied=!1,this.updateCallback=function(){},this.closeCallback=function(){},this._create()}return kd(g,[{key:"insertTo",value:function(g){void 0!==this.hammer&&(this.hammer.destroy(),this.hammer=void 0),this.container=g,this.container.appendChild(this.frame),this._bindHammer(),this._setSize()}},{key:"setUpdateCallback",value:function(g){if("function"!=typeof g)throw new Error("Function attempted to set as colorPicker update callback is not a function.");this.updateCallback=g}},{key:"setCloseCallback",value:function(g){if("function"!=typeof g)throw new Error("Function attempted to set as colorPicker closing callback is not a function.");this.closeCallback=g}},{key:"_isColorString",value:function(g){if("string"==typeof g)return cv[g]}},{key:"setColor",value:function(g){var t=!(arguments.length>1&&void 0!==arguments[1])||arguments[1];if("none"!==g){var A,e=this._isColorString(g);if(void 0!==e&&(g=e),!0===Uf(g)){if(!0===sv(g)){var C=g.substr(4).substr(0,g.length-5).split(",");A={r:C[0],g:C[1],b:C[2],a:1}}else if(!0===function(g){return Wf.test(g)}(g)){var I=g.substr(5).substr(0,g.length-6).split(",");A={r:I[0],g:I[1],b:I[2],a:I[3]}}else if(!0===rv(g)){var i=Av(g);A={r:i.r,g:i.g,b:i.b,a:1}}}else if(g instanceof Object&&void 0!==g.r&&void 0!==g.g&&void 0!==g.b){var o=void 0!==g.a?g.a:"1.0";A={r:g.r,g:g.g,b:g.b,a:o}}if(void 0===A)throw new Error("Unknown color passed to the colorPicker. Supported are strings: rgb, hex, rgba. Object: rgb ({r:r,g:g,b:b,[a:a]}). Supplied: "+eu(g));this._setColor(A,t)}}},{key:"show",value:function(){void 0!==this.closeCallback&&(this.closeCallback(),this.closeCallback=void 0),this.applied=!1,this.frame.style.display="block",this._generateHueCircle()}},{key:"_hide",value:function(){var g=this;!0===(!(arguments.length>0&&void 0!==arguments[0])||arguments[0])&&(this.previousColor=fe({},this.color)),!0===this.applied&&this.updateCallback(this.initialColor),this.frame.style.display="none",wu((function(){void 0!==g.closeCallback&&(g.closeCallback(),g.closeCallback=void 0)}),0)}},{key:"_save",value:function(){this.updateCallback(this.color),this.applied=!1,this._hide()}},{key:"_apply",value:function(){this.applied=!0,this.updateCallback(this.color),this._updatePicker(this.color)}},{key:"_loadLast",value:function(){void 0!==this.previousColor?this.setColor(this.previousColor,!1):alert("There is no last color to load...")}},{key:"_setColor",value:function(g){!0===(!(arguments.length>1&&void 0!==arguments[1])||arguments[1])&&(this.initialColor=fe({},g)),this.color=g;var t=iv(g.r,g.g,g.b),A=2*Math.PI,e=this.r*t.s,C=this.centerCoordinates.x+e*Math.sin(A*t.h),I=this.centerCoordinates.y+e*Math.cos(A*t.h);this.colorPickerSelector.style.left=C-.5*this.colorPickerSelector.clientWidth+"px",this.colorPickerSelector.style.top=I-.5*this.colorPickerSelector.clientHeight+"px",this._updatePicker(g)}},{key:"_setOpacity",value:function(g){this.color.a=g/100,this._updatePicker(this.color)}},{key:"_setBrightness",value:function(g){var t=iv(this.color.r,this.color.g,this.color.b);t.v=g/100;var A=ov(t.h,t.s,t.v);A.a=this.color.a,this.color=A,this._updatePicker()}},{key:"_updatePicker",value:function(){var g=arguments.length>0&&void 0!==arguments[0]?arguments[0]:this.color,t=iv(g.r,g.g,g.b),A=this.colorPickerCanvas.getContext("2d");void 0===this.pixelRation&&(this.pixelRatio=(window.devicePixelRatio||1)/(A.webkitBackingStorePixelRatio||A.mozBackingStorePixelRatio||A.msBackingStorePixelRatio||A.oBackingStorePixelRatio||A.backingStorePixelRatio||1)),A.setTransform(this.pixelRatio,0,0,this.pixelRatio,0,0);var e=this.colorPickerCanvas.clientWidth,C=this.colorPickerCanvas.clientHeight;A.clearRect(0,0,e,C),A.putImageData(this.hueCircle,0,0),A.fillStyle="rgba(0,0,0,"+(1-t.v)+")",A.circle(this.centerCoordinates.x,this.centerCoordinates.y,this.r),Bu(A).call(A),this.brightnessRange.value=100*t.v,this.opacityRange.value=100*g.a,this.initialColorDiv.style.backgroundColor="rgba("+this.initialColor.r+","+this.initialColor.g+","+this.initialColor.b+","+this.initialColor.a+")",this.newColorDiv.style.backgroundColor="rgba("+this.color.r+","+this.color.g+","+this.color.b+","+this.color.a+")"}},{key:"_setSize",value:function(){this.colorPickerCanvas.style.width="100%",this.colorPickerCanvas.style.height="100%",this.colorPickerCanvas.width=289*this.pixelRatio,this.colorPickerCanvas.height=289*this.pixelRatio}},{key:"_create",value:function(){var g,t,A,e;if(this.frame=document.createElement("div"),this.frame.className="vis-color-picker",this.colorPickerDiv=document.createElement("div"),this.colorPickerSelector=document.createElement("div"),this.colorPickerSelector.className="vis-selector",this.colorPickerDiv.appendChild(this.colorPickerSelector),this.colorPickerCanvas=document.createElement("canvas"),this.colorPickerDiv.appendChild(this.colorPickerCanvas),this.colorPickerCanvas.getContext){var C=this.colorPickerCanvas.getContext("2d");this.pixelRatio=(window.devicePixelRatio||1)/(C.webkitBackingStorePixelRatio||C.mozBackingStorePixelRatio||C.msBackingStorePixelRatio||C.oBackingStorePixelRatio||C.backingStorePixelRatio||1),this.colorPickerCanvas.getContext("2d").setTransform(this.pixelRatio,0,0,this.pixelRatio,0,0)}else{var I=document.createElement("DIV");I.style.color="red",I.style.fontWeight="bold",I.style.padding="10px",I.innerText="Error: your browser does not support HTML canvas",this.colorPickerCanvas.appendChild(I)}this.colorPickerDiv.className="vis-color",this.opacityDiv=document.createElement("div"),this.opacityDiv.className="vis-opacity",this.brightnessDiv=document.createElement("div"),this.brightnessDiv.className="vis-brightness",this.arrowDiv=document.createElement("div"),this.arrowDiv.className="vis-arrow",this.opacityRange=document.createElement("input");try{this.opacityRange.type="range",this.opacityRange.min="0",this.opacityRange.max="100"}catch(g){}this.opacityRange.value="100",this.opacityRange.className="vis-range",this.brightnessRange=document.createElement("input");try{this.brightnessRange.type="range",this.brightnessRange.min="0",this.brightnessRange.max="100"}catch(g){}this.brightnessRange.value="100",this.brightnessRange.className="vis-range",this.opacityDiv.appendChild(this.opacityRange),this.brightnessDiv.appendChild(this.brightnessRange);var i=this;this.opacityRange.onchange=function(){i._setOpacity(this.value)},this.opacityRange.oninput=function(){i._setOpacity(this.value)},this.brightnessRange.onchange=function(){i._setBrightness(this.value)},this.brightnessRange.oninput=function(){i._setBrightness(this.value)},this.brightnessLabel=document.createElement("div"),this.brightnessLabel.className="vis-label vis-brightness",this.brightnessLabel.innerText="brightness:",this.opacityLabel=document.createElement("div"),this.opacityLabel.className="vis-label vis-opacity",this.opacityLabel.innerText="opacity:",this.newColorDiv=document.createElement("div"),this.newColorDiv.className="vis-new-color",this.newColorDiv.innerText="new",this.initialColorDiv=document.createElement("div"),this.initialColorDiv.className="vis-initial-color",this.initialColorDiv.innerText="initial",this.cancelButton=document.createElement("div"),this.cancelButton.className="vis-button vis-cancel",this.cancelButton.innerText="cancel",this.cancelButton.onclick=je(g=this._hide).call(g,this,!1),this.applyButton=document.createElement("div"),this.applyButton.className="vis-button vis-apply",this.applyButton.innerText="apply",this.applyButton.onclick=je(t=this._apply).call(t,this),this.saveButton=document.createElement("div"),this.saveButton.className="vis-button vis-save",this.saveButton.innerText="save",this.saveButton.onclick=je(A=this._save).call(A,this),this.loadButton=document.createElement("div"),this.loadButton.className="vis-button vis-load",this.loadButton.innerText="load last",this.loadButton.onclick=je(e=this._loadLast).call(e,this),this.frame.appendChild(this.colorPickerDiv),this.frame.appendChild(this.arrowDiv),this.frame.appendChild(this.brightnessLabel),this.frame.appendChild(this.brightnessDiv),this.frame.appendChild(this.opacityLabel),this.frame.appendChild(this.opacityDiv),this.frame.appendChild(this.newColorDiv),this.frame.appendChild(this.initialColorDiv),this.frame.appendChild(this.cancelButton),this.frame.appendChild(this.applyButton),this.frame.appendChild(this.saveButton),this.frame.appendChild(this.loadButton)}},{key:"_bindHammer",value:function(){var g=this;this.drag={},this.pinch={},this.hammer=new Gf(this.colorPickerCanvas),this.hammer.get("pinch").set({enable:!0}),this.hammer.on("hammer.input",(function(t){t.isFirst&&g._moveSelector(t)})),this.hammer.on("tap",(function(t){g._moveSelector(t)})),this.hammer.on("panstart",(function(t){g._moveSelector(t)})),this.hammer.on("panmove",(function(t){g._moveSelector(t)})),this.hammer.on("panend",(function(t){g._moveSelector(t)}))}},{key:"_generateHueCircle",value:function(){if(!1===this.generated){var g=this.colorPickerCanvas.getContext("2d");void 0===this.pixelRation&&(this.pixelRatio=(window.devicePixelRatio||1)/(g.webkitBackingStorePixelRatio||g.mozBackingStorePixelRatio||g.msBackingStorePixelRatio||g.oBackingStorePixelRatio||g.backingStorePixelRatio||1)),g.setTransform(this.pixelRatio,0,0,this.pixelRatio,0,0);var t,A,e,C,I=this.colorPickerCanvas.clientWidth,i=this.colorPickerCanvas.clientHeight;g.clearRect(0,0,I,i),this.centerCoordinates={x:.5*I,y:.5*i},this.r=.49*I;var o,n=2*Math.PI/360,r=1/this.r;for(e=0;e<360;e++)for(C=0;C3&&void 0!==arguments[3]?arguments[3]:1,I=arguments.length>4&&void 0!==arguments[4]?arguments[4]:function(){return!1};cn(this,g),this.parent=t,this.changedOptions=[],this.container=A,this.allowCreation=!1,this.hideOption=I,this.options={},this.initialized=!1,this.popupCounter=0,this.defaultOptions={enabled:!1,filter:!0,container:void 0,showButton:!0},fe(this.options,this.defaultOptions),this.configureOptions=e,this.moduleOptions={},this.domElements=[],this.popupDiv={},this.popupLimit=5,this.popupHistory={},this.colorPicker=new uv(C),this.wrapper=void 0}return kd(g,[{key:"setOptions",value:function(g){if(void 0!==g){this.popupHistory={},this._removePopup();var t=!0;if("string"==typeof g)this.options.filter=g;else if(Rh(g))this.options.filter=g.join();else if("object"===yd(g)){if(null==g)throw new TypeError("options cannot be null");void 0!==g.container&&(this.options.container=g.container),void 0!==pc(g)&&(this.options.filter=pc(g)),void 0!==g.showButton&&(this.options.showButton=g.showButton),void 0!==g.enabled&&(t=g.enabled)}else"boolean"==typeof g?(this.options.filter=!0,t=g):"function"==typeof g&&(this.options.filter=g,t=!0);!1===pc(this.options)&&(t=!1),this.options.enabled=t}this._clean()}},{key:"setModuleOptions",value:function(g){this.moduleOptions=g,!0===this.options.enabled&&(this._clean(),void 0!==this.options.container&&(this.container=this.options.container),this._create())}},{key:"_create",value:function(){this._clean(),this.changedOptions=[];var g=pc(this.options),t=0,A=!1;for(var e in this.configureOptions)Object.prototype.hasOwnProperty.call(this.configureOptions,e)&&(this.allowCreation=!1,A=!1,"function"==typeof g?A=(A=g(e,[]))||this._handleObject(this.configureOptions[e],[e],!0):!0!==g&&-1===Xc(g).call(g,e)||(A=!0),!1!==A&&(this.allowCreation=!0,t>0&&this._makeItem([]),this._makeHeader(e),this._handleObject(this.configureOptions[e],[e])),t++);this._makeButton(),this._push()}},{key:"_push",value:function(){this.wrapper=document.createElement("div"),this.wrapper.className="vis-configuration-wrapper",this.container.appendChild(this.wrapper);for(var g=0;g1?A-1:0),C=1;C2&&void 0!==arguments[2]&&arguments[2],e=document.createElement("div");if(e.className="vis-configuration vis-config-label vis-config-s"+t.length,!0===A){for(;e.firstChild;)e.removeChild(e.firstChild);e.appendChild(pv("i","b",g))}else e.innerText=g+":";return e}},{key:"_makeDropdown",value:function(g,t,A){var e=document.createElement("select");e.className="vis-configuration vis-config-select";var C=0;void 0!==t&&-1!==Xc(g).call(g,t)&&(C=Xc(g).call(g,t));for(var I=0;II&&1!==I&&(o.max=Math.ceil(t*s),r=o.max,n="range increased"),o.value=t}else o.value=e;var a=document.createElement("input");a.className="vis-configuration vis-config-rangeinput",a.value=o.value;var d=this;o.onchange=function(){a.value=this.value,d._update(Number(this.value),A)},o.oninput=function(){a.value=this.value};var h=this._makeLabel(A[A.length-1],A),l=this._makeItem(A,h,o,a);""!==n&&this.popupHistory[l]!==r&&(this.popupHistory[l]=r,this._setupPopup(n,l))}},{key:"_makeButton",value:function(){var g=this;if(!0===this.options.showButton){var t=document.createElement("div");t.className="vis-configuration vis-config-button",t.innerText="generate options",t.onclick=function(){g._printOptions()},t.onmouseover=function(){t.className="vis-configuration vis-config-button hover"},t.onmouseout=function(){t.className="vis-configuration vis-config-button"},this.optionsContainer=document.createElement("div"),this.optionsContainer.className="vis-configuration vis-config-option-container",this.domElements.push(this.optionsContainer),this.domElements.push(t)}}},{key:"_setupPopup",value:function(g,t){var A=this;if(!0===this.initialized&&!0===this.allowCreation&&this.popupCounter1&&void 0!==arguments[1]?arguments[1]:[],A=arguments.length>2&&void 0!==arguments[2]&&arguments[2],e=!1,C=pc(this.options),I=!1;for(var i in g)if(Object.prototype.hasOwnProperty.call(g,i)){e=!0;var o=g[i],n=$f(t,i);if("function"==typeof C&&!1===(e=C(i,t))&&!Rh(o)&&"string"!=typeof o&&"boolean"!=typeof o&&o instanceof Object&&(this.allowCreation=!1,e=this._handleObject(o,n,!0),this.allowCreation=!1===A),!1!==e){I=!0;var r=this._getValue(n);if(Rh(o))this._handleArray(o,r,n);else if("string"==typeof o)this._makeTextInput(o,r,n);else if("boolean"==typeof o)this._makeCheckbox(o,r,n);else if(o instanceof Object){if(!this.hideOption(t,i,this.moduleOptions))if(void 0!==o.enabled){var s=$f(n,"enabled"),a=this._getValue(s);if(!0===a){var d=this._makeLabel(i,n,!0);this._makeItem(n,d),I=this._handleObject(o,n)||I}else this._makeCheckbox(o,a,n)}else{var h=this._makeLabel(i,n,!0);this._makeItem(n,h),I=this._handleObject(o,n)||I}}else console.error("dont know how to handle",o,i,n)}}return I}},{key:"_handleArray",value:function(g,t,A){"string"==typeof g[0]&&"color"===g[0]?(this._makeColorField(g,t,A),g[1]!==t&&this.changedOptions.push({path:A,value:t})):"string"==typeof g[0]?(this._makeDropdown(g,t,A),g[0]!==t&&this.changedOptions.push({path:A,value:t})):"number"==typeof g[0]&&(this._makeRange(g,t,A),g[0]!==t&&this.changedOptions.push({path:A,value:Number(t)}))}},{key:"_update",value:function(g,t){var A=this._constructOptions(g,t);this.parent.body&&this.parent.body.emitter&&this.parent.body.emitter.emit&&this.parent.body.emitter.emit("configChange",A),this.initialized=!0,this.parent.setOptions(A)}},{key:"_constructOptions",value:function(g,t){var A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{},e=A;g="false"!==(g="true"===g||g)&&g;for(var C=0;CC-this.padding&&(o=!0),I=o?this.x-A:this.x,i=n?this.y-t:this.y}else(i=this.y-t)+t+this.padding>e&&(i=e-t-this.padding),iC&&(I=C-A-this.padding),Ii.distance?" in "+g.printLocation(I.path,t,"")+"Perhaps it was misplaced? Matching option found at: "+g.printLocation(i.path,i.closestMatch,""):I.distance<=8?'. Did you mean "'+I.closestMatch+'"?'+g.printLocation(I.path,t):". Did you mean one of these: "+g.print(Lh(A))+g.printLocation(e,t),console.error('%cUnknown option detected: "'+t+'"'+C,bv),mv=!0}},{key:"findInOptions",value:function(t,A,e){var C,I=arguments.length>3&&void 0!==arguments[3]&&arguments[3],i=1e9,o="",n=[],r=t.toLowerCase(),s=void 0;for(var a in A){var d=void 0;if(void 0!==A[a].__type__&&!0===I){var h=g.findInOptions(t,A[a],$f(e,a));i>h.distance&&(o=h.closestMatch,n=h.path,i=h.distance,s=h.indexMatch)}else{var l;-1!==Xc(l=a.toLowerCase()).call(l,r)&&(s=a),i>(d=g.levenshteinDistance(t,a))&&(o=a,n=wh(C=e).call(C),i=d)}}return{closestMatch:o,path:n,distance:i,indexMatch:s}}},{key:"printLocation",value:function(g,t){for(var A="\n\n"+(arguments.length>2&&void 0!==arguments[2]?arguments[2]:"Problem value found at: \n")+"options = {\n",e=0;e":!0,"--":!0},zv="",Sv=0,Zv="",Fv="",Gv=Mv.NULL;function jv(){Sv++,Zv=zv.charAt(Sv)}function Lv(){return zv.charAt(Sv+1)}function Vv(g){var t=g.charCodeAt(0);return t<47?35===t||46===t:t<59?t>47:t<91?t>64:t<96?95===t:t<123&&t>96}function Yv(g,t){if(g||(g={}),t)for(var A in t)t.hasOwnProperty(A)&&(g[A]=t[A]);return g}function Wv(g,t,A){for(var e=t.split("."),C=g;e.length;){var I=e.shift();e.length?(C[I]||(C[I]={}),C=C[I]):C[I]=A}}function Qv(g,t){for(var A,e,C=null,I=[g],i=g;i.parent;)I.push(i.parent),i=i.parent;if(i.nodes)for(A=0,e=i.nodes.length;A=0;A--){var o,n=I[A];n.nodes||(n.nodes=[]),-1===Xc(o=n.nodes).call(o,C)&&n.nodes.push(C)}t.attr&&(C.attr=Yv(C.attr,t.attr))}function Uv(g,t){if(g.edges||(g.edges=[]),g.edges.push(t),g.edge){var A=Yv({},g.edge);t.attr=Yv(A,t.attr)}}function _v(g,t,A,e,C){var I={from:t,to:A,type:e};return g.edge&&(I.attr=Yv({},g.edge)),I.attr=Yv(I.attr||{},C),null!=C&&C.hasOwnProperty("arrows")&&null!=C.arrows&&(I.arrows={to:{enabled:!0,type:C.arrows.type}},C.arrows=null),I}function Kv(){for(Gv=Mv.NULL,Fv="";" "===Zv||"\t"===Zv||"\n"===Zv||"\r"===Zv;)jv();do{var g=!1;if("#"===Zv){for(var t=Sv-1;" "===zv.charAt(t)||"\t"===zv.charAt(t);)t--;if("\n"===zv.charAt(t)||""===zv.charAt(t)){for(;""!=Zv&&"\n"!=Zv;)jv();g=!0}}if("/"===Zv&&"/"===Lv()){for(;""!=Zv&&"\n"!=Zv;)jv();g=!0}if("/"===Zv&&"*"===Lv()){for(;""!=Zv;){if("*"===Zv&&"/"===Lv()){jv(),jv();break}jv()}g=!0}for(;" "===Zv||"\t"===Zv||"\n"===Zv||"\r"===Zv;)jv()}while(g);if(""!==Zv){var A=Zv+Lv();if(Bv[A])return Gv=Mv.DELIMITER,Fv=A,jv(),void jv();if(Bv[Zv])return Gv=Mv.DELIMITER,Fv=Zv,void jv();if(Vv(Zv)||"-"===Zv){for(Fv+=Zv,jv();Vv(Zv);)Fv+=Zv,jv();return"false"===Fv?Fv=!1:"true"===Fv?Fv=!0:isNaN(Number(Fv))||(Fv=Number(Fv)),void(Gv=Mv.IDENTIFIER)}if('"'===Zv){for(jv();""!=Zv&&('"'!=Zv||'"'===Zv&&'"'===Lv());)'"'===Zv?(Fv+=Zv,jv()):"\\"===Zv&&"n"===Lv()?(Fv+="\n",jv()):Fv+=Zv,jv();if('"'!=Zv)throw gy('End of string " expected');return jv(),void(Gv=Mv.IDENTIFIER)}for(Gv=Mv.UNKNOWN;""!=Zv;)Fv+=Zv,jv();throw new SyntaxError('Syntax error in part "'+ty(Fv,30)+'"')}Gv=Mv.DELIMITER}function Hv(g){for(;""!==Fv&&"}"!=Fv;)Xv(g),";"===Fv&&Kv()}function Xv(g){var t=Jv(g);if(t)qv(g,t);else{var A=function(g){if("node"===Fv)return Kv(),g.node=$v(),"node";if("edge"===Fv)return Kv(),g.edge=$v(),"edge";if("graph"===Fv)return Kv(),g.graph=$v(),"graph";return null}(g);if(!A){if(Gv!=Mv.IDENTIFIER)throw gy("Identifier expected");var e=Fv;if(Kv(),"="===Fv){if(Kv(),Gv!=Mv.IDENTIFIER)throw gy("Identifier expected");g[e]=Fv,Kv()}else!function(g,t){var A={id:t},e=$v();e&&(A.attr=e);Qv(g,A),qv(g,t)}(g,e)}}}function Jv(g){var t=null;if("subgraph"===Fv&&((t={}).type="subgraph",Kv(),Gv===Mv.IDENTIFIER&&(t.id=Fv,Kv())),"{"===Fv){if(Kv(),t||(t={}),t.parent=g,t.node=g.node,t.edge=g.edge,t.graph=g.graph,Hv(t),"}"!=Fv)throw gy("Angle bracket } expected");Kv(),delete t.node,delete t.edge,delete t.graph,delete t.parent,g.subgraphs||(g.subgraphs=[]),g.subgraphs.push(t)}return t}function qv(g,t){for(;"->"===Fv||"--"===Fv;){var A,e=Fv;Kv();var C=Jv(g);if(C)A=C;else{if(Gv!=Mv.IDENTIFIER)throw gy("Identifier or subgraph expected");Qv(g,{id:A=Fv}),Kv()}Uv(g,_v(g,t,A,e,$v())),t=A}}function $v(){for(var g,t,A=null,e={dashed:!0,solid:!1,dotted:[1,5]},C={dot:"circle",box:"box",crow:"crow",curve:"curve",icurve:"inv_curve",normal:"triangle",inv:"inv_triangle",diamond:"diamond",tee:"bar",vee:"vee"},I=new Array,i=new Array;"["===Fv;){for(Kv(),A={};""!==Fv&&"]"!=Fv;){if(Gv!=Mv.IDENTIFIER)throw gy("Attribute name expected");var o=Fv;if(Kv(),"="!=Fv)throw gy("Equal sign = expected");if(Kv(),Gv!=Mv.IDENTIFIER)throw gy("Attribute value expected");var n=Fv;"style"===o&&(n=e[n]),"arrowhead"===o&&(o="arrows",n={to:{enabled:!0,type:C[n]}}),"arrowtail"===o&&(o="arrows",n={from:{enabled:!0,type:C[n]}}),I.push({attr:A,name:o,value:n}),i.push(o),Kv(),","==Fv&&Kv()}if("]"!=Fv)throw gy("Bracket ] expected");Kv()}if(Ic(i).call(i,"dir")){var r={arrows:{}};for(g=0;g"===g.type&&(t.arrows="to"),t};Cl(C=A.edges).call(C,(function(g){var t,A,C,i,o,n,r;(t=g.from instanceof Object?g.from.nodes:{id:g.from},A=g.to instanceof Object?g.to.nodes:{id:g.to},g.from instanceof Object&&g.from.edges)&&Cl(C=g.from.edges).call(C,(function(g){var t=I(g);e.edges.push(t)}));(o=A,n=function(t,A){var C=_v(e,t.id,A.id,g.type,g.attr),i=I(C);e.edges.push(i)},Rh(i=t)?Cl(i).call(i,(function(g){Rh(o)?Cl(o).call(o,(function(t){n(g,t)})):n(g,o)})):Rh(o)?Cl(o).call(o,(function(g){n(i,g)})):n(i,o),g.to instanceof Object&&g.to.edges)&&Cl(r=g.to.edges).call(r,(function(g){var t=I(g);e.edges.push(t)}))}))}return A.attr&&(e.options=A.attr),e}var Iy=Object.freeze({__proto__:null,DOTToGraph:Cy,parseDOT:Nv});function iy(g,t){var A,e={edges:{inheritColor:!1},nodes:{fixed:!1,parseColor:!1}};null!=t&&(null!=t.fixed&&(e.nodes.fixed=t.fixed),null!=t.parseColor&&(e.nodes.parseColor=t.parseColor),null!=t.inheritColor&&(e.edges.inheritColor=t.inheritColor));var C=g.edges,I=Fh(C).call(C,(function(g){var t={from:g.source,id:g.id,to:g.target};return null!=g.attributes&&(t.attributes=g.attributes),null!=g.label&&(t.label=g.label),null!=g.attributes&&null!=g.attributes.title&&(t.title=g.attributes.title),"Directed"===g.type&&(t.arrows="to"),g.color&&!1===e.edges.inheritColor&&(t.color=g.color),t}));return{nodes:Fh(A=g.nodes).call(A,(function(g){var t={id:g.id,fixed:e.nodes.fixed&&null!=g.x&&null!=g.y};return null!=g.attributes&&(t.attributes=g.attributes),null!=g.label&&(t.label=g.label),null!=g.size&&(t.size=g.size),null!=g.attributes&&null!=g.attributes.title&&(t.title=g.attributes.title),null!=g.title&&(t.title=g.title),null!=g.x&&(t.x=g.x),null!=g.y&&(t.y=g.y),null!=g.color&&(!0===e.nodes.parseColor?t.color=g.color:t.color={background:g.color,border:g.color,highlight:{background:g.color,border:g.color},hover:{background:g.color,border:g.color}}),t})),edges:I}}var oy=Object.freeze({__proto__:null,parseGephi:iy}),ny=Object.freeze({__proto__:null,cn:{addDescription:"单击空白处放置新节点。",addEdge:"添加连接线",addNode:"添加节点",back:"返回",close:"關閉",createEdgeError:"无法将连接线连接到群集。",del:"删除选定",deleteClusterError:"无法删除群集。",edgeDescription:"单击某个节点并将该连接线拖动到另一个节点以连接它们。",edit:"编辑",editClusterError:"无法编辑群集。",editEdge:"编辑连接线",editEdgeDescription:"单击控制节点并将它们拖到节点上连接。",editNode:"编辑节点"},cs:{addDescription:"Kluknutím do prázdného prostoru můžete přidat nový vrchol.",addEdge:"Přidat hranu",addNode:"Přidat vrchol",back:"Zpět",close:"Zavřít",createEdgeError:"Nelze připojit hranu ke shluku.",del:"Smazat výběr",deleteClusterError:"Nelze mazat shluky.",edgeDescription:"Přetažením z jednoho vrcholu do druhého můžete spojit tyto vrcholy novou hranou.",edit:"Upravit",editClusterError:"Nelze upravovat shluky.",editEdge:"Upravit hranu",editEdgeDescription:"Přetažením kontrolního vrcholu hrany ji můžete připojit k jinému vrcholu.",editNode:"Upravit vrchol"},de:{addDescription:"Klicke auf eine freie Stelle, um einen neuen Knoten zu plazieren.",addEdge:"Kante hinzufügen",addNode:"Knoten hinzufügen",back:"Zurück",close:"Schließen",createEdgeError:"Es ist nicht möglich, Kanten mit Clustern zu verbinden.",del:"Lösche Auswahl",deleteClusterError:"Cluster können nicht gelöscht werden.",edgeDescription:"Klicke auf einen Knoten und ziehe die Kante zu einem anderen Knoten, um diese zu verbinden.",edit:"Editieren",editClusterError:"Cluster können nicht editiert werden.",editEdge:"Kante editieren",editEdgeDescription:"Klicke auf die Verbindungspunkte und ziehe diese auf einen Knoten, um sie zu verbinden.",editNode:"Knoten editieren"},en:{addDescription:"Click in an empty space to place a new node.",addEdge:"Add Edge",addNode:"Add Node",back:"Back",close:"Close",createEdgeError:"Cannot link edges to a cluster.",del:"Delete selected",deleteClusterError:"Clusters cannot be deleted.",edgeDescription:"Click on a node and drag the edge to another node to connect them.",edit:"Edit",editClusterError:"Clusters cannot be edited.",editEdge:"Edit Edge",editEdgeDescription:"Click on the control points and drag them to a node to connect to it.",editNode:"Edit Node"},es:{addDescription:"Haga clic en un lugar vacío para colocar un nuevo nodo.",addEdge:"Añadir arista",addNode:"Añadir nodo",back:"Atrás",close:"Cerrar",createEdgeError:"No se puede conectar una arista a un grupo.",del:"Eliminar selección",deleteClusterError:"No es posible eliminar grupos.",edgeDescription:"Haga clic en un nodo y arrastre la arista hacia otro nodo para conectarlos.",edit:"Editar",editClusterError:"No es posible editar grupos.",editEdge:"Editar arista",editEdgeDescription:"Haga clic en un punto de control y arrastrelo a un nodo para conectarlo.",editNode:"Editar nodo"},fr:{addDescription:"Cliquez dans un endroit vide pour placer un nœud.",addEdge:"Ajouter un lien",addNode:"Ajouter un nœud",back:"Retour",close:"Fermer",createEdgeError:"Impossible de créer un lien vers un cluster.",del:"Effacer la sélection",deleteClusterError:"Les clusters ne peuvent pas être effacés.",edgeDescription:"Cliquez sur un nœud et glissez le lien vers un autre nœud pour les connecter.",edit:"Éditer",editClusterError:"Les clusters ne peuvent pas être édités.",editEdge:"Éditer le lien",editEdgeDescription:"Cliquez sur les points de contrôle et glissez-les pour connecter un nœud.",editNode:"Éditer le nœud"},it:{addDescription:"Clicca per aggiungere un nuovo nodo",addEdge:"Aggiungi un vertice",addNode:"Aggiungi un nodo",back:"Indietro",close:"Chiudere",createEdgeError:"Non si possono collegare vertici ad un cluster",del:"Cancella la selezione",deleteClusterError:"I cluster non possono essere cancellati",edgeDescription:"Clicca su un nodo e trascinalo ad un altro nodo per connetterli.",edit:"Modifica",editClusterError:"I clusters non possono essere modificati.",editEdge:"Modifica il vertice",editEdgeDescription:"Clicca sui Punti di controllo e trascinali ad un nodo per connetterli.",editNode:"Modifica il nodo"},nl:{addDescription:"Klik op een leeg gebied om een nieuwe node te maken.",addEdge:"Link toevoegen",addNode:"Node toevoegen",back:"Terug",close:"Sluiten",createEdgeError:"Kan geen link maken naar een cluster.",del:"Selectie verwijderen",deleteClusterError:"Clusters kunnen niet worden verwijderd.",edgeDescription:"Klik op een node en sleep de link naar een andere node om ze te verbinden.",edit:"Wijzigen",editClusterError:"Clusters kunnen niet worden aangepast.",editEdge:"Link wijzigen",editEdgeDescription:"Klik op de verbindingspunten en sleep ze naar een node om daarmee te verbinden.",editNode:"Node wijzigen"},pt:{addDescription:"Clique em um espaço em branco para adicionar um novo nó",addEdge:"Adicionar aresta",addNode:"Adicionar nó",back:"Voltar",close:"Fechar",createEdgeError:"Não foi possível linkar arestas a um cluster.",del:"Remover selecionado",deleteClusterError:"Clusters não puderam ser removidos.",edgeDescription:"Clique em um nó e arraste a aresta até outro nó para conectá-los",edit:"Editar",editClusterError:"Clusters não puderam ser editados.",editEdge:"Editar aresta",editEdgeDescription:"Clique nos pontos de controle e os arraste para um nó para conectá-los",editNode:"Editar nó"},ru:{addDescription:"Кликните в свободное место, чтобы добавить новый узел.",addEdge:"Добавить ребро",addNode:"Добавить узел",back:"Назад",close:"Закрывать",createEdgeError:"Невозможно соединить ребра в кластер.",del:"Удалить выбранное",deleteClusterError:"Кластеры не могут быть удалены",edgeDescription:"Кликните на узел и протяните ребро к другому узлу, чтобы соединить их.",edit:"Редактировать",editClusterError:"Кластеры недоступны для редактирования.",editEdge:"Редактировать ребро",editEdgeDescription:"Кликните на контрольные точки и перетащите их в узел, чтобы подключиться к нему.",editNode:"Редактировать узел"},uk:{addDescription:"Kлікніть на вільне місце, щоб додати новий вузол.",addEdge:"Додати край",addNode:"Додати вузол",back:"Назад",close:"Закрити",createEdgeError:"Не можливо об'єднати краї в групу.",del:"Видалити обране",deleteClusterError:"Групи не можуть бути видалені.",edgeDescription:"Клікніть на вузол і перетягніть край до іншого вузла, щоб їх з'єднати.",edit:"Редагувати",editClusterError:"Групи недоступні для редагування.",editEdge:"Редагувати край",editEdgeDescription:"Клікніть на контрольні точки і перетягніть їх у вузол, щоб підключитися до нього.",editNode:"Редагувати вузол"}});var ry=function(){function g(){cn(this,g),this.NUM_ITERATIONS=4,this.image=new Image,this.canvas=document.createElement("canvas")}return kd(g,[{key:"init",value:function(){if(!this.initialized()){this.src=this.image.src;var g=this.image.width,t=this.image.height;this.width=g,this.height=t;var A=Math.floor(t/2),e=Math.floor(t/4),C=Math.floor(t/8),I=Math.floor(t/16),i=Math.floor(g/2),o=Math.floor(g/4),n=Math.floor(g/8),r=Math.floor(g/16);this.canvas.width=3*o,this.canvas.height=A,this.coordinates=[[0,0,i,A],[i,0,o,e],[i,e,n,C],[5*n,e,r,I]],this._fillMipMap()}}},{key:"initialized",value:function(){return void 0!==this.coordinates}},{key:"_fillMipMap",value:function(){var g=this.canvas.getContext("2d"),t=this.coordinates[0];g.drawImage(this.image,t[0],t[1],t[2],t[3]);for(var A=1;A2){t*=.5;for(var i=0;t>2&&i=this.NUM_ITERATIONS&&(i=this.NUM_ITERATIONS-1);var o=this.coordinates[i];g.drawImage(this.canvas,o[0],o[1],o[2],o[3],A,e,C,I)}else g.drawImage(this.image,A,e,C,I)}}]),g}(),sy=function(){function g(t){cn(this,g),this.images={},this.imageBroken={},this.callback=t}return kd(g,[{key:"_tryloadBrokenUrl",value:function(g,t,A){void 0!==g&&void 0!==A&&(void 0!==t?(A.image.onerror=function(){console.error("Could not load brokenImage:",t)},A.image.src=t):console.warn("No broken url image defined"))}},{key:"_redrawWithImage",value:function(g){this.callback&&this.callback(g)}},{key:"load",value:function(g,t){var A=this,e=this.images[g];if(e)return e;var C=new ry;return this.images[g]=C,C.image.onload=function(){A._fixImageCoordinates(C.image),C.init(),A._redrawWithImage(C)},C.image.onerror=function(){console.error("Could not load image:",g),A._tryloadBrokenUrl(g,t,C)},C.image.src=g,C}},{key:"_fixImageCoordinates",value:function(g){0===g.width&&(document.body.appendChild(g),g.width=g.offsetWidth,g.height=g.offsetHeight,document.body.removeChild(g))}}]),g}(),ay={exports:{}},dy=I((function(){if("function"==typeof ArrayBuffer){var g=new ArrayBuffer(8);Object.isExtensible(g)&&Object.defineProperty(g,"a",{value:8})}})),hy=I,ly=gg,cy=y,uy=dy,py=Object.isExtensible,fy=hy((function(){py(1)}))||uy?function(g){return!!ly(g)&&((!uy||"ArrayBuffer"!==cy(g))&&(!py||py(g)))}:py,vy=!I((function(){return Object.isExtensible(Object.preventExtensions({}))})),yy=TA,my=u,by=UA,wy=gg,ky=qg,xy=Jt.f,Ey=gr,Oy=er,Ty=fy,Dy=vy,Ny=!1,Ry=et("meta"),Py=0,My=function(g){xy(g,Ry,{value:{objectID:"O"+Py++,weakData:{}}})},By=ay.exports={enable:function(){By.enable=function(){},Ny=!0;var g=Ey.f,t=my([].splice),A={};A[Ry]=1,g(A).length&&(Ey.f=function(A){for(var e=g(A),C=0,I=e.length;CI;I++)if((o=p(g[I]))&&Vy(Ky,o))return o;return new _y(!1)}e=Yy(g,C)}for(n=d?g.next:e.next;!(r=Zy(n,e)).done;){try{o=p(r.value)}catch(g){Qy(e,"throw",g)}if("object"==typeof o&&o&&Vy(Ky,o))return o}return new _y(!1)},Xy=og,Jy=TypeError,qy=function(g,t){if(Xy(t,g))return g;throw new Jy("Incorrect invocation")},$y=TA,gm=C,tm=zy,Am=I,em=uA,Cm=Hy,Im=qy,im=O,om=gg,nm=Q,rm=_I,sm=Jt.f,am=Sr.forEach,dm=D,hm=GC.set,lm=GC.getterFor,cm=function(g,t,A){var e,C=-1!==g.indexOf("Map"),I=-1!==g.indexOf("Weak"),i=C?"set":"add",o=gm[g],n=o&&o.prototype,r={};if(dm&&im(o)&&(I||n.forEach&&!Am((function(){(new o).entries().next()})))){var s=(e=t((function(t,A){hm(Im(t,s),{type:g,collection:new o}),nm(A)||Cm(A,t[i],{that:t,AS_ENTRIES:C})}))).prototype,a=lm(g);am(["add","clear","delete","forEach","get","has","set","keys","values","entries"],(function(g){var t="add"===g||"set"===g;!(g in n)||I&&"clear"===g||em(s,g,(function(A,e){var C=a(this).collection;if(!t&&I&&!om(A))return"get"===g&&void 0;var i=C[g](0===A?0:A,e);return t?this:i}))})),I||sm(s,"size",{configurable:!0,get:function(){return a(this).collection.size}})}else e=A.getConstructor(t,g,C,i),tm.enable();return rm(e,g,!1,!0),r[g]=e,$y({global:!0,forced:!0},r),I||A.setStrong(e,g,C),e},um=TI,pm=function(g,t,A){for(var e in t)A&&A.unsafe&&g[e]?g[e]=t[e]:um(g,e,t[e],A);return g},fm=ig,vm=ur,ym=D,mm=dt("species"),bm=function(g){var t=fm(g);ym&&t&&!t[mm]&&vm(t,mm,{configurable:!0,get:function(){return this}})},wm=pI,km=ur,xm=pm,Em=Xt,Om=qy,Tm=Q,Dm=Hy,Nm=Ei,Rm=Oi,Pm=bm,Mm=D,Bm=zy.fastKey,zm=GC.set,Sm=GC.getterFor,Zm={getConstructor:function(g,t,A,e){var C=g((function(g,C){Om(g,I),zm(g,{type:t,index:wm(null),first:void 0,last:void 0,size:0}),Mm||(g.size=0),Tm(C)||Dm(C,g[e],{that:g,AS_ENTRIES:A})})),I=C.prototype,i=Sm(t),o=function(g,t,A){var e,C,I=i(g),o=n(g,t);return o?o.value=A:(I.last=o={index:C=Bm(t,!0),key:t,value:A,previous:e=I.last,next:void 0,removed:!1},I.first||(I.first=o),e&&(e.next=o),Mm?I.size++:g.size++,"F"!==C&&(I.index[C]=o)),g},n=function(g,t){var A,e=i(g),C=Bm(t);if("F"!==C)return e.index[C];for(A=e.first;A;A=A.next)if(A.key===t)return A};return xm(I,{clear:function(){for(var g=i(this),t=g.index,A=g.first;A;)A.removed=!0,A.previous&&(A.previous=A.previous.next=void 0),delete t[A.index],A=A.next;g.first=g.last=void 0,Mm?g.size=0:this.size=0},delete:function(g){var t=this,A=i(t),e=n(t,g);if(e){var C=e.next,I=e.previous;delete A.index[e.index],e.removed=!0,I&&(I.next=C),C&&(C.previous=I),A.first===e&&(A.first=C),A.last===e&&(A.last=I),Mm?A.size--:t.size--}return!!e},forEach:function(g){for(var t,A=i(this),e=Em(g,arguments.length>1?arguments[1]:void 0);t=t?t.next:A.first;)for(e(t.value,t.key,this);t&&t.removed;)t=t.previous},has:function(g){return!!n(this,g)}}),xm(I,A?{get:function(g){var t=n(this,g);return t&&t.value},set:function(g,t){return o(this,0===g?0:g,t)}}:{add:function(g){return o(this,g=0===g?0:g,g)}}),Mm&&km(I,"size",{configurable:!0,get:function(){return i(this).size}}),C},setStrong:function(g,t,A){var e=t+" Iterator",C=Sm(t),I=Sm(e);Nm(g,t,(function(g,t){zm(this,{type:e,target:g,state:C(g),kind:t,last:void 0})}),(function(){for(var g=I(this),t=g.kind,A=g.last;A&&A.removed;)A=A.previous;return g.target&&(g.last=A=A?A.next:g.state.first)?Rm("keys"===t?A.key:"values"===t?A.value:[A.key,A.value],!1):(g.target=void 0,Rm(void 0,!0))}),A?"entries":"values",!A,!0),Pm(t)}};cm("Map",(function(g){return function(){return g(this,arguments.length?arguments[0]:void 0)}}),Zm);var Fm=A(tg.Map),Gm=function(){function g(){cn(this,g),this.clear(),this._defaultIndex=0,this._groupIndex=0,this._defaultGroups=[{border:"#2B7CE9",background:"#97C2FC",highlight:{border:"#2B7CE9",background:"#D2E5FF"},hover:{border:"#2B7CE9",background:"#D2E5FF"}},{border:"#FFA500",background:"#FFFF00",highlight:{border:"#FFA500",background:"#FFFFA3"},hover:{border:"#FFA500",background:"#FFFFA3"}},{border:"#FA0A10",background:"#FB7E81",highlight:{border:"#FA0A10",background:"#FFAFB1"},hover:{border:"#FA0A10",background:"#FFAFB1"}},{border:"#41A906",background:"#7BE141",highlight:{border:"#41A906",background:"#A1EC76"},hover:{border:"#41A906",background:"#A1EC76"}},{border:"#E129F0",background:"#EB7DF4",highlight:{border:"#E129F0",background:"#F0B3F5"},hover:{border:"#E129F0",background:"#F0B3F5"}},{border:"#7C29F0",background:"#AD85E4",highlight:{border:"#7C29F0",background:"#D3BDF0"},hover:{border:"#7C29F0",background:"#D3BDF0"}},{border:"#C37F00",background:"#FFA807",highlight:{border:"#C37F00",background:"#FFCA66"},hover:{border:"#C37F00",background:"#FFCA66"}},{border:"#4220FB",background:"#6E6EFD",highlight:{border:"#4220FB",background:"#9B9BFD"},hover:{border:"#4220FB",background:"#9B9BFD"}},{border:"#FD5A77",background:"#FFC0CB",highlight:{border:"#FD5A77",background:"#FFD1D9"},hover:{border:"#FD5A77",background:"#FFD1D9"}},{border:"#4AD63A",background:"#C2FABC",highlight:{border:"#4AD63A",background:"#E6FFE3"},hover:{border:"#4AD63A",background:"#E6FFE3"}},{border:"#990000",background:"#EE0000",highlight:{border:"#BB0000",background:"#FF3333"},hover:{border:"#BB0000",background:"#FF3333"}},{border:"#FF6000",background:"#FF6000",highlight:{border:"#FF6000",background:"#FF6000"},hover:{border:"#FF6000",background:"#FF6000"}},{border:"#97C2FC",background:"#2B7CE9",highlight:{border:"#D2E5FF",background:"#2B7CE9"},hover:{border:"#D2E5FF",background:"#2B7CE9"}},{border:"#399605",background:"#255C03",highlight:{border:"#399605",background:"#255C03"},hover:{border:"#399605",background:"#255C03"}},{border:"#B70054",background:"#FF007E",highlight:{border:"#B70054",background:"#FF007E"},hover:{border:"#B70054",background:"#FF007E"}},{border:"#AD85E4",background:"#7C29F0",highlight:{border:"#D3BDF0",background:"#7C29F0"},hover:{border:"#D3BDF0",background:"#7C29F0"}},{border:"#4557FA",background:"#000EA1",highlight:{border:"#6E6EFD",background:"#000EA1"},hover:{border:"#6E6EFD",background:"#000EA1"}},{border:"#FFC0CB",background:"#FD5A77",highlight:{border:"#FFD1D9",background:"#FD5A77"},hover:{border:"#FFD1D9",background:"#FD5A77"}},{border:"#C2FABC",background:"#74D66A",highlight:{border:"#E6FFE3",background:"#74D66A"},hover:{border:"#E6FFE3",background:"#74D66A"}},{border:"#EE0000",background:"#990000",highlight:{border:"#FF3333",background:"#BB0000"},hover:{border:"#FF3333",background:"#BB0000"}}],this.options={},this.defaultOptions={useDefaultGroups:!0},fe(this.options,this.defaultOptions)}return kd(g,[{key:"setOptions",value:function(g){var t=["useDefaultGroups"];if(void 0!==g)for(var A in g)if(Object.prototype.hasOwnProperty.call(g,A)&&-1===Xc(t).call(t,A)){var e=g[A];this.add(A,e)}}},{key:"clear",value:function(){this._groups=new Fm,this._groupNames=[]}},{key:"get",value:function(g){var t=!(arguments.length>1&&void 0!==arguments[1])||arguments[1],A=this._groups.get(g);if(void 0===A&&t)if(!1===this.options.useDefaultGroups&&this._groupNames.length>0){var e=this._groupIndex%this._groupNames.length;++this._groupIndex,(A={}).color=this._groups.get(this._groupNames[e]),this._groups.set(g,A)}else{var C=this._defaultIndex%this._defaultGroups.length;this._defaultIndex++,(A={}).color=this._defaultGroups[C],this._groups.set(g,A)}return A}},{key:"add",value:function(g,t){return this._groups.has(g)||this._groupNames.push(g),this._groups.set(g,t),t}}]),g}();TA({target:"Number",stat:!0},{isNaN:function(g){return g!=g}});var jm=A(tg.Number.isNaN),Lm=C.isFinite,Vm=Number.isFinite||function(g){return"number"==typeof g&&Lm(g)};TA({target:"Number",stat:!0},{isFinite:Vm});var Ym=A(tg.Number.isFinite),Wm=Sr.some;TA({target:"Array",proto:!0,forced:!_h("some")},{some:function(g){return Wm(this,g,arguments.length>1?arguments[1]:void 0)}});var Qm=Me("Array").some,Um=og,_m=Qm,Km=Array.prototype,Hm=function(g){var t=g.some;return g===Km||Um(Km,g)&&t===Km.some?_m:t},Xm=A(Hm);function Jm(g){if(void 0===g)throw new ReferenceError("this hasn't been initialised - super() hasn't been called");return g}var qm=qc,$m=A(qm);TA({target:"Object",stat:!0},{setPrototypeOf:ri});var gb=tg.Object.setPrototypeOf,tb=A(gb),Ab=A(Ge);function eb(g,t){var A;return eb=tb?Ab(A=tb).call(A):function(g,t){return g.__proto__=t,g},eb(g,t)}function Cb(g,t){if("function"!=typeof t&&null!==t)throw new TypeError("Super expression must either be null or a function");g.prototype=$m(t&&t.prototype,{constructor:{value:g,writable:!0,configurable:!0}}),kn(g,"prototype",{writable:!1}),t&&eb(g,t)}function Ib(g,t){if(t&&("object"===yd(t)||"function"==typeof t))return t;if(void 0!==t)throw new TypeError("Derived constructors may only return object or undefined");return Jm(g)}var ib=rc,ob=A(ib);function nb(g){var t;return nb=tb?Ab(t=ob).call(t):function(g){return g.__proto__||ob(g)},nb(g)}function rb(g,t,A){return(t=bd(t))in g?kn(g,t,{value:A,enumerable:!0,configurable:!0,writable:!0}):g[t]=A,g}var sb={exports:{}},ab={exports:{}};!function(g){var t=cd,A=fd;function e(C){return g.exports=e="function"==typeof t&&"symbol"==typeof A?function(g){return typeof g}:function(g){return g&&"function"==typeof t&&g.constructor===t&&g!==t.prototype?"symbol":typeof g},g.exports.__esModule=!0,g.exports.default=g.exports,e(C)}g.exports=e,g.exports.__esModule=!0,g.exports.default=g.exports}(ab);var db=ab.exports,hb=el,lb=qg,cb=Dh,ub=T,pb=Jt,fb=gg,vb=uA,yb=Error,mb=u("".replace),bb=String(new yb("zxcasd").stack),wb=/\n\s*at [^:]*:[^\n]*/,kb=wb.test(bb),xb=G,Eb=!I((function(){var g=new Error("a");return!("stack"in g)||(Object.defineProperty(g,"stack",xb(1,7)),7!==g.stack)})),Ob=uA,Tb=function(g,t){if(kb&&"string"==typeof g&&!yb.prepareStackTrace)for(;t--;)g=mb(g,wb,"");return g},Db=Eb,Nb=Error.captureStackTrace,Rb=sC,Pb=TA,Mb=og,Bb=EI,zb=ri,Sb=function(g,t,A){for(var e=cb(t),C=pb.f,I=ub.f,i=0;i2&&jb(A,arguments[2]);var C=[];return Vb(g,Ub,{that:C}),Fb(A,"errors",C),A};zb?zb(_b,Qb):Sb(_b,Qb,{name:!0});var Kb=_b.prototype=Zb(Qb.prototype,{constructor:Gb(1,_b),message:Gb(1,""),name:Gb(1,"AggregateError")});Pb({global:!0,constructor:!0,arity:2},{AggregateError:_b});var Hb,Xb,Jb,qb,$b="process"===y(C.process),gw=ro,tw=Og,Aw=TypeError,ew=function(g){if(gw(g))return g;throw new Aw(tw(g)+" is not a constructor")},Cw=AA,Iw=ew,iw=Q,ow=dt("species"),nw=function(g,t){var A,e=Cw(g).constructor;return void 0===e||iw(A=Cw(e)[ow])?t:Iw(A)},rw=/(?:ipad|iphone|ipod).*applewebkit/i.test(ng),sw=C,aw=a,dw=Xt,hw=O,lw=qg,cw=I,uw=gI,pw=ve,fw=Et,vw=iu,yw=rw,mw=$b,bw=sw.setImmediate,ww=sw.clearImmediate,kw=sw.process,xw=sw.Dispatch,Ew=sw.Function,Ow=sw.MessageChannel,Tw=sw.String,Dw=0,Nw={},Rw="onreadystatechange";cw((function(){Hb=sw.location}));var Pw=function(g){if(lw(Nw,g)){var t=Nw[g];delete Nw[g],t()}},Mw=function(g){return function(){Pw(g)}},Bw=function(g){Pw(g.data)},zw=function(g){sw.postMessage(Tw(g),Hb.protocol+"//"+Hb.host)};bw&&ww||(bw=function(g){vw(arguments.length,1);var t=hw(g)?g:Ew(g),A=pw(arguments,1);return Nw[++Dw]=function(){aw(t,void 0,A)},Xb(Dw),Dw},ww=function(g){delete Nw[g]},mw?Xb=function(g){kw.nextTick(Mw(g))}:xw&&xw.now?Xb=function(g){xw.now(Mw(g))}:Ow&&!yw?(qb=(Jb=new Ow).port2,Jb.port1.onmessage=Bw,Xb=dw(qb.postMessage,qb)):sw.addEventListener&&hw(sw.postMessage)&&!sw.importScripts&&Hb&&"file:"!==Hb.protocol&&!cw(zw)?(Xb=zw,sw.addEventListener("message",Bw,!1)):Xb=Rw in fw("script")?function(g){uw.appendChild(fw("script"))[Rw]=function(){uw.removeChild(this),Pw(g)}}:function(g){setTimeout(Mw(g),0)});var Sw={set:bw,clear:ww},Zw=function(){this.head=null,this.tail=null};Zw.prototype={add:function(g){var t={item:g,next:null},A=this.tail;A?A.next=t:this.head=t,this.tail=t},get:function(){var g=this.head;if(g)return null===(this.head=g.next)&&(this.tail=null),g.item}};var Fw,Gw,jw,Lw,Vw,Yw=Zw,Ww=/ipad|iphone|ipod/i.test(ng)&&"undefined"!=typeof Pebble,Qw=/web0s(?!.*chrome)/i.test(ng),Uw=C,_w=Xt,Kw=T.f,Hw=Sw.set,Xw=Yw,Jw=rw,qw=Ww,$w=Qw,gk=$b,tk=Uw.MutationObserver||Uw.WebKitMutationObserver,Ak=Uw.document,ek=Uw.process,Ck=Uw.Promise,Ik=Kw(Uw,"queueMicrotask"),ik=Ik&&Ik.value;if(!ik){var ok=new Xw,nk=function(){var g,t;for(gk&&(g=ek.domain)&&g.exit();t=ok.get();)try{t()}catch(g){throw ok.head&&Fw(),g}g&&g.enter()};Jw||gk||$w||!tk||!Ak?!qw&&Ck&&Ck.resolve?((Lw=Ck.resolve(void 0)).constructor=Ck,Vw=_w(Lw.then,Lw),Fw=function(){Vw(nk)}):gk?Fw=function(){ek.nextTick(nk)}:(Hw=_w(Hw,Uw),Fw=function(){Hw(nk)}):(Gw=!0,jw=Ak.createTextNode(""),new tk(nk).observe(jw,{characterData:!0}),Fw=function(){jw.data=Gw=!Gw}),ik=function(g){ok.head||Fw(),ok.add(g)}}var rk=ik,sk=function(g){try{return{error:!1,value:g()}}catch(g){return{error:!0,value:g}}},ak=C.Promise,dk="object"==typeof Deno&&Deno&&"object"==typeof Deno.version,hk=!dk&&!$b&&"object"==typeof window&&"object"==typeof document,lk=C,ck=ak,uk=O,pk=Ut,fk=Hi,vk=dt,yk=hk,mk=dk,bk=cg,wk=ck&&ck.prototype,kk=vk("species"),xk=!1,Ek=uk(lk.PromiseRejectionEvent),Ok=pk("Promise",(function(){var g=fk(ck),t=g!==String(ck);if(!t&&66===bk)return!0;if(!wk.catch||!wk.finally)return!0;if(!bk||bk<51||!/native code/.test(g)){var A=new ck((function(g){g(1)})),e=function(g){g((function(){}),(function(){}))};if((A.constructor={})[kk]=e,!(xk=A.then((function(){}))instanceof e))return!0}return!t&&(yk||mk)&&!Ek})),Tk={CONSTRUCTOR:Ok,REJECTION_EVENT:Ek,SUBCLASSING:xk},Dk={},Nk=Rg,Rk=TypeError,Pk=function(g){var t,A;this.promise=new g((function(g,e){if(void 0!==t||void 0!==A)throw new Rk("Bad Promise constructor");t=g,A=e})),this.resolve=Nk(t),this.reject=Nk(A)};Dk.f=function(g){return new Pk(g)};var Mk,Bk,zk=TA,Sk=$b,Zk=C,Fk=P,Gk=TI,jk=_I,Lk=bm,Vk=Rg,Yk=O,Wk=gg,Qk=qy,Uk=nw,_k=Sw.set,Kk=rk,Hk=function(g,t){try{1===arguments.length?console.error(g):console.error(g,t)}catch(g){}},Xk=sk,Jk=Yw,qk=GC,$k=ak,gx=Tk,tx=Dk,Ax="Promise",ex=gx.CONSTRUCTOR,Cx=gx.REJECTION_EVENT,Ix=qk.getterFor(Ax),ix=qk.set,ox=$k&&$k.prototype,nx=$k,rx=ox,sx=Zk.TypeError,ax=Zk.document,dx=Zk.process,hx=tx.f,lx=hx,cx=!!(ax&&ax.createEvent&&Zk.dispatchEvent),ux="unhandledrejection",px=function(g){var t;return!(!Wk(g)||!Yk(t=g.then))&&t},fx=function(g,t){var A,e,C,I=t.value,i=1===t.state,o=i?g.ok:g.fail,n=g.resolve,r=g.reject,s=g.domain;try{o?(i||(2===t.rejection&&wx(t),t.rejection=1),!0===o?A=I:(s&&s.enter(),A=o(I),s&&(s.exit(),C=!0)),A===g.promise?r(new sx("Promise-chain cycle")):(e=px(A))?Fk(e,A,n,r):n(A)):r(I)}catch(g){s&&!C&&s.exit(),r(g)}},vx=function(g,t){g.notified||(g.notified=!0,Kk((function(){for(var A,e=g.reactions;A=e.get();)fx(A,g);g.notified=!1,t&&!g.rejection&&mx(g)})))},yx=function(g,t,A){var e,C;cx?((e=ax.createEvent("Event")).promise=t,e.reason=A,e.initEvent(g,!1,!0),Zk.dispatchEvent(e)):e={promise:t,reason:A},!Cx&&(C=Zk["on"+g])?C(e):g===ux&&Hk("Unhandled promise rejection",A)},mx=function(g){Fk(_k,Zk,(function(){var t,A=g.facade,e=g.value;if(bx(g)&&(t=Xk((function(){Sk?dx.emit("unhandledRejection",e,A):yx(ux,A,e)})),g.rejection=Sk||bx(g)?2:1,t.error))throw t.value}))},bx=function(g){return 1!==g.rejection&&!g.parent},wx=function(g){Fk(_k,Zk,(function(){var t=g.facade;Sk?dx.emit("rejectionHandled",t):yx("rejectionhandled",t,g.value)}))},kx=function(g,t,A){return function(e){g(t,e,A)}},xx=function(g,t,A){g.done||(g.done=!0,A&&(g=A),g.value=t,g.state=2,vx(g,!0))},Ex=function(g,t,A){if(!g.done){g.done=!0,A&&(g=A);try{if(g.facade===t)throw new sx("Promise can't be resolved itself");var e=px(t);e?Kk((function(){var A={done:!1};try{Fk(e,t,kx(Ex,A,g),kx(xx,A,g))}catch(t){xx(A,t,g)}})):(g.value=t,g.state=1,vx(g,!1))}catch(t){xx({done:!1},t,g)}}};ex&&(rx=(nx=function(g){Qk(this,rx),Vk(g),Fk(Mk,this);var t=Ix(this);try{g(kx(Ex,t),kx(xx,t))}catch(g){xx(t,g)}}).prototype,(Mk=function(g){ix(this,{type:Ax,done:!1,notified:!1,parent:!1,reactions:new Jk,rejection:!1,state:0,value:void 0})}).prototype=Gk(rx,"then",(function(g,t){var A=Ix(this),e=hx(Uk(this,nx));return A.parent=!0,e.ok=!Yk(g)||g,e.fail=Yk(t)&&t,e.domain=Sk?dx.domain:void 0,0===A.state?A.reactions.add(e):Kk((function(){fx(e,A)})),e.promise})),Bk=function(){var g=new Mk,t=Ix(g);this.promise=g,this.resolve=kx(Ex,t),this.reject=kx(xx,t)},tx.f=hx=function(g){return g===nx||undefined===g?new Bk(g):lx(g)}),zk({global:!0,constructor:!0,wrap:!0,forced:ex},{Promise:nx}),jk(nx,Ax,!1,!0),Lk(Ax);var Ox=ak,Tx=Tk.CONSTRUCTOR||!Yo((function(g){Ox.all(g).then(void 0,(function(){}))})),Dx=P,Nx=Rg,Rx=Dk,Px=sk,Mx=Hy;TA({target:"Promise",stat:!0,forced:Tx},{all:function(g){var t=this,A=Rx.f(t),e=A.resolve,C=A.reject,I=Px((function(){var A=Nx(t.resolve),I=[],i=0,o=1;Mx(g,(function(g){var n=i++,r=!1;o++,Dx(A,t,g).then((function(g){r||(r=!0,I[n]=g,--o||e(I))}),C)})),--o||e(I)}));return I.error&&C(I.value),A.promise}});var Bx=TA,zx=Tk.CONSTRUCTOR;ak&&ak.prototype,Bx({target:"Promise",proto:!0,forced:zx,real:!0},{catch:function(g){return this.then(void 0,g)}});var Sx=P,Zx=Rg,Fx=Dk,Gx=sk,jx=Hy;TA({target:"Promise",stat:!0,forced:Tx},{race:function(g){var t=this,A=Fx.f(t),e=A.reject,C=Gx((function(){var C=Zx(t.resolve);jx(g,(function(g){Sx(C,t,g).then(A.resolve,e)}))}));return C.error&&e(C.value),A.promise}});var Lx=P,Vx=Dk;TA({target:"Promise",stat:!0,forced:Tk.CONSTRUCTOR},{reject:function(g){var t=Vx.f(this);return Lx(t.reject,void 0,g),t.promise}});var Yx=AA,Wx=gg,Qx=Dk,Ux=function(g,t){if(Yx(g),Wx(t)&&t.constructor===g)return t;var A=Qx.f(g);return(0,A.resolve)(t),A.promise},_x=TA,Kx=ak,Hx=Tk.CONSTRUCTOR,Xx=Ux,Jx=ig("Promise"),qx=!Hx;_x({target:"Promise",stat:!0,forced:true},{resolve:function(g){return Xx(qx&&this===Jx?Kx:this,g)}});var $x=P,gE=Rg,tE=Dk,AE=sk,eE=Hy;TA({target:"Promise",stat:!0,forced:Tx},{allSettled:function(g){var t=this,A=tE.f(t),e=A.resolve,C=A.reject,I=AE((function(){var A=gE(t.resolve),C=[],I=0,i=1;eE(g,(function(g){var o=I++,n=!1;i++,$x(A,t,g).then((function(g){n||(n=!0,C[o]={status:"fulfilled",value:g},--i||e(C))}),(function(g){n||(n=!0,C[o]={status:"rejected",reason:g},--i||e(C))}))})),--i||e(C)}));return I.error&&C(I.value),A.promise}});var CE=P,IE=Rg,iE=ig,oE=Dk,nE=sk,rE=Hy,sE="No one promise resolved";TA({target:"Promise",stat:!0,forced:Tx},{any:function(g){var t=this,A=iE("AggregateError"),e=oE.f(t),C=e.resolve,I=e.reject,i=nE((function(){var e=IE(t.resolve),i=[],o=0,n=1,r=!1;rE(g,(function(g){var s=o++,a=!1;n++,CE(e,t,g).then((function(g){a||r||(r=!0,C(g))}),(function(g){a||r||(a=!0,i[s]=g,--n||I(new A(i,sE)))}))})),--n||I(new A(i,sE))}));return i.error&&I(i.value),e.promise}});var aE=TA,dE=ak,hE=I,lE=ig,cE=O,uE=nw,pE=Ux,fE=dE&&dE.prototype;aE({target:"Promise",proto:!0,real:!0,forced:!!dE&&hE((function(){fE.finally.call({then:function(){}},(function(){}))}))},{finally:function(g){var t=uE(this,lE("Promise")),A=cE(g);return this.then(A?function(A){return pE(t,g()).then((function(){return A}))}:g,A?function(A){return pE(t,g()).then((function(){throw A}))}:g)}});var vE=tg.Promise,yE=Dk;TA({target:"Promise",stat:!0},{withResolvers:function(){var g=yE.f(this);return{promise:g.promise,resolve:g.resolve,reject:g.reject}}});var mE=vE,bE=Dk,wE=sk;TA({target:"Promise",stat:!0,forced:!0},{try:function(g){var t=bE.f(this),A=wE(g);return(A.error?t.reject:t.resolve)(A.value),t.promise}});var kE=mE,xE=ll;!function(g){var t=db.default,A=wn,e=cd,C=qm,I=ib,i=hb,o=Ld,n=gb,r=kE,s=xE,a=rh;function d(){g.exports=d=function(){return l},g.exports.__esModule=!0,g.exports.default=g.exports;var h,l={},c=Object.prototype,u=c.hasOwnProperty,p=A||function(g,t,A){g[t]=A.value},f="function"==typeof e?e:{},v=f.iterator||"@@iterator",y=f.asyncIterator||"@@asyncIterator",m=f.toStringTag||"@@toStringTag";function b(g,t,e){return A(g,t,{value:e,enumerable:!0,configurable:!0,writable:!0}),g[t]}try{b({},"")}catch(h){b=function(g,t,A){return g[t]=A}}function w(g,t,A,e){var I=t&&t.prototype instanceof N?t:N,i=C(I.prototype),o=new V(e||[]);return p(i,"_invoke",{value:F(g,A,o)}),i}function k(g,t,A){try{return{type:"normal",arg:g.call(t,A)}}catch(g){return{type:"throw",arg:g}}}l.wrap=w;var x="suspendedStart",E="suspendedYield",O="executing",T="completed",D={};function N(){}function R(){}function P(){}var M={};b(M,v,(function(){return this}));var B=I&&I(I(Y([])));B&&B!==c&&u.call(B,v)&&(M=B);var z=P.prototype=N.prototype=C(M);function S(g){var t;i(t=["next","throw","return"]).call(t,(function(t){b(g,t,(function(g){return this._invoke(t,g)}))}))}function Z(g,A){function e(C,I,i,o){var n=k(g[C],g,I);if("throw"!==n.type){var r=n.arg,s=r.value;return s&&"object"==t(s)&&u.call(s,"__await")?A.resolve(s.__await).then((function(g){e("next",g,i,o)}),(function(g){e("throw",g,i,o)})):A.resolve(s).then((function(g){r.value=g,i(r)}),(function(g){return e("throw",g,i,o)}))}o(n.arg)}var C;p(this,"_invoke",{value:function(g,t){function I(){return new A((function(A,C){e(g,t,A,C)}))}return C=C?C.then(I,I):I()}})}function F(g,t,A){var e=x;return function(C,I){if(e===O)throw new Error("Generator is already running");if(e===T){if("throw"===C)throw I;return{value:h,done:!0}}for(A.method=C,A.arg=I;;){var i=A.delegate;if(i){var o=G(i,A);if(o){if(o===D)continue;return o}}if("next"===A.method)A.sent=A._sent=A.arg;else if("throw"===A.method){if(e===x)throw e=T,A.arg;A.dispatchException(A.arg)}else"return"===A.method&&A.abrupt("return",A.arg);e=O;var n=k(g,t,A);if("normal"===n.type){if(e=A.done?T:E,n.arg===D)continue;return{value:n.arg,done:A.done}}"throw"===n.type&&(e=T,A.method="throw",A.arg=n.arg)}}}function G(g,t){var A=t.method,e=g.iterator[A];if(e===h)return t.delegate=null,"throw"===A&&g.iterator.return&&(t.method="return",t.arg=h,G(g,t),"throw"===t.method)||"return"!==A&&(t.method="throw",t.arg=new TypeError("The iterator does not provide a '"+A+"' method")),D;var C=k(e,g.iterator,t.arg);if("throw"===C.type)return t.method="throw",t.arg=C.arg,t.delegate=null,D;var I=C.arg;return I?I.done?(t[g.resultName]=I.value,t.next=g.nextLoc,"return"!==t.method&&(t.method="next",t.arg=h),t.delegate=null,D):I:(t.method="throw",t.arg=new TypeError("iterator result is not an object"),t.delegate=null,D)}function j(g){var t,A={tryLoc:g[0]};1 in g&&(A.catchLoc=g[1]),2 in g&&(A.finallyLoc=g[2],A.afterLoc=g[3]),o(t=this.tryEntries).call(t,A)}function L(g){var t=g.completion||{};t.type="normal",delete t.arg,g.completion=t}function V(g){this.tryEntries=[{tryLoc:"root"}],i(g).call(g,j,this),this.reset(!0)}function Y(g){if(g||""===g){var A=g[v];if(A)return A.call(g);if("function"==typeof g.next)return g;if(!isNaN(g.length)){var e=-1,C=function t(){for(;++e=0;--e){var C=this.tryEntries[e],I=C.completion;if("root"===C.tryLoc)return A("end");if(C.tryLoc<=this.prev){var i=u.call(C,"catchLoc"),o=u.call(C,"finallyLoc");if(i&&o){if(this.prev=0;--A){var e=this.tryEntries[A];if(e.tryLoc<=this.prev&&u.call(e,"finallyLoc")&&this.prev=0;--t){var A=this.tryEntries[t];if(A.finallyLoc===g)return this.complete(A.completion,A.afterLoc),L(A),D}},catch:function(g){for(var t=this.tryEntries.length-1;t>=0;--t){var A=this.tryEntries[t];if(A.tryLoc===g){var e=A.completion;if("throw"===e.type){var C=e.arg;L(A)}return C}}throw new Error("illegal catch attempt")},delegateYield:function(g,t,A){return this.delegate={iterator:Y(g),resultName:t,nextLoc:A},"next"===this.method&&(this.arg=h),D}},l}g.exports=d,g.exports.__esModule=!0,g.exports.default=g.exports}(sb);var EE=(0,sb.exports)(),OE=EE;try{regeneratorRuntime=EE}catch(g){"object"==typeof globalThis?globalThis.regeneratorRuntime=EE:Function("r","regeneratorRuntime = r")(EE)}var TE=A(OE),DE=Rg,NE=Hg,RE=W,PE=jA,ME=TypeError,BE=function(g){return function(t,A,e,C){DE(A);var I=NE(t),i=RE(I),o=PE(I),n=g?o-1:0,r=g?-1:1;if(e<2)for(;;){if(n in i){C=i[n],n+=r;break}if(n+=r,g?n<0:o<=n)throw new ME("Reduce of empty array with no initial value")}for(;g?n>=0:o>n;n+=r)n in i&&(C=A(C,i[n],n,I));return C}},zE={left:BE(!1),right:BE(!0)}.left;TA({target:"Array",proto:!0,forced:!$b&&cg>79&&cg<83||!_h("reduce")},{reduce:function(g){var t=arguments.length;return zE(this,g,t,t>1?arguments[1]:void 0)}});var SE=Me("Array").reduce,ZE=og,FE=SE,GE=Array.prototype,jE=function(g){var t=g.reduce;return g===GE||ZE(GE,g)&&t===GE.reduce?FE:t},LE=A(jE),VE=En,YE=jA,WE=Tn,QE=Xt,UE=function(g,t,A,e,C,I,i,o){for(var n,r,s=C,a=0,d=!!i&&QE(i,o);a0&&VE(n)?(r=YE(n),s=UE(g,t,n,r,s,I-1)-1):(WE(s+1),g[s]=n),s++),a++;return s},_E=UE,KE=Rg,HE=Hg,XE=jA,JE=zn;TA({target:"Array",proto:!0},{flatMap:function(g){var t,A=HE(this),e=XE(A);return KE(g),(t=JE(A,0)).length=_E(t,A,A,e,0,1,g,arguments.length>1?arguments[1]:void 0),t}});var qE=Me("Array").flatMap,$E=og,gO=qE,tO=Array.prototype,AO=function(g){var t=g.flatMap;return g===tO||$E(tO,g)&&t===tO.flatMap?gO:t},eO=A(AO);cm("Set",(function(g){return function(){return g(this,arguments.length?arguments[0]:void 0)}}),Zm);var CO=A(tg.Set),IO=A(pd),iO=A(Oo),oO=rr,nO=Math.floor,rO=function(g,t){var A=g.length,e=nO(A/2);return A<8?sO(g,t):aO(g,rO(oO(g,0,e),t),rO(oO(g,e),t),t)},sO=function(g,t){for(var A,e,C=g.length,I=1;I0;)g[e]=g[--e];e!==I++&&(g[e]=A)}return g},aO=function(g,t,A,e){for(var C=t.length,I=A.length,i=0,o=0;i3)){if(DO)return!0;if(RO)return RO<603;var g,t,A,e,C="";for(g=65;g<76;g++){switch(t=String.fromCharCode(g),g){case 66:case 69:case 70:case 72:A=3;break;case 68:case 71:A=4;break;default:A=2}for(e=0;e<47;e++)PO.push({k:t+e,v:A})}for(PO.sort((function(g,t){return t.v-g.v})),e=0;ekO(A)?1:-1}}(g)),A=bO(C),e=0;eo;)void 0!==(A=C(e,t=I[o++]))&&KT(i,t,A);return i}});var HT=A(tg.Object.getOwnPropertyDescriptors),XT={exports:{}},JT=TA,qT=D,$T=UC.f;JT({target:"Object",stat:!0,forced:Object.defineProperties!==$T,sham:!qT},{defineProperties:$T});var gD=tg.Object,tD=XT.exports=function(g,t){return gD.defineProperties(g,t)};gD.defineProperties.sham&&(tD.sham=!0);var AD=A(XT.exports);let eD;const CD=new Uint8Array(16);function ID(){if(!eD&&(eD="undefined"!=typeof crypto&&crypto.getRandomValues&&crypto.getRandomValues.bind(crypto),!eD))throw new Error("crypto.getRandomValues() not supported. See https://github.com/uuidjs/uuid#getrandomvalues-not-supported");return eD(CD)}const iD=[];for(let g=0;g<256;++g)iD.push((g+256).toString(16).slice(1));var oD,nD={randomUUID:"undefined"!=typeof crypto&&crypto.randomUUID&&crypto.randomUUID.bind(crypto)};function rD(g,t,A){if(nD.randomUUID&&!t&&!g)return nD.randomUUID();const e=(g=g||{}).random||(g.rng||ID)();if(e[6]=15&e[6]|64,e[8]=63&e[8]|128,t){A=A||0;for(let g=0;g<16;++g)t[A+g]=e[g];return t}return function(g,t=0){return iD[g[t+0]]+iD[g[t+1]]+iD[g[t+2]]+iD[g[t+3]]+"-"+iD[g[t+4]]+iD[g[t+5]]+"-"+iD[g[t+6]]+iD[g[t+7]]+"-"+iD[g[t+8]]+iD[g[t+9]]+"-"+iD[g[t+10]]+iD[g[t+11]]+iD[g[t+12]]+iD[g[t+13]]+iD[g[t+14]]+iD[g[t+15]]}(e)}function sD(g,t){var A=Lh(g);if(BT){var e=BT(g);t&&(e=pc(e).call(e,(function(t){return WT(g,t).enumerable}))),A.push.apply(A,e)}return A}function aD(g){for(var t=1;t=g.length?{done:!0}:{done:!1,value:g[e++]}},e:function(g){throw g},f:C}}throw new TypeError("Invalid attempt to iterate non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.")}var I,i=!0,o=!1;return{s:function(){A=A.call(g)},n:function(){var g=A.next();return i=g.done,g},e:function(g){o=!0,I=g},f:function(){try{i||null==A.return||A.return()}finally{if(o)throw I}}}}function lD(g,t){(null==t||t>g.length)&&(t=g.length);for(var A=0,e=new Array(t);Athis.max&&this.flush(),null!=this._timeout&&(clearTimeout(this._timeout),this._timeout=null),this.queue.length>0&&"number"==typeof this.delay&&(this._timeout=wu((function(){g.flush()}),this.delay))}},{key:"flush",value:function(){var g,t;Cl(g=Zl(t=this._queue).call(t,0)).call(g,(function(g){g.fn.apply(g.context||g.fn,g.args||[])}))}}],[{key:"extend",value:function(t,A){var e=new g(A);if(void 0!==t.flush)throw new Error("Target object already has a property flush");t.flush=function(){e.flush()};var C=[{name:"flush",original:void 0}];if(A&&A.replace)for(var I=0;IC&&(C=n,e=o)}return e}},{key:"min",value:function(g){var t=iO(this._pairs),A=t.next();if(A.done)return null;for(var e=A.value[1],C=g(A.value[1],A.value[0]);!(A=t.next()).done;){var I=lh(A.value,2),i=I[0],o=I[1],n=g(o,i);n1?A-1:0),C=1;CC?1:eC)&&(e=i,C=o)}}catch(g){I.e(g)}finally{I.f()}return e||null}},{key:"min",value:function(g){var t,A,e=null,C=null,I=hD(nT(t=this._data).call(t));try{for(I.s();!(A=I.n()).done;){var i=A.value,o=i[g];"number"==typeof o&&(null==C||ot.x&&g.topt.y}function QD(g){return"string"==typeof g&&""!==g}function UD(g,t,A,e){var C=e.x,I=e.y;if("function"==typeof e.distanceToBorder){var i=e.distanceToBorder(g,t),o=Math.sin(t)*i,n=Math.cos(t)*i;n===i?(C+=i,I=e.y):o===i?(C=e.x,I-=i):(C+=n,I-=o)}else e.shape.width>e.shape.height?(C=e.x+.5*e.shape.width,I=e.y-A):(C=e.x+A,I=e.y-.5*e.shape.height);return{x:C,y:I}}var _D=function(){function g(t){cn(this,g),this.measureText=t,this.current=0,this.width=0,this.height=0,this.lines=[]}return kd(g,[{key:"_add",value:function(g,t){var A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:"normal";void 0===this.lines[g]&&(this.lines[g]={width:0,height:0,blocks:[]});var e=t;void 0!==t&&""!==t||(e=" ");var C=this.measureText(e,A),I=fe({},nT(C));I.text=t,I.width=C.width,I.mod=A,void 0!==t&&""!==t||(I.width=0),this.lines[g].blocks.push(I),this.lines[g].width+=I.width}},{key:"curWidth",value:function(){var g=this.lines[this.current];return void 0===g?0:g.width}},{key:"append",value:function(g){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:"normal";this._add(this.current,g,t)}},{key:"newLine",value:function(g){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:"normal";this._add(this.current,g,t),this.current++}},{key:"determineLineHeights",value:function(){for(var g=0;gg&&(g=e.width),t+=e.height}this.width=g,this.height=t}},{key:"removeEmptyBlocks",value:function(){for(var g=[],t=0;t"://,""://,""://,"":/<\/b>/,"":/<\/i>/,"":/<\/code>/,"*":/\*/,_:/_/,"`":/`/,afterBold:/[^*]/,afterItal:/[^_]/,afterMono:/[^`]/},HD=function(){function g(t){cn(this,g),this.text=t,this.bold=!1,this.ital=!1,this.mono=!1,this.spacing=!1,this.position=0,this.buffer="",this.modStack=[],this.blocks=[]}return kd(g,[{key:"mod",value:function(){return 0===this.modStack.length?"normal":this.modStack[0]}},{key:"modName",value:function(){return 0===this.modStack.length?"normal":"mono"===this.modStack[0]?"mono":this.bold&&this.ital?"boldital":this.bold?"bold":this.ital?"ital":void 0}},{key:"emitBlock",value:function(){this.spacing&&(this.add(" "),this.spacing=!1),this.buffer.length>0&&(this.blocks.push({text:this.buffer,mod:this.modName()}),this.buffer="")}},{key:"add",value:function(g){" "===g&&(this.spacing=!0),this.spacing&&(this.buffer+=" ",this.spacing=!1)," "!=g&&(this.buffer+=g)}},{key:"parseWS",value:function(g){return!!/[ \t]/.test(g)&&(this.mono?this.add(g):this.spacing=!0,!0)}},{key:"setTag",value:function(g){this.emitBlock(),this[g]=!0,this.modStack.unshift(g)}},{key:"unsetTag",value:function(g){this.emitBlock(),this[g]=!1,this.modStack.shift()}},{key:"parseStartTag",value:function(g,t){return!(this.mono||this[g]||!this.match(t))&&(this.setTag(g),!0)}},{key:"match",value:function(g){var t=!(arguments.length>1&&void 0!==arguments[1])||arguments[1],A=lh(this.prepareRegExp(g),2),e=A[0],C=A[1],I=e.test(this.text.substr(this.position,C));return I&&t&&(this.position+=C-1),I}},{key:"parseEndTag",value:function(g,t,A){var e=this.mod()===g;return!(!(e="mono"===g?e&&this.mono:e&&!this.mono)||!this.match(t))&&(void 0!==A?(this.position===this.text.length-1||this.match(A,!1))&&this.unsetTag(g):this.unsetTag(g),!0)}},{key:"replace",value:function(g,t){return!!this.match(g)&&(this.add(t),this.position+=length-1,!0)}},{key:"prepareRegExp",value:function(g){var t,A;if(g instanceof RegExp)A=g,t=1;else{var e=KD[g];A=void 0!==e?e:new RegExp(g),t=g.length}return[A,t]}}]),g}(),XD=function(){function g(t,A,e,C){var I=this;cn(this,g),this.ctx=t,this.parent=A,this.selected=e,this.hover=C;this.lines=new _D((function(g,A){if(void 0===g)return 0;var i=I.parent.getFormattingValues(t,e,C,A),o=0;""!==g&&(o=I.ctx.measureText(g).width);return{width:o,values:i}}))}return kd(g,[{key:"process",value:function(g){if(!QD(g))return this.lines.finalize();var t=this.parent.fontOptions;g=(g=g.replace(/\r\n/g,"\n")).replace(/\r/g,"\n");var A=String(g).split("\n"),e=A.length;if(t.multi)for(var C=0;C0)for(var i=0;i0)for(var d=0;d")||t.parseStartTag("ital","")||t.parseStartTag("mono","")||t.parseEndTag("bold","")||t.parseEndTag("ital","")||t.parseEndTag("mono",""))||A(e)||t.add(e),t.position++}return t.emitBlock(),t.blocks}},{key:"splitMarkdownBlocks",value:function(g){for(var t=this,A=new HD(g),e=!0,C=function(g){return!!/\\/.test(g)&&(A.positionthis.parent.fontOptions.maxWdt}},{key:"getLongestFit",value:function(g){for(var t="",A=0;A1&&void 0!==arguments[1]?arguments[1]:"normal",A=arguments.length>2&&void 0!==arguments[2]&&arguments[2];this.parent.getFormattingValues(this.ctx,this.selected,this.hover,t);for(var e=(g=(g=g.replace(/^( +)/g,"$1\r")).replace(/([^\r][^ ]*)( +)/g,"$1\r$2\r")).split("\r");e.length>0;){var C=this.getLongestFit(e);if(0===C){var I=e[0],i=this.getLongestFitWord(I);this.lines.newLine(wh(I).call(I,0,i),t),e[0]=wh(I).call(I,i)}else{var o=C;" "===e[C-1]?C--:" "===e[o]&&o++;var n=wh(e).call(e,0,C).join("");C==e.length&&A?this.lines.append(n,t):this.lines.newLine(n,t),e=wh(e).call(e,o)}}}}]),g}(),JD=["bold","ital","boldital","mono"],qD=function(){function g(t,A){var e=arguments.length>2&&void 0!==arguments[2]&&arguments[2];cn(this,g),this.body=t,this.pointToSelf=!1,this.baseSize=void 0,this.fontOptions={},this.setOptions(A),this.size={top:0,left:0,width:0,height:0,yLine:0},this.isEdgeLabel=e}return kd(g,[{key:"setOptions",value:function(g){if(this.elementOptions=g,this.initFontOptions(g.font),QD(g.label)?this.labelDirty=!0:g.label=void 0,void 0!==g.font&&null!==g.font)if("string"==typeof g.font)this.baseSize=this.fontOptions.size;else if("object"===yd(g.font)){var t=g.font.size;void 0!==t&&(this.baseSize=t)}}},{key:"initFontOptions",value:function(t){var A=this;tv(JD,(function(g){A.fontOptions[g]={}})),g.parseFontString(this.fontOptions,t)?this.fontOptions.vadjust=0:tv(t,(function(g,t){null!=g&&"object"!==yd(g)&&(A.fontOptions[t]=g)}))}},{key:"constrain",value:function(g){var t={constrainWidth:!1,maxWdt:-1,minWdt:-1,constrainHeight:!1,minHgt:-1,valign:"middle"},A=lv(g,"widthConstraint");if("number"==typeof A)t.maxWdt=Number(A),t.minWdt=Number(A);else if("object"===yd(A)){var e=lv(g,["widthConstraint","maximum"]);"number"==typeof e&&(t.maxWdt=Number(e));var C=lv(g,["widthConstraint","minimum"]);"number"==typeof C&&(t.minWdt=Number(C))}var I=lv(g,"heightConstraint");if("number"==typeof I)t.minHgt=Number(I);else if("object"===yd(I)){var i=lv(g,["heightConstraint","minimum"]);"number"==typeof i&&(t.minHgt=Number(i));var o=lv(g,["heightConstraint","valign"]);"string"==typeof o&&("top"!==o&&"bottom"!==o||(t.valign=o))}return t}},{key:"update",value:function(g,t){this.setOptions(g,!0),this.propagateFonts(t),qf(this.fontOptions,this.constrain(t)),this.fontOptions.chooser=YD("label",t)}},{key:"adjustSizes",value:function(g){var t=g?g.right+g.left:0;this.fontOptions.constrainWidth&&(this.fontOptions.maxWdt-=t,this.fontOptions.minWdt-=t);var A=g?g.top+g.bottom:0;this.fontOptions.constrainHeight&&(this.fontOptions.minHgt-=A)}},{key:"addFontOptionsToPile",value:function(g,t){for(var A=0;A5&&void 0!==arguments[5]?arguments[5]:"middle";if(void 0!==this.elementOptions.label){var i=this.fontOptions.size*this.body.view.scale;this.elementOptions.label&&i=this.elementOptions.scaling.label.maxVisible&&(i=Number(this.elementOptions.scaling.label.maxVisible)/this.body.view.scale),this.calculateLabelSize(g,e,C,t,A,I),this._drawBackground(g),this._drawText(g,t,this.size.yLine,I,i))}}},{key:"_drawBackground",value:function(g){if(void 0!==this.fontOptions.background&&"none"!==this.fontOptions.background){g.fillStyle=this.fontOptions.background;var t=this.getSize();g.fillRect(t.left,t.top,t.width,t.height)}}},{key:"_drawText",value:function(g,t,A){var e=arguments.length>3&&void 0!==arguments[3]?arguments[3]:"middle",C=arguments.length>4?arguments[4]:void 0,I=lh(this._setAlignment(g,t,A,e),2);t=I[0],A=I[1],g.textAlign="left",t-=this.size.width/2,this.fontOptions.valign&&this.size.height>this.size.labelHeight&&("top"===this.fontOptions.valign&&(A-=(this.size.height-this.size.labelHeight)/2),"bottom"===this.fontOptions.valign&&(A+=(this.size.height-this.size.labelHeight)/2));for(var i=0;i0&&(g.lineWidth=s.strokeWidth,g.strokeStyle=h,g.lineJoin="round"),g.fillStyle=d,s.strokeWidth>0&&g.strokeText(s.text,t+n,A+s.vadjust),g.fillText(s.text,t+n,A+s.vadjust),n+=s.width}A+=o.height}}}},{key:"_setAlignment",value:function(g,t,A,e){if(this.isEdgeLabel&&"horizontal"!==this.fontOptions.align&&!1===this.pointToSelf){t=0,A=0;"top"===this.fontOptions.align?(g.textBaseline="alphabetic",A-=4):"bottom"===this.fontOptions.align?(g.textBaseline="hanging",A+=4):g.textBaseline="middle"}else g.textBaseline=e;return[t,A]}},{key:"_getColor",value:function(g,t,A){var e=g||"#000000",C=A||"#ffffff";if(t<=this.elementOptions.scaling.label.drawThreshold){var I=Math.max(0,Math.min(1,1-(this.elementOptions.scaling.label.drawThreshold-t)));e=ev(e,I),C=ev(C,I)}return[e,C]}},{key:"getTextSize",value:function(g){var t=arguments.length>1&&void 0!==arguments[1]&&arguments[1],A=arguments.length>2&&void 0!==arguments[2]&&arguments[2];return this._processLabel(g,t,A),{width:this.size.width,height:this.size.height,lineCount:this.lineCount}}},{key:"getSize",value:function(){var g=this.size.left,t=this.size.top-1;if(this.isEdgeLabel){var A=.5*-this.size.width;switch(this.fontOptions.align){case"middle":g=A,t=.5*-this.size.height;break;case"top":g=A,t=-(this.size.height+2);break;case"bottom":g=A,t=2}}return{left:g,top:t,width:this.size.width,height:this.size.height}}},{key:"calculateLabelSize",value:function(g,t,A){var e=arguments.length>3&&void 0!==arguments[3]?arguments[3]:0,C=arguments.length>4&&void 0!==arguments[4]?arguments[4]:0,I=arguments.length>5&&void 0!==arguments[5]?arguments[5]:"middle";this._processLabel(g,t,A),this.size.left=e-.5*this.size.width,this.size.top=C-.5*this.size.height,this.size.yLine=C+.5*(1-this.lineCount)*this.fontOptions.size,"hanging"===I&&(this.size.top+=.5*this.fontOptions.size,this.size.top+=4,this.size.yLine+=4)}},{key:"getFormattingValues",value:function(g,t,A,e){var C=function(g,t,A){return"normal"===t?"mod"===A?"":g[A]:void 0!==g[t][A]?g[t][A]:g[A]},I={color:C(this.fontOptions,e,"color"),size:C(this.fontOptions,e,"size"),face:C(this.fontOptions,e,"face"),mod:C(this.fontOptions,e,"mod"),vadjust:C(this.fontOptions,e,"vadjust"),strokeWidth:this.fontOptions.strokeWidth,strokeColor:this.fontOptions.strokeColor};(t||A)&&("normal"===e&&!0===this.fontOptions.chooser&&this.elementOptions.labelHighlightBold?I.mod="bold":"function"==typeof this.fontOptions.chooser&&this.fontOptions.chooser(I,this.elementOptions.id,t,A));var i="";return void 0!==I.mod&&""!==I.mod&&(i+=I.mod+" "),i+=I.size+"px "+I.face,g.font=i.replace(/"/g,""),I.font=g.font,I.height=I.size,I}},{key:"differentState",value:function(g,t){return g!==this.selectedState||t!==this.hoverState}},{key:"_processLabelText",value:function(g,t,A,e){return new XD(g,this,t,A).process(e)}},{key:"_processLabel",value:function(g,t,A){if(!1!==this.labelDirty||this.differentState(t,A)){var e=this._processLabelText(g,t,A,this.elementOptions.label);this.fontOptions.minWdt>0&&e.width0&&e.height0&&(this.enableBorderDashes(g,t),g.stroke(),this.disableBorderDashes(g,t)),g.restore()}},{key:"performFill",value:function(g,t){g.save(),g.fillStyle=t.color,this.enableShadow(g,t),Bu(g).call(g),this.disableShadow(g,t),g.restore(),this.performStroke(g,t)}},{key:"_addBoundingBoxMargin",value:function(g){this.boundingBox.left-=g,this.boundingBox.top-=g,this.boundingBox.bottom+=g,this.boundingBox.right+=g}},{key:"_updateBoundingBox",value:function(g,t,A,e,C){void 0!==A&&this.resize(A,e,C),this.left=g-this.width/2,this.top=t-this.height/2,this.boundingBox.left=this.left,this.boundingBox.top=this.top,this.boundingBox.bottom=this.top+this.height,this.boundingBox.right=this.left+this.width}},{key:"updateBoundingBox",value:function(g,t,A,e,C){this._updateBoundingBox(g,t,A,e,C)}},{key:"getDimensionsFromLabel",value:function(g,t,A){this.textSize=this.labelModule.getTextSize(g,t,A);var e=this.textSize.width,C=this.textSize.height;return 0===e&&(e=14,C=14),{width:e,height:C}}}]),g}();function gN(g){var t=function(){if("undefined"==typeof Reflect||!MT)return!1;if(MT.sham)return!1;if("function"==typeof Proxy)return!0;try{return Boolean.prototype.valueOf.call(MT(Boolean,[],(function(){}))),!0}catch(g){return!1}}();return function(){var A,e=nb(g);if(t){var C=nb(this).constructor;A=MT(e,arguments,C)}else A=e.apply(this,arguments);return Ib(this,A)}}var tN=function(g){Cb(A,g);var t=gN(A);function A(g,e,C){var I;return cn(this,A),(I=t.call(this,g,e,C))._setMargins(C),I}return kd(A,[{key:"resize",value:function(g){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:this.selected,A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:this.hover;if(this.needsRefresh(t,A)){var e=this.getDimensionsFromLabel(g,t,A);this.width=e.width+this.margin.right+this.margin.left,this.height=e.height+this.margin.top+this.margin.bottom,this.radius=this.width/2}}},{key:"draw",value:function(g,t,A,e,C,I){this.resize(g,e,C),this.left=t-this.width/2,this.top=A-this.height/2,this.initContextForDraw(g,I),Ve(g,this.left,this.top,this.width,this.height,I.borderRadius),this.performFill(g,I),this.updateBoundingBox(t,A,g,e,C),this.labelModule.draw(g,this.left+this.textSize.width/2+this.margin.left,this.top+this.textSize.height/2+this.margin.top,e,C)}},{key:"updateBoundingBox",value:function(g,t,A,e,C){this._updateBoundingBox(g,t,A,e,C);var I=this.options.shapeProperties.borderRadius;this._addBoundingBoxMargin(I)}},{key:"distanceToBorder",value:function(g,t){g&&this.resize(g);var A=this.options.borderWidth;return Math.min(Math.abs(this.width/2/Math.cos(t)),Math.abs(this.height/2/Math.sin(t)))+A}}]),A}($D);function AN(g){var t=function(){if("undefined"==typeof Reflect||!MT)return!1;if(MT.sham)return!1;if("function"==typeof Proxy)return!0;try{return Boolean.prototype.valueOf.call(MT(Boolean,[],(function(){}))),!0}catch(g){return!1}}();return function(){var A,e=nb(g);if(t){var C=nb(this).constructor;A=MT(e,arguments,C)}else A=e.apply(this,arguments);return Ib(this,A)}}var eN=function(g){Cb(A,g);var t=AN(A);function A(g,e,C){var I;return cn(this,A),(I=t.call(this,g,e,C)).labelOffset=0,I.selected=!1,I}return kd(A,[{key:"setOptions",value:function(g,t,A){this.options=g,void 0===t&&void 0===A||this.setImages(t,A)}},{key:"setImages",value:function(g,t){t&&this.selected?(this.imageObj=t,this.imageObjAlt=g):(this.imageObj=g,this.imageObjAlt=t)}},{key:"switchImages",value:function(g){var t=g&&!this.selected||!g&&this.selected;if(this.selected=g,void 0!==this.imageObjAlt&&t){var A=this.imageObj;this.imageObj=this.imageObjAlt,this.imageObjAlt=A}}},{key:"_getImagePadding",value:function(){var g={top:0,right:0,bottom:0,left:0};if(this.options.imagePadding){var t=this.options.imagePadding;"object"==yd(t)?(g.top=t.top,g.right=t.right,g.bottom=t.bottom,g.left=t.left):(g.top=t,g.right=t,g.bottom=t,g.left=t)}return g}},{key:"_resizeImage",value:function(){var g,t;if(!1===this.options.shapeProperties.useImageSize){var A=1,e=1;this.imageObj.width&&this.imageObj.height&&(this.imageObj.width>this.imageObj.height?A=this.imageObj.width/this.imageObj.height:e=this.imageObj.height/this.imageObj.width),g=2*this.options.size*A,t=2*this.options.size*e}else{var C=this._getImagePadding();g=this.imageObj.width+C.left+C.right,t=this.imageObj.height+C.top+C.bottom}this.width=g,this.height=t,this.radius=.5*this.width}},{key:"_drawRawCircle",value:function(g,t,A,e){this.initContextForDraw(g,e),Le(g,t,A,e.size),this.performFill(g,e)}},{key:"_drawImageAtPosition",value:function(g,t){if(0!=this.imageObj.width){g.globalAlpha=void 0!==t.opacity?t.opacity:1,this.enableShadow(g,t);var A=1;!0===this.options.shapeProperties.interpolation&&(A=this.imageObj.width/this.width/this.body.view.scale);var e=this._getImagePadding(),C=this.left+e.left,I=this.top+e.top,i=this.width-e.left-e.right,o=this.height-e.top-e.bottom;this.imageObj.drawImageAtPosition(g,A,C,I,i,o),this.disableShadow(g,t)}}},{key:"_drawImageLabel",value:function(g,t,A,e,C){var I=0;if(void 0!==this.height){I=.5*this.height;var i=this.labelModule.getTextSize(g,e,C);i.lineCount>=1&&(I+=i.height/2)}var o=A+I;this.options.label&&(this.labelOffset=I),this.labelModule.draw(g,t,o,e,C,"hanging")}}]),A}($D);function CN(g){var t=function(){if("undefined"==typeof Reflect||!MT)return!1;if(MT.sham)return!1;if("function"==typeof Proxy)return!0;try{return Boolean.prototype.valueOf.call(MT(Boolean,[],(function(){}))),!0}catch(g){return!1}}();return function(){var A,e=nb(g);if(t){var C=nb(this).constructor;A=MT(e,arguments,C)}else A=e.apply(this,arguments);return Ib(this,A)}}var IN=function(g){Cb(A,g);var t=CN(A);function A(g,e,C){var I;return cn(this,A),(I=t.call(this,g,e,C))._setMargins(C),I}return kd(A,[{key:"resize",value:function(g){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:this.selected,A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:this.hover;if(this.needsRefresh(t,A)){var e=this.getDimensionsFromLabel(g,t,A),C=Math.max(e.width+this.margin.right+this.margin.left,e.height+this.margin.top+this.margin.bottom);this.options.size=C/2,this.width=C,this.height=C,this.radius=this.width/2}}},{key:"draw",value:function(g,t,A,e,C,I){this.resize(g,e,C),this.left=t-this.width/2,this.top=A-this.height/2,this._drawRawCircle(g,t,A,I),this.updateBoundingBox(t,A),this.labelModule.draw(g,this.left+this.textSize.width/2+this.margin.left,A,e,C)}},{key:"updateBoundingBox",value:function(g,t){this.boundingBox.top=t-this.options.size,this.boundingBox.left=g-this.options.size,this.boundingBox.right=g+this.options.size,this.boundingBox.bottom=t+this.options.size}},{key:"distanceToBorder",value:function(g){return g&&this.resize(g),.5*this.width}}]),A}(eN);function iN(g){var t=function(){if("undefined"==typeof Reflect||!MT)return!1;if(MT.sham)return!1;if("function"==typeof Proxy)return!0;try{return Boolean.prototype.valueOf.call(MT(Boolean,[],(function(){}))),!0}catch(g){return!1}}();return function(){var A,e=nb(g);if(t){var C=nb(this).constructor;A=MT(e,arguments,C)}else A=e.apply(this,arguments);return Ib(this,A)}}var oN=function(g){Cb(A,g);var t=iN(A);function A(g,e,C,I,i){var o;return cn(this,A),(o=t.call(this,g,e,C)).setImages(I,i),o}return kd(A,[{key:"resize",value:function(g){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:this.selected,A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:this.hover;if(void 0===this.imageObj.src||void 0===this.imageObj.width||void 0===this.imageObj.height){var e=2*this.options.size;return this.width=e,this.height=e,void(this.radius=.5*this.width)}this.needsRefresh(t,A)&&this._resizeImage()}},{key:"draw",value:function(g,t,A,e,C,I){this.switchImages(e),this.resize();var i=t,o=A;"top-left"===this.options.shapeProperties.coordinateOrigin?(this.left=t,this.top=A,i+=this.width/2,o+=this.height/2):(this.left=t-this.width/2,this.top=A-this.height/2),this._drawRawCircle(g,i,o,I),g.save(),g.clip(),this._drawImageAtPosition(g,I),g.restore(),this._drawImageLabel(g,i,o,e,C),this.updateBoundingBox(t,A)}},{key:"updateBoundingBox",value:function(g,t){"top-left"===this.options.shapeProperties.coordinateOrigin?(this.boundingBox.top=t,this.boundingBox.left=g,this.boundingBox.right=g+2*this.options.size,this.boundingBox.bottom=t+2*this.options.size):(this.boundingBox.top=t-this.options.size,this.boundingBox.left=g-this.options.size,this.boundingBox.right=g+this.options.size,this.boundingBox.bottom=t+this.options.size),this.boundingBox.left=Math.min(this.boundingBox.left,this.labelModule.size.left),this.boundingBox.right=Math.max(this.boundingBox.right,this.labelModule.size.left+this.labelModule.size.width),this.boundingBox.bottom=Math.max(this.boundingBox.bottom,this.boundingBox.bottom+this.labelOffset)}},{key:"distanceToBorder",value:function(g){return g&&this.resize(g),.5*this.width}}]),A}(eN);function nN(g){var t=function(){if("undefined"==typeof Reflect||!MT)return!1;if(MT.sham)return!1;if("function"==typeof Proxy)return!0;try{return Boolean.prototype.valueOf.call(MT(Boolean,[],(function(){}))),!0}catch(g){return!1}}();return function(){var A,e=nb(g);if(t){var C=nb(this).constructor;A=MT(e,arguments,C)}else A=e.apply(this,arguments);return Ib(this,A)}}var rN=function(g){Cb(A,g);var t=nN(A);function A(g,e,C){return cn(this,A),t.call(this,g,e,C)}return kd(A,[{key:"resize",value:function(g){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:this.selected,A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:this.hover,e=arguments.length>3&&void 0!==arguments[3]?arguments[3]:{size:this.options.size};if(this.needsRefresh(t,A)){var C,I;this.labelModule.getTextSize(g,t,A);var i=2*e.size;this.width=null!==(C=this.customSizeWidth)&&void 0!==C?C:i,this.height=null!==(I=this.customSizeHeight)&&void 0!==I?I:i,this.radius=.5*this.width}}},{key:"_drawShape",value:function(g,t,A,e,C,I,i,o){var n,r=this;return this.resize(g,I,i,o),this.left=e-this.width/2,this.top=C-this.height/2,this.initContextForDraw(g,o),(n=t,Object.prototype.hasOwnProperty.call(Ue,n)?Ue[n]:function(g){for(var t=arguments.length,A=new Array(t>1?t-1:0),e=1;e0&&(this.boundingBox.left=Math.min(this.boundingBox.left,this.labelModule.size.left),this.boundingBox.right=Math.max(this.boundingBox.right,this.labelModule.size.left+this.labelModule.size.width),this.boundingBox.bottom=Math.max(this.boundingBox.bottom,this.boundingBox.bottom+this.labelModule.size.height))}}]),A}($D);function sN(g,t){var A=Lh(g);if(BT){var e=BT(g);t&&(e=pc(e).call(e,(function(t){return WT(g,t).enumerable}))),A.push.apply(A,e)}return A}function aN(g){for(var t=1;t1&&void 0!==arguments[1]?arguments[1]:this.selected,A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:this.hover;if(this.needsRefresh(t,A)){var e=this.getDimensionsFromLabel(g,t,A);this.height=2*e.height,this.width=e.width+e.height,this.radius=.5*this.width}}},{key:"draw",value:function(g,t,A,e,C,I){this.resize(g,e,C),this.left=t-.5*this.width,this.top=A-.5*this.height,this.initContextForDraw(g,I),Ye(g,this.left,this.top,this.width,this.height),this.performFill(g,I),this.updateBoundingBox(t,A,g,e,C),this.labelModule.draw(g,t,A,e,C)}},{key:"distanceToBorder",value:function(g,t){g&&this.resize(g);var A=.5*this.width,e=.5*this.height,C=Math.sin(t)*A,I=Math.cos(t)*e;return A*e/Math.sqrt(C*C+I*I)}}]),A}($D);function bN(g){var t=function(){if("undefined"==typeof Reflect||!MT)return!1;if(MT.sham)return!1;if("function"==typeof Proxy)return!0;try{return Boolean.prototype.valueOf.call(MT(Boolean,[],(function(){}))),!0}catch(g){return!1}}();return function(){var A,e=nb(g);if(t){var C=nb(this).constructor;A=MT(e,arguments,C)}else A=e.apply(this,arguments);return Ib(this,A)}}var wN=function(g){Cb(A,g);var t=bN(A);function A(g,e,C){var I;return cn(this,A),(I=t.call(this,g,e,C))._setMargins(C),I}return kd(A,[{key:"resize",value:function(g,t,A){this.needsRefresh(t,A)&&(this.iconSize={width:Number(this.options.icon.size),height:Number(this.options.icon.size)},this.width=this.iconSize.width+this.margin.right+this.margin.left,this.height=this.iconSize.height+this.margin.top+this.margin.bottom,this.radius=.5*this.width)}},{key:"draw",value:function(g,t,A,e,C,I){var i=this;return this.resize(g,e,C),this.options.icon.size=this.options.icon.size||50,this.left=t-this.width/2,this.top=A-this.height/2,this._icon(g,t,A,e,C,I),{drawExternalLabel:function(){if(void 0!==i.options.label){i.labelModule.draw(g,i.left+i.iconSize.width/2+i.margin.left,A+i.height/2+5,e)}i.updateBoundingBox(t,A)}}}},{key:"updateBoundingBox",value:function(g,t){if(this.boundingBox.top=t-.5*this.options.icon.size,this.boundingBox.left=g-.5*this.options.icon.size,this.boundingBox.right=g+.5*this.options.icon.size,this.boundingBox.bottom=t+.5*this.options.icon.size,void 0!==this.options.label&&this.labelModule.size.width>0){this.boundingBox.left=Math.min(this.boundingBox.left,this.labelModule.size.left),this.boundingBox.right=Math.max(this.boundingBox.right,this.labelModule.size.left+this.labelModule.size.width),this.boundingBox.bottom=Math.max(this.boundingBox.bottom,this.boundingBox.bottom+this.labelModule.size.height+5)}}},{key:"_icon",value:function(g,t,A,e,C,I){var i=Number(this.options.icon.size);void 0!==this.options.icon.code?(g.font=[null!=this.options.icon.weight?this.options.icon.weight:e?"bold":"",(null!=this.options.icon.weight&&e?5:0)+i+"px",this.options.icon.face].join(" "),g.fillStyle=this.options.icon.color||"black",g.textAlign="center",g.textBaseline="middle",this.enableShadow(g,I),g.fillText(this.options.icon.code,t,A),this.disableShadow(g,I)):console.error("When using the icon shape, you need to define the code in the icon options object. This can be done per node or globally.")}},{key:"distanceToBorder",value:function(g,t){return this._distanceToBorder(g,t)}}]),A}($D);function kN(g){var t=function(){if("undefined"==typeof Reflect||!MT)return!1;if(MT.sham)return!1;if("function"==typeof Proxy)return!0;try{return Boolean.prototype.valueOf.call(MT(Boolean,[],(function(){}))),!0}catch(g){return!1}}();return function(){var A,e=nb(g);if(t){var C=nb(this).constructor;A=MT(e,arguments,C)}else A=e.apply(this,arguments);return Ib(this,A)}}var xN=function(g){Cb(A,g);var t=kN(A);function A(g,e,C,I,i){var o;return cn(this,A),(o=t.call(this,g,e,C)).setImages(I,i),o}return kd(A,[{key:"resize",value:function(g){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:this.selected,A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:this.hover;if(void 0===this.imageObj.src||void 0===this.imageObj.width||void 0===this.imageObj.height){var e=2*this.options.size;return this.width=e,void(this.height=e)}this.needsRefresh(t,A)&&this._resizeImage()}},{key:"draw",value:function(g,t,A,e,C,I){g.save(),this.switchImages(e),this.resize();var i=t,o=A;if("top-left"===this.options.shapeProperties.coordinateOrigin?(this.left=t,this.top=A,i+=this.width/2,o+=this.height/2):(this.left=t-this.width/2,this.top=A-this.height/2),!0===this.options.shapeProperties.useBorderWithImage){var n=this.options.borderWidth,r=this.options.borderWidthSelected||2*this.options.borderWidth,s=(e?r:n)/this.body.view.scale;g.lineWidth=Math.min(this.width,s),g.beginPath();var a=e?this.options.color.highlight.border:C?this.options.color.hover.border:this.options.color.border,d=e?this.options.color.highlight.background:C?this.options.color.hover.background:this.options.color.background;void 0!==I.opacity&&(a=ev(a,I.opacity),d=ev(d,I.opacity)),g.strokeStyle=a,g.fillStyle=d,g.rect(this.left-.5*g.lineWidth,this.top-.5*g.lineWidth,this.width+g.lineWidth,this.height+g.lineWidth),Bu(g).call(g),this.performStroke(g,I),g.closePath()}this._drawImageAtPosition(g,I),this._drawImageLabel(g,i,o,e,C),this.updateBoundingBox(t,A),g.restore()}},{key:"updateBoundingBox",value:function(g,t){this.resize(),"top-left"===this.options.shapeProperties.coordinateOrigin?(this.left=g,this.top=t):(this.left=g-this.width/2,this.top=t-this.height/2),this.boundingBox.left=this.left,this.boundingBox.top=this.top,this.boundingBox.bottom=this.top+this.height,this.boundingBox.right=this.left+this.width,void 0!==this.options.label&&this.labelModule.size.width>0&&(this.boundingBox.left=Math.min(this.boundingBox.left,this.labelModule.size.left),this.boundingBox.right=Math.max(this.boundingBox.right,this.labelModule.size.left+this.labelModule.size.width),this.boundingBox.bottom=Math.max(this.boundingBox.bottom,this.boundingBox.bottom+this.labelOffset))}},{key:"distanceToBorder",value:function(g,t){return this._distanceToBorder(g,t)}}]),A}(eN);function EN(g){var t=function(){if("undefined"==typeof Reflect||!MT)return!1;if(MT.sham)return!1;if("function"==typeof Proxy)return!0;try{return Boolean.prototype.valueOf.call(MT(Boolean,[],(function(){}))),!0}catch(g){return!1}}();return function(){var A,e=nb(g);if(t){var C=nb(this).constructor;A=MT(e,arguments,C)}else A=e.apply(this,arguments);return Ib(this,A)}}var ON=function(g){Cb(A,g);var t=EN(A);function A(g,e,C){return cn(this,A),t.call(this,g,e,C)}return kd(A,[{key:"draw",value:function(g,t,A,e,C,I){return this._drawShape(g,"square",2,t,A,e,C,I)}},{key:"distanceToBorder",value:function(g,t){return this._distanceToBorder(g,t)}}]),A}(rN);function TN(g){var t=function(){if("undefined"==typeof Reflect||!MT)return!1;if(MT.sham)return!1;if("function"==typeof Proxy)return!0;try{return Boolean.prototype.valueOf.call(MT(Boolean,[],(function(){}))),!0}catch(g){return!1}}();return function(){var A,e=nb(g);if(t){var C=nb(this).constructor;A=MT(e,arguments,C)}else A=e.apply(this,arguments);return Ib(this,A)}}var DN=function(g){Cb(A,g);var t=TN(A);function A(g,e,C){return cn(this,A),t.call(this,g,e,C)}return kd(A,[{key:"draw",value:function(g,t,A,e,C,I){return this._drawShape(g,"hexagon",4,t,A,e,C,I)}},{key:"distanceToBorder",value:function(g,t){return this._distanceToBorder(g,t)}}]),A}(rN);function NN(g){var t=function(){if("undefined"==typeof Reflect||!MT)return!1;if(MT.sham)return!1;if("function"==typeof Proxy)return!0;try{return Boolean.prototype.valueOf.call(MT(Boolean,[],(function(){}))),!0}catch(g){return!1}}();return function(){var A,e=nb(g);if(t){var C=nb(this).constructor;A=MT(e,arguments,C)}else A=e.apply(this,arguments);return Ib(this,A)}}var RN=function(g){Cb(A,g);var t=NN(A);function A(g,e,C){return cn(this,A),t.call(this,g,e,C)}return kd(A,[{key:"draw",value:function(g,t,A,e,C,I){return this._drawShape(g,"star",4,t,A,e,C,I)}},{key:"distanceToBorder",value:function(g,t){return this._distanceToBorder(g,t)}}]),A}(rN);function PN(g){var t=function(){if("undefined"==typeof Reflect||!MT)return!1;if(MT.sham)return!1;if("function"==typeof Proxy)return!0;try{return Boolean.prototype.valueOf.call(MT(Boolean,[],(function(){}))),!0}catch(g){return!1}}();return function(){var A,e=nb(g);if(t){var C=nb(this).constructor;A=MT(e,arguments,C)}else A=e.apply(this,arguments);return Ib(this,A)}}var MN=function(g){Cb(A,g);var t=PN(A);function A(g,e,C){var I;return cn(this,A),(I=t.call(this,g,e,C))._setMargins(C),I}return kd(A,[{key:"resize",value:function(g,t,A){this.needsRefresh(t,A)&&(this.textSize=this.labelModule.getTextSize(g,t,A),this.width=this.textSize.width+this.margin.right+this.margin.left,this.height=this.textSize.height+this.margin.top+this.margin.bottom,this.radius=.5*this.width)}},{key:"draw",value:function(g,t,A,e,C,I){this.resize(g,e,C),this.left=t-this.width/2,this.top=A-this.height/2,this.enableShadow(g,I),this.labelModule.draw(g,this.left+this.textSize.width/2+this.margin.left,this.top+this.textSize.height/2+this.margin.top,e,C),this.disableShadow(g,I),this.updateBoundingBox(t,A,g,e,C)}},{key:"distanceToBorder",value:function(g,t){return this._distanceToBorder(g,t)}}]),A}($D);function BN(g){var t=function(){if("undefined"==typeof Reflect||!MT)return!1;if(MT.sham)return!1;if("function"==typeof Proxy)return!0;try{return Boolean.prototype.valueOf.call(MT(Boolean,[],(function(){}))),!0}catch(g){return!1}}();return function(){var A,e=nb(g);if(t){var C=nb(this).constructor;A=MT(e,arguments,C)}else A=e.apply(this,arguments);return Ib(this,A)}}var zN=function(g){Cb(A,g);var t=BN(A);function A(g,e,C){return cn(this,A),t.call(this,g,e,C)}return kd(A,[{key:"draw",value:function(g,t,A,e,C,I){return this._drawShape(g,"triangle",3,t,A,e,C,I)}},{key:"distanceToBorder",value:function(g,t){return this._distanceToBorder(g,t)}}]),A}(rN);function SN(g){var t=function(){if("undefined"==typeof Reflect||!MT)return!1;if(MT.sham)return!1;if("function"==typeof Proxy)return!0;try{return Boolean.prototype.valueOf.call(MT(Boolean,[],(function(){}))),!0}catch(g){return!1}}();return function(){var A,e=nb(g);if(t){var C=nb(this).constructor;A=MT(e,arguments,C)}else A=e.apply(this,arguments);return Ib(this,A)}}var ZN=function(g){Cb(A,g);var t=SN(A);function A(g,e,C){return cn(this,A),t.call(this,g,e,C)}return kd(A,[{key:"draw",value:function(g,t,A,e,C,I){return this._drawShape(g,"triangleDown",3,t,A,e,C,I)}},{key:"distanceToBorder",value:function(g,t){return this._distanceToBorder(g,t)}}]),A}(rN);function FN(g,t){var A=Lh(g);if(BT){var e=BT(g);t&&(e=pc(e).call(e,(function(t){return WT(g,t).enumerable}))),A.push.apply(A,e)}return A}function GN(g){for(var t=1;tg.left&&this.shape.topg.top}},{key:"isBoundingBoxOverlappingWith",value:function(g){return this.shape.boundingBox.leftg.left&&this.shape.boundingBox.topg.top}}],[{key:"checkOpacity",value:function(g){return 0<=g&&g<=1}},{key:"checkCoordinateOrigin",value:function(g){return void 0===g||"center"===g||"top-left"===g}},{key:"updateGroupOptions",value:function(t,A,e){var C;if(void 0!==e){var I=t.group;if(void 0!==A&&void 0!==A.group&&I!==A.group)throw new Error("updateGroupOptions: group values in options don't match.");if("number"==typeof I||"string"==typeof I&&""!=I){var i=e.get(I);void 0!==i.opacity&&void 0===A.opacity&&(g.checkOpacity(i.opacity)||(console.error("Invalid option for node opacity. Value must be between 0 and 1, found: "+i.opacity),i.opacity=void 0));var o=pc(C=VD(A)).call(C,(function(g){return null!=A[g]}));o.push("font"),Jf(o,t,i),t.color=Iv(t.color)}}}},{key:"parseOptions",value:function(t,A){var e=arguments.length>2&&void 0!==arguments[2]&&arguments[2],C=arguments.length>3&&void 0!==arguments[3]?arguments[3]:{},I=arguments.length>4?arguments[4]:void 0;if(Jf(["color","fixed","shadow"],t,A,e),g.checkMass(A),void 0!==t.opacity&&(g.checkOpacity(t.opacity)||(console.error("Invalid option for node opacity. Value must be between 0 and 1, found: "+t.opacity),t.opacity=void 0)),void 0!==A.opacity&&(g.checkOpacity(A.opacity)||(console.error("Invalid option for node opacity. Value must be between 0 and 1, found: "+A.opacity),A.opacity=void 0)),A.shapeProperties&&!g.checkCoordinateOrigin(A.shapeProperties.coordinateOrigin)&&console.error("Invalid option for node coordinateOrigin, found: "+A.shapeProperties.coordinateOrigin),dv(t,A,"shadow",C),void 0!==A.color&&null!==A.color){var i=Iv(A.color);Hf(t.color,i)}else!0===e&&null===A.color&&(t.color=av(C.color));void 0!==A.fixed&&null!==A.fixed&&("boolean"==typeof A.fixed?(t.fixed.x=A.fixed,t.fixed.y=A.fixed):(void 0!==A.fixed.x&&"boolean"==typeof A.fixed.x&&(t.fixed.x=A.fixed.x),void 0!==A.fixed.y&&"boolean"==typeof A.fixed.y&&(t.fixed.y=A.fixed.y))),!0===e&&null===A.font&&(t.font=av(C.font)),g.updateGroupOptions(t,A,I),void 0!==A.scaling&&dv(t.scaling,A.scaling,"label",C.scaling)}},{key:"checkMass",value:function(g,t){if(void 0!==g.mass&&g.mass<=0){var A="";void 0!==t&&(A=" in node id: "+t),console.error("%cNegative or zero mass disallowed"+A+", setting mass to 1.",Tv),g.mass=1}}}]),g}();function LN(g,t){var A=void 0!==uh&&ln(g)||g["@@iterator"];if(!A){if(Rh(g)||(A=function(g,t){var A;if(!g)return;if("string"==typeof g)return VN(g,t);var e=wh(A=Object.prototype.toString.call(g)).call(A,8,-1);"Object"===e&&g.constructor&&(e=g.constructor.name);if("Map"===e||"Set"===e)return Uo(g);if("Arguments"===e||/^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(e))return VN(g,t)}(g))||t&&g&&"number"==typeof g.length){A&&(g=A);var e=0,C=function(){};return{s:C,n:function(){return e>=g.length?{done:!0}:{done:!1,value:g[e++]}},e:function(g){throw g},f:C}}throw new TypeError("Invalid attempt to iterate non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.")}var I,i=!0,o=!1;return{s:function(){A=A.call(g)},n:function(){var g=A.next();return i=g.done,g},e:function(g){o=!0,I=g},f:function(){try{i||null==A.return||A.return()}finally{if(o)throw I}}}}function VN(g,t){(null==t||t>g.length)&&(t=g.length);for(var A=0,e=new Array(t);A1?console.error("Invalid option for node opacity. Value must be between 0 and 1, found: "+g.opacity):this.options.opacity=g.opacity),void 0!==g.shape)for(var t in this.body.nodes)Object.prototype.hasOwnProperty.call(this.body.nodes,t)&&this.body.nodes[t].updateShape();if(void 0!==g.font||void 0!==g.widthConstraint||void 0!==g.heightConstraint)for(var A=0,e=Lh(this.body.nodes);A1&&void 0!==arguments[1]&&arguments[1],A=this.body.data.nodes;if(kD("id",g))this.body.data.nodes=g;else if(Rh(g))this.body.data.nodes=new mD,this.body.data.nodes.add(g);else{if(g)throw new TypeError("Array or DataSet expected");this.body.data.nodes=new mD}if(A&&tv(this.nodesListeners,(function(g,t){A.off(t,g)})),this.body.nodes={},this.body.data.nodes){var e=this;tv(this.nodesListeners,(function(g,t){e.body.data.nodes.on(t,g)}));var C=this.body.data.nodes.getIds();this.add(C,!0)}!1===t&&this.body.emitter.emit("_dataChanged")}},{key:"add",value:function(g){for(var t,A=arguments.length>1&&void 0!==arguments[1]&&arguments[1],e=[],C=0;C1&&void 0!==arguments[1]?arguments[1]:jN)(g,this.body,this.images,this.groups,this.options,this.defaultOptions)}},{key:"refresh",value:function(){var g=this,t=arguments.length>0&&void 0!==arguments[0]&&arguments[0];tv(this.body.nodes,(function(A,e){var C=g.body.data.nodes.get(e);void 0!==C&&(!0===t&&A.setOptions({x:null,y:null}),A.setOptions({fixed:!1}),A.setOptions(C))}))}},{key:"getPositions",value:function(g){var t={};if(void 0!==g){if(!0===Rh(g)){for(var A=0;A0?(e=A/o)*e:A;return o===1/0?1/0:o*eR(C)}});var CR=A(tg.Math.hypot);function IR(g){var t=function(){if("undefined"==typeof Reflect||!MT)return!1;if(MT.sham)return!1;if("function"==typeof Proxy)return!0;try{return Boolean.prototype.valueOf.call(MT(Boolean,[],(function(){}))),!0}catch(g){return!1}}();return function(){var A,e=nb(g);if(t){var C=nb(this).constructor;A=MT(e,arguments,C)}else A=e.apply(this,arguments);return Ib(this,A)}}var iR=function(){function g(){cn(this,g)}return kd(g,null,[{key:"transform",value:function(g,t){Rh(g)||(g=[g]);for(var A=t.point.x,e=t.point.y,C=t.angle,I=t.length,i=0;i4&&void 0!==arguments[4]?arguments[4]:this.getViaNode();g.strokeStyle=this.getColor(g,t),g.lineWidth=t.width,!1!==t.dashes?this._drawDashedLine(g,t,C):this._drawLine(g,t,C)}},{key:"_drawLine",value:function(g,t,A,e,C){if(this.from!=this.to)this._line(g,t,A,e,C);else{var I=lh(this._getCircleData(g),3),i=I[0],o=I[1],n=I[2];this._circle(g,t,i,o,n)}}},{key:"_drawDashedLine",value:function(g,t,A,e,C){g.lineCap="round";var I=Rh(t.dashes)?t.dashes:[5,5];if(void 0!==g.setLineDash){if(g.save(),g.setLineDash(I),g.lineDashOffset=0,this.from!=this.to)this._line(g,t,A);else{var i=lh(this._getCircleData(g),3),o=i[0],n=i[1],r=i[2];this._circle(g,t,o,n,r)}g.setLineDash([0]),g.lineDashOffset=0,g.restore()}else{if(this.from!=this.to)Qe(g,this.from.x,this.from.y,this.to.x,this.to.y,I);else{var s=lh(this._getCircleData(g),3),a=s[0],d=s[1],h=s[2];this._circle(g,t,a,d,h)}this.enableShadow(g,t),g.stroke(),this.disableShadow(g,t)}}},{key:"findBorderPosition",value:function(g,t,A){return this.from!=this.to?this._findBorderPosition(g,t,A):this._findBorderPositionCircle(g,t,A)}},{key:"findBorderPositions",value:function(g){if(this.from!=this.to)return{from:this._findBorderPosition(this.from,g),to:this._findBorderPosition(this.to,g)};var t,A=lh(wh(t=this._getCircleData(g)).call(t,0,2),2),e=A[0],C=A[1];return{from:this._findBorderPositionCircle(this.from,g,{x:e,y:C,low:.25,high:.6,direction:-1}),to:this._findBorderPositionCircle(this.from,g,{x:e,y:C,low:.6,high:.8,direction:1})}}},{key:"_getCircleData",value:function(g){var t=this.options.selfReference.size;void 0!==g&&void 0===this.from.shape.width&&this.from.shape.resize(g);var A=UD(g,this.options.selfReference.angle,t,this.from);return[A.x,A.y,t]}},{key:"_pointOnCircle",value:function(g,t,A,e){var C=2*e*Math.PI;return{x:g+A*Math.cos(C),y:t-A*Math.sin(C)}}},{key:"_findBorderPositionCircle",value:function(g,t,A){var e,C=A.x,I=A.y,i=A.low,o=A.high,n=A.direction,r=this.options.selfReference.size,s=.5*(i+o),a=0;!0===this.options.arrowStrikethrough&&(-1===n?a=this.options.endPointOffset.from:1===n&&(a=this.options.endPointOffset.to));var d=0;do{s=.5*(i+o),e=this._pointOnCircle(C,I,r,s);var h=Math.atan2(g.y-e.y,g.x-e.x),l=g.distanceToBorder(t,h)+a-Math.sqrt(Math.pow(e.x-g.x,2)+Math.pow(e.y-g.y,2));if(Math.abs(l)<.05)break;l>0?n>0?i=s:o=s:n>0?o=s:i=s,++d}while(i<=o&&d<10);return mR(mR({},e),{},{t:s})}},{key:"getLineWidth",value:function(g,t){return!0===g?Math.max(this.selectionWidth,.3/this._body.view.scale):!0===t?Math.max(this.hoverWidth,.3/this._body.view.scale):Math.max(this.options.width,.3/this._body.view.scale)}},{key:"getColor",value:function(g,t){if(!1!==t.inheritsColor){if("both"===t.inheritsColor&&this.from.id!==this.to.id){var A=g.createLinearGradient(this.from.x,this.from.y,this.to.x,this.to.y),e=this.from.options.color.highlight.border,C=this.to.options.color.highlight.border;return!1===this.from.selected&&!1===this.to.selected?(e=ev(this.from.options.color.border,t.opacity),C=ev(this.to.options.color.border,t.opacity)):!0===this.from.selected&&!1===this.to.selected?C=this.to.options.color.border:!1===this.from.selected&&!0===this.to.selected&&(e=this.from.options.color.border),A.addColorStop(0,e),A.addColorStop(1,C),A}return"to"===t.inheritsColor?ev(this.to.options.color.border,t.opacity):ev(this.from.options.color.border,t.opacity)}return ev(t.color,t.opacity)}},{key:"_circle",value:function(g,t,A,e,C){this.enableShadow(g,t);var I=0,i=2*Math.PI;if(!this.options.selfReference.renderBehindTheNode){var o=this.options.selfReference.angle,n=this.options.selfReference.angle+Math.PI,r=this._findBorderPositionCircle(this.from,g,{x:A,y:e,low:o,high:n,direction:-1}),s=this._findBorderPositionCircle(this.from,g,{x:A,y:e,low:o,high:n,direction:1});I=Math.atan2(r.y-e,r.x-A),i=Math.atan2(s.y-e,s.x-A)}g.beginPath(),g.arc(A,e,C,I,i,!1),g.stroke(),this.disableShadow(g,t)}},{key:"getDistanceToEdge",value:function(g,t,A,e,C,I){if(this.from!=this.to)return this._getDistanceToEdge(g,t,A,e,C,I);var i=lh(this._getCircleData(void 0),3),o=i[0],n=i[1],r=i[2],s=o-C,a=n-I;return Math.abs(Math.sqrt(s*s+a*a)-r)}},{key:"_getDistanceToLine",value:function(g,t,A,e,C,I){var i=A-g,o=e-t,n=((C-g)*i+(I-t)*o)/(i*i+o*o);n>1?n=1:n<0&&(n=0);var r=g+n*i-C,s=t+n*o-I;return Math.sqrt(r*r+s*s)}},{key:"getArrowData",value:function(g,t,A,e,C,I){var i,o,n,r,s,a,d,h=I.width;"from"===t?(n=this.from,r=this.to,s=I.fromArrowScale<0,a=Math.abs(I.fromArrowScale),d=I.fromArrowType):"to"===t?(n=this.to,r=this.from,s=I.toArrowScale<0,a=Math.abs(I.toArrowScale),d=I.toArrowType):(n=this.to,r=this.from,s=I.middleArrowScale<0,a=Math.abs(I.middleArrowScale),d=I.middleArrowType);var l=15*a+3*h;if(n!=r){var c=l/CR(n.x-r.x,n.y-r.y);if("middle"!==t)if(!0===this.options.smooth.enabled){var u=this._findBorderPosition(n,g,{via:A}),p=this.getPoint(u.t+c*("from"===t?1:-1),A);i=Math.atan2(u.y-p.y,u.x-p.x),o=u}else i=Math.atan2(n.y-r.y,n.x-r.x),o=this._findBorderPosition(n,g);else{var f=(s?-c:c)/2,v=this.getPoint(.5+f,A),y=this.getPoint(.5-f,A);i=Math.atan2(v.y-y.y,v.x-y.x),o=this.getPoint(.5,A)}}else{var m=lh(this._getCircleData(g),3),b=m[0],w=m[1],k=m[2];if("from"===t){var x=this.options.selfReference.angle,E=this.options.selfReference.angle+Math.PI,O=this._findBorderPositionCircle(this.from,g,{x:b,y:w,low:x,high:E,direction:-1});i=-2*O.t*Math.PI+1.5*Math.PI+.1*Math.PI,o=O}else if("to"===t){var T=this.options.selfReference.angle,D=this.options.selfReference.angle+Math.PI,N=this._findBorderPositionCircle(this.from,g,{x:b,y:w,low:T,high:D,direction:1});i=-2*N.t*Math.PI+1.5*Math.PI-1.1*Math.PI,o=N}else{var R=this.options.selfReference.angle/(2*Math.PI);o=this._pointOnCircle(b,w,k,R),i=-2*R*Math.PI+1.5*Math.PI+.1*Math.PI}}return{point:o,core:{x:o.x-.9*l*Math.cos(i),y:o.y-.9*l*Math.sin(i)},angle:i,length:l,type:d}}},{key:"drawArrowHead",value:function(g,t,A,e,C){g.strokeStyle=this.getColor(g,t),g.fillStyle=g.strokeStyle,g.lineWidth=t.width,vR.draw(g,C)&&(this.enableShadow(g,t),Bu(g).call(g),this.disableShadow(g,t))}},{key:"enableShadow",value:function(g,t){!0===t.shadow&&(g.shadowColor=t.shadowColor,g.shadowBlur=t.shadowSize,g.shadowOffsetX=t.shadowX,g.shadowOffsetY=t.shadowY)}},{key:"disableShadow",value:function(g,t){!0===t.shadow&&(g.shadowColor="rgba(0,0,0,0)",g.shadowBlur=0,g.shadowOffsetX=0,g.shadowOffsetY=0)}},{key:"drawBackground",value:function(g,t){if(!1!==t.background){var A={strokeStyle:g.strokeStyle,lineWidth:g.lineWidth,dashes:g.dashes};g.strokeStyle=t.backgroundColor,g.lineWidth=t.backgroundSize,this.setStrokeDashed(g,t.backgroundDashes),g.stroke(),g.strokeStyle=A.strokeStyle,g.lineWidth=A.lineWidth,g.dashes=A.dashes,this.setStrokeDashed(g,t.dashes)}}},{key:"setStrokeDashed",value:function(g,t){if(!1!==t)if(void 0!==g.setLineDash){var A=Rh(t)?t:[5,5];g.setLineDash(A)}else console.warn("setLineDash is not supported in this browser. The dashed stroke cannot be used.");else void 0!==g.setLineDash?g.setLineDash([]):console.warn("setLineDash is not supported in this browser. The dashed stroke cannot be used.")}}]),g}();function wR(g,t){var A=Lh(g);if(BT){var e=BT(g);t&&(e=pc(e).call(e,(function(t){return WT(g,t).enumerable}))),A.push.apply(A,e)}return A}function kR(g){for(var t=1;t2&&void 0!==arguments[2]?arguments[2]:this._getViaCoordinates(),I=!1,i=1,o=0,n=this.to,r=this.options.endPointOffset?this.options.endPointOffset.to:0;g.id===this.from.id&&(n=this.from,I=!0,r=this.options.endPointOffset?this.options.endPointOffset.from:0),!1===this.options.arrowStrikethrough&&(r=0);var s=0;do{e=.5*(o+i),A=this.getPoint(e,C);var a=Math.atan2(n.y-A.y,n.x-A.x),d=n.distanceToBorder(t,a)+r-Math.sqrt(Math.pow(A.x-n.x,2)+Math.pow(A.y-n.y,2));if(Math.abs(d)<.2)break;d<0?!1===I?o=e:i=e:!1===I?i=e:o=e,++s}while(o<=i&&s<10);return kR(kR({},A),{},{t:e})}},{key:"_getDistanceToBezierEdge",value:function(g,t,A,e,C,I,i){var o,n,r,s,a,d=1e9,h=g,l=t;for(n=1;n<10;n++)r=.1*n,s=Math.pow(1-r,2)*g+2*r*(1-r)*i.x+Math.pow(r,2)*A,a=Math.pow(1-r,2)*t+2*r*(1-r)*i.y+Math.pow(r,2)*e,n>0&&(d=(o=this._getDistanceToLine(h,l,s,a,C,I))1&&void 0!==arguments[1]?arguments[1]:this.via;if(this.from===this.to){var A=lh(this._getCircleData(),3),e=A[0],C=A[1],I=A[2],i=2*Math.PI*(1-g);return{x:e+I*Math.sin(i),y:C+I-I*(1-Math.cos(i))}}return{x:Math.pow(1-g,2)*this.fromPoint.x+2*g*(1-g)*t.x+Math.pow(g,2)*this.toPoint.x,y:Math.pow(1-g,2)*this.fromPoint.y+2*g*(1-g)*t.y+Math.pow(g,2)*this.toPoint.y}}},{key:"_findBorderPosition",value:function(g,t){return this._findBorderPositionBezier(g,t,this.via)}},{key:"_getDistanceToEdge",value:function(g,t,A,e,C,I){return this._getDistanceToBezierEdge(g,t,A,e,C,I,this.via)}}]),A}(ER);function DR(g){var t=function(){if("undefined"==typeof Reflect||!MT)return!1;if(MT.sham)return!1;if("function"==typeof Proxy)return!0;try{return Boolean.prototype.valueOf.call(MT(Boolean,[],(function(){}))),!0}catch(g){return!1}}();return function(){var A,e=nb(g);if(t){var C=nb(this).constructor;A=MT(e,arguments,C)}else A=e.apply(this,arguments);return Ib(this,A)}}var NR=function(g){Cb(A,g);var t=DR(A);function A(g,e,C){return cn(this,A),t.call(this,g,e,C)}return kd(A,[{key:"_line",value:function(g,t,A){this._bezierCurve(g,t,A)}},{key:"getViaNode",value:function(){return this._getViaCoordinates()}},{key:"_getViaCoordinates",value:function(){var g,t,A=this.options.smooth.roundness,e=this.options.smooth.type,C=Math.abs(this.from.x-this.to.x),I=Math.abs(this.from.y-this.to.y);if("discrete"===e||"diagonalCross"===e){var i,o;i=o=C<=I?A*I:A*C,this.from.x>this.to.x&&(i=-i),this.from.y>=this.to.y&&(o=-o);var n=this.from.x+i,r=this.from.y+o;return"discrete"===e&&(C<=I?n=Cthis.to.x&&(g=-g),this.from.y>=this.to.y&&(t=-t);var y=this.from.x+g,m=this.from.y+t;return C<=I?y=this.from.x<=this.to.x?this.to.xy?this.to.x:y:m=this.from.y>=this.to.y?this.to.y>m?this.to.y:m:this.to.y2&&void 0!==arguments[2]?arguments[2]:{};return this._findBorderPositionBezier(g,t,A.via)}},{key:"_getDistanceToEdge",value:function(g,t,A,e,C,I){var i=arguments.length>6&&void 0!==arguments[6]?arguments[6]:this._getViaCoordinates();return this._getDistanceToBezierEdge(g,t,A,e,C,I,i)}},{key:"getPoint",value:function(g){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:this._getViaCoordinates(),A=g;return{x:Math.pow(1-A,2)*this.fromPoint.x+2*A*(1-A)*t.x+Math.pow(A,2)*this.toPoint.x,y:Math.pow(1-A,2)*this.fromPoint.y+2*A*(1-A)*t.y+Math.pow(A,2)*this.toPoint.y}}}]),A}(ER);function RR(g){var t=function(){if("undefined"==typeof Reflect||!MT)return!1;if(MT.sham)return!1;if("function"==typeof Proxy)return!0;try{return Boolean.prototype.valueOf.call(MT(Boolean,[],(function(){}))),!0}catch(g){return!1}}();return function(){var A,e=nb(g);if(t){var C=nb(this).constructor;A=MT(e,arguments,C)}else A=e.apply(this,arguments);return Ib(this,A)}}var PR=function(g){Cb(A,g);var t=RR(A);function A(g,e,C){return cn(this,A),t.call(this,g,e,C)}return kd(A,[{key:"_getDistanceToBezierEdge2",value:function(g,t,A,e,C,I,i,o){for(var n=1e9,r=g,s=t,a=[0,0,0,0],d=1;d<10;d++){var h=.1*d;a[0]=Math.pow(1-h,3),a[1]=3*h*Math.pow(1-h,2),a[2]=3*Math.pow(h,2)*(1-h),a[3]=Math.pow(h,3);var l=a[0]*g+a[1]*i.x+a[2]*o.x+a[3]*A,c=a[0]*t+a[1]*i.y+a[2]*o.y+a[3]*e;if(d>0){var u=this._getDistanceToLine(r,s,l,c,C,I);n=uMath.abs(I)||!0===this.options.smooth.forceDirection||"horizontal"===this.options.smooth.forceDirection)&&"vertical"!==this.options.smooth.forceDirection?(t=this.from.y,e=this.to.y,g=this.from.x-i*C,A=this.to.x+i*C):(t=this.from.y-i*I,e=this.to.y+i*I,g=this.from.x,A=this.to.x),[{x:g,y:t},{x:A,y:e}]}},{key:"getViaNode",value:function(){return this._getViaCoordinates()}},{key:"_findBorderPosition",value:function(g,t){return this._findBorderPositionBezier(g,t)}},{key:"_getDistanceToEdge",value:function(g,t,A,e,C,I){var i=lh(arguments.length>6&&void 0!==arguments[6]?arguments[6]:this._getViaCoordinates(),2),o=i[0],n=i[1];return this._getDistanceToBezierEdge2(g,t,A,e,C,I,o,n)}},{key:"getPoint",value:function(g){var t=lh(arguments.length>1&&void 0!==arguments[1]?arguments[1]:this._getViaCoordinates(),2),A=t[0],e=t[1],C=g,I=[Math.pow(1-C,3),3*C*Math.pow(1-C,2),3*Math.pow(C,2)*(1-C),Math.pow(C,3)];return{x:I[0]*this.fromPoint.x+I[1]*A.x+I[2]*e.x+I[3]*this.toPoint.x,y:I[0]*this.fromPoint.y+I[1]*A.y+I[2]*e.y+I[3]*this.toPoint.y}}}]),A}(PR);function zR(g){var t=function(){if("undefined"==typeof Reflect||!MT)return!1;if(MT.sham)return!1;if("function"==typeof Proxy)return!0;try{return Boolean.prototype.valueOf.call(MT(Boolean,[],(function(){}))),!0}catch(g){return!1}}();return function(){var A,e=nb(g);if(t){var C=nb(this).constructor;A=MT(e,arguments,C)}else A=e.apply(this,arguments);return Ib(this,A)}}var SR=function(g){Cb(A,g);var t=zR(A);function A(g,e,C){return cn(this,A),t.call(this,g,e,C)}return kd(A,[{key:"_line",value:function(g,t){g.beginPath(),g.moveTo(this.fromPoint.x,this.fromPoint.y),g.lineTo(this.toPoint.x,this.toPoint.y),this.enableShadow(g,t),g.stroke(),this.disableShadow(g,t)}},{key:"getViaNode",value:function(){}},{key:"getPoint",value:function(g){return{x:(1-g)*this.fromPoint.x+g*this.toPoint.x,y:(1-g)*this.fromPoint.y+g*this.toPoint.y}}},{key:"_findBorderPosition",value:function(g,t){var A=this.to,e=this.from;g.id===this.from.id&&(A=this.from,e=this.to);var C=Math.atan2(A.y-e.y,A.x-e.x),I=A.x-e.x,i=A.y-e.y,o=Math.sqrt(I*I+i*i),n=(o-g.distanceToBorder(t,C))/o;return{x:(1-n)*e.x+n*A.x,y:(1-n)*e.y+n*A.y,t:0}}},{key:"_getDistanceToEdge",value:function(g,t,A,e,C,I){return this._getDistanceToLine(g,t,A,e,C,I)}}]),A}(bR),ZR=function(){function g(t,A,e,C,I){if(cn(this,g),void 0===A)throw new Error("No body provided");this.options=av(C),this.globalOptions=C,this.defaultOptions=I,this.body=A,this.imagelist=e,this.id=void 0,this.fromId=void 0,this.toId=void 0,this.selected=!1,this.hover=!1,this.labelDirty=!0,this.baseWidth=this.options.width,this.baseFontSize=this.options.font.size,this.from=void 0,this.to=void 0,this.edgeType=void 0,this.connected=!1,this.labelModule=new qD(this.body,this.options,!0),this.setOptions(t)}return kd(g,[{key:"setOptions",value:function(t){if(t){var A=void 0!==t.physics&&this.options.physics!==t.physics||void 0!==t.hidden&&(this.options.hidden||!1)!==(t.hidden||!1)||void 0!==t.from&&this.options.from!==t.from||void 0!==t.to&&this.options.to!==t.to;g.parseOptions(this.options,t,!0,this.globalOptions),void 0!==t.id&&(this.id=t.id),void 0!==t.from&&(this.fromId=t.from),void 0!==t.to&&(this.toId=t.to),void 0!==t.title&&(this.title=t.title),void 0!==t.value&&(t.value=SD(t.value));var e=[t,this.options,this.defaultOptions];return this.chooser=YD("edge",e),this.updateLabelModule(t),A=this.updateEdgeType()||A,this._setInteractionWidths(),this.connect(),A}}},{key:"getFormattingValues",value:function(){var g=!0===this.options.arrows.to||!0===this.options.arrows.to.enabled,t=!0===this.options.arrows.from||!0===this.options.arrows.from.enabled,A=!0===this.options.arrows.middle||!0===this.options.arrows.middle.enabled,e=this.options.color.inherit,C={toArrow:g,toArrowScale:this.options.arrows.to.scaleFactor,toArrowType:this.options.arrows.to.type,toArrowSrc:this.options.arrows.to.src,toArrowImageWidth:this.options.arrows.to.imageWidth,toArrowImageHeight:this.options.arrows.to.imageHeight,middleArrow:A,middleArrowScale:this.options.arrows.middle.scaleFactor,middleArrowType:this.options.arrows.middle.type,middleArrowSrc:this.options.arrows.middle.src,middleArrowImageWidth:this.options.arrows.middle.imageWidth,middleArrowImageHeight:this.options.arrows.middle.imageHeight,fromArrow:t,fromArrowScale:this.options.arrows.from.scaleFactor,fromArrowType:this.options.arrows.from.type,fromArrowSrc:this.options.arrows.from.src,fromArrowImageWidth:this.options.arrows.from.imageWidth,fromArrowImageHeight:this.options.arrows.from.imageHeight,arrowStrikethrough:this.options.arrowStrikethrough,color:e?void 0:this.options.color.color,inheritsColor:e,opacity:this.options.color.opacity,hidden:this.options.hidden,length:this.options.length,shadow:this.options.shadow.enabled,shadowColor:this.options.shadow.color,shadowSize:this.options.shadow.size,shadowX:this.options.shadow.x,shadowY:this.options.shadow.y,dashes:this.options.dashes,width:this.options.width,background:this.options.background.enabled,backgroundColor:this.options.background.color,backgroundSize:this.options.background.size,backgroundDashes:this.options.background.dashes};if(this.selected||this.hover)if(!0===this.chooser){if(this.selected){var I=this.options.selectionWidth;"function"==typeof I?C.width=I(C.width):"number"==typeof I&&(C.width+=I),C.width=Math.max(C.width,.3/this.body.view.scale),C.color=this.options.color.highlight,C.shadow=this.options.shadow.enabled}else if(this.hover){var i=this.options.hoverWidth;"function"==typeof i?C.width=i(C.width):"number"==typeof i&&(C.width+=i),C.width=Math.max(C.width,.3/this.body.view.scale),C.color=this.options.color.hover,C.shadow=this.options.shadow.enabled}}else"function"==typeof this.chooser&&(this.chooser(C,this.options.id,this.selected,this.hover),void 0!==C.color&&(C.inheritsColor=!1),!1===C.shadow&&(C.shadowColor===this.options.shadow.color&&C.shadowSize===this.options.shadow.size&&C.shadowX===this.options.shadow.x&&C.shadowY===this.options.shadow.y||(C.shadow=!0)));else C.shadow=this.options.shadow.enabled,C.width=Math.max(C.width,.3/this.body.view.scale);return C}},{key:"updateLabelModule",value:function(g){var t=[g,this.options,this.globalOptions,this.defaultOptions];this.labelModule.update(this.options,t),void 0!==this.labelModule.baseSize&&(this.baseFontSize=this.labelModule.baseSize)}},{key:"updateEdgeType",value:function(){var g=this.options.smooth,t=!1,A=!0;return void 0!==this.edgeType&&((this.edgeType instanceof TR&&!0===g.enabled&&"dynamic"===g.type||this.edgeType instanceof BR&&!0===g.enabled&&"cubicBezier"===g.type||this.edgeType instanceof NR&&!0===g.enabled&&"dynamic"!==g.type&&"cubicBezier"!==g.type||this.edgeType instanceof SR&&!1===g.type.enabled)&&(A=!1),!0===A&&(t=this.cleanup())),!0===A?!0===g.enabled?"dynamic"===g.type?(t=!0,this.edgeType=new TR(this.options,this.body,this.labelModule)):"cubicBezier"===g.type?this.edgeType=new BR(this.options,this.body,this.labelModule):this.edgeType=new NR(this.options,this.body,this.labelModule):this.edgeType=new SR(this.options,this.body,this.labelModule):this.edgeType.setOptions(this.options),t}},{key:"connect",value:function(){this.disconnect(),this.from=this.body.nodes[this.fromId]||void 0,this.to=this.body.nodes[this.toId]||void 0,this.connected=void 0!==this.from&&void 0!==this.to,!0===this.connected?(this.from.attachEdge(this),this.to.attachEdge(this)):(this.from&&this.from.detachEdge(this),this.to&&this.to.detachEdge(this)),this.edgeType.connect()}},{key:"disconnect",value:function(){this.from&&(this.from.detachEdge(this),this.from=void 0),this.to&&(this.to.detachEdge(this),this.to=void 0),this.connected=!1}},{key:"getTitle",value:function(){return this.title}},{key:"isSelected",value:function(){return this.selected}},{key:"getValue",value:function(){return this.options.value}},{key:"setValueRange",value:function(g,t,A){if(void 0!==this.options.value){var e=this.options.scaling.customScalingFunction(g,t,A,this.options.value),C=this.options.scaling.max-this.options.scaling.min;if(!0===this.options.scaling.label.enabled){var I=this.options.scaling.label.max-this.options.scaling.label.min;this.options.font.size=this.options.scaling.label.min+e*I}this.options.width=this.options.scaling.min+e*C}else this.options.width=this.baseWidth,this.options.font.size=this.baseFontSize;this._setInteractionWidths(),this.updateLabelModule()}},{key:"_setInteractionWidths",value:function(){"function"==typeof this.options.hoverWidth?this.edgeType.hoverWidth=this.options.hoverWidth(this.options.width):this.edgeType.hoverWidth=this.options.hoverWidth+this.options.width,"function"==typeof this.options.selectionWidth?this.edgeType.selectionWidth=this.options.selectionWidth(this.options.width):this.edgeType.selectionWidth=this.options.selectionWidth+this.options.width}},{key:"draw",value:function(g){var t=this.getFormattingValues();if(!t.hidden){var A=this.edgeType.getViaNode();this.edgeType.drawLine(g,t,this.selected,this.hover,A),this.drawLabel(g,A)}}},{key:"drawArrows",value:function(g){var t=this.getFormattingValues();if(!t.hidden){var A=this.edgeType.getViaNode(),e={};this.edgeType.fromPoint=this.edgeType.from,this.edgeType.toPoint=this.edgeType.to,t.fromArrow&&(e.from=this.edgeType.getArrowData(g,"from",A,this.selected,this.hover,t),!1===t.arrowStrikethrough&&(this.edgeType.fromPoint=e.from.core),t.fromArrowSrc&&(e.from.image=this.imagelist.load(t.fromArrowSrc)),t.fromArrowImageWidth&&(e.from.imageWidth=t.fromArrowImageWidth),t.fromArrowImageHeight&&(e.from.imageHeight=t.fromArrowImageHeight)),t.toArrow&&(e.to=this.edgeType.getArrowData(g,"to",A,this.selected,this.hover,t),!1===t.arrowStrikethrough&&(this.edgeType.toPoint=e.to.core),t.toArrowSrc&&(e.to.image=this.imagelist.load(t.toArrowSrc)),t.toArrowImageWidth&&(e.to.imageWidth=t.toArrowImageWidth),t.toArrowImageHeight&&(e.to.imageHeight=t.toArrowImageHeight)),t.middleArrow&&(e.middle=this.edgeType.getArrowData(g,"middle",A,this.selected,this.hover,t),t.middleArrowSrc&&(e.middle.image=this.imagelist.load(t.middleArrowSrc)),t.middleArrowImageWidth&&(e.middle.imageWidth=t.middleArrowImageWidth),t.middleArrowImageHeight&&(e.middle.imageHeight=t.middleArrowImageHeight)),t.fromArrow&&this.edgeType.drawArrowHead(g,t,this.selected,this.hover,e.from),t.middleArrow&&this.edgeType.drawArrowHead(g,t,this.selected,this.hover,e.middle),t.toArrow&&this.edgeType.drawArrowHead(g,t,this.selected,this.hover,e.to)}}},{key:"drawLabel",value:function(g,t){if(void 0!==this.options.label){var A,e=this.from,C=this.to;if(this.labelModule.differentState(this.selected,this.hover)&&this.labelModule.getTextSize(g,this.selected,this.hover),e.id!=C.id){this.labelModule.pointToSelf=!1,A=this.edgeType.getPoint(.5,t),g.save();var I=this._getRotation(g);0!=I.angle&&(g.translate(I.x,I.y),g.rotate(I.angle)),this.labelModule.draw(g,A.x,A.y,this.selected,this.hover),g.restore()}else{this.labelModule.pointToSelf=!0;var i=UD(g,this.options.selfReference.angle,this.options.selfReference.size,e);A=this._pointOnCircle(i.x,i.y,this.options.selfReference.size,this.options.selfReference.angle),this.labelModule.draw(g,A.x,A.y,this.selected,this.hover)}}}},{key:"getItemsOnPoint",value:function(g){var t=[];if(this.labelModule.visible()){var A=this._getRotation();WD(this.labelModule.getSize(),g,A)&&t.push({edgeId:this.id,labelId:0})}var e={left:g.x,top:g.y};return this.isOverlappingWith(e)&&t.push({edgeId:this.id}),t}},{key:"isOverlappingWith",value:function(g){if(this.connected){var t=this.from.x,A=this.from.y,e=this.to.x,C=this.to.y,I=g.left,i=g.top;return this.edgeType.getDistanceToEdge(t,A,e,C,I,i)<10}return!1}},{key:"_getRotation",value:function(g){var t=this.edgeType.getViaNode(),A=this.edgeType.getPoint(.5,t);void 0!==g&&this.labelModule.calculateLabelSize(g,this.selected,this.hover,A.x,A.y);var e={x:A.x,y:this.labelModule.size.yLine,angle:0};if(!this.labelModule.visible())return e;if("horizontal"===this.options.font.align)return e;var C=this.from.y-this.to.y,I=this.from.x-this.to.x,i=Math.atan2(C,I);return(i<-1&&I<0||i>0&&I<0)&&(i+=Math.PI),e.angle=i,e}},{key:"_pointOnCircle",value:function(g,t,A,e){return{x:g+A*Math.cos(e),y:t-A*Math.sin(e)}}},{key:"select",value:function(){this.selected=!0}},{key:"unselect",value:function(){this.selected=!1}},{key:"cleanup",value:function(){return this.edgeType.cleanup()}},{key:"remove",value:function(){this.cleanup(),this.disconnect(),delete this.body.edges[this.id]}},{key:"endPointsValid",value:function(){return void 0!==this.body.nodes[this.fromId]&&void 0!==this.body.nodes[this.toId]}}],[{key:"parseOptions",value:function(g,t){var A=arguments.length>2&&void 0!==arguments[2]&&arguments[2],e=arguments.length>3&&void 0!==arguments[3]?arguments[3]:{},C=arguments.length>4&&void 0!==arguments[4]&&arguments[4];if(Xf(["endPointOffset","arrowStrikethrough","id","from","hidden","hoverWidth","labelHighlightBold","length","line","opacity","physics","scaling","selectionWidth","selfReferenceSize","selfReference","to","title","value","width","font","chosen","widthConstraint"],g,t,A),void 0!==t.endPointOffset&&void 0!==t.endPointOffset.from&&(Ym(t.endPointOffset.from)?g.endPointOffset.from=t.endPointOffset.from:(g.endPointOffset.from=void 0!==e.endPointOffset.from?e.endPointOffset.from:0,console.error("endPointOffset.from is not a valid number"))),void 0!==t.endPointOffset&&void 0!==t.endPointOffset.to&&(Ym(t.endPointOffset.to)?g.endPointOffset.to=t.endPointOffset.to:(g.endPointOffset.to=void 0!==e.endPointOffset.to?e.endPointOffset.to:0,console.error("endPointOffset.to is not a valid number"))),QD(t.label)?g.label=t.label:QD(g.label)||(g.label=void 0),dv(g,t,"smooth",e),dv(g,t,"shadow",e),dv(g,t,"background",e),void 0!==t.dashes&&null!==t.dashes?g.dashes=t.dashes:!0===A&&null===t.dashes&&(g.dashes=$c(e.dashes)),void 0!==t.scaling&&null!==t.scaling?(void 0!==t.scaling.min&&(g.scaling.min=t.scaling.min),void 0!==t.scaling.max&&(g.scaling.max=t.scaling.max),dv(g.scaling,t.scaling,"label",e.scaling)):!0===A&&null===t.scaling&&(g.scaling=$c(e.scaling)),void 0!==t.arrows&&null!==t.arrows)if("string"==typeof t.arrows){var I=t.arrows.toLowerCase();g.arrows.to.enabled=-1!=Xc(I).call(I,"to"),g.arrows.middle.enabled=-1!=Xc(I).call(I,"middle"),g.arrows.from.enabled=-1!=Xc(I).call(I,"from")}else{if("object"!==yd(t.arrows))throw new Error("The arrow newOptions can only be an object or a string. Refer to the documentation. You used:"+eu(t.arrows));dv(g.arrows,t.arrows,"to",e.arrows),dv(g.arrows,t.arrows,"middle",e.arrows),dv(g.arrows,t.arrows,"from",e.arrows)}else!0===A&&null===t.arrows&&(g.arrows=$c(e.arrows));if(void 0!==t.color&&null!==t.color){var i=Uf(t.color)?{color:t.color,highlight:t.color,hover:t.color,inherit:!1,opacity:1}:t.color,o=g.color;if(C)qf(o,e.color,!1,A);else for(var n in o)Object.prototype.hasOwnProperty.call(o,n)&&delete o[n];if(Uf(o))o.color=o,o.highlight=o,o.hover=o,o.inherit=!1,void 0===i.opacity&&(o.opacity=1);else{var r=!1;void 0!==i.color&&(o.color=i.color,r=!0),void 0!==i.highlight&&(o.highlight=i.highlight,r=!0),void 0!==i.hover&&(o.hover=i.hover,r=!0),void 0!==i.inherit&&(o.inherit=i.inherit),void 0!==i.opacity&&(o.opacity=Math.min(1,Math.max(0,i.opacity))),!0===r?o.inherit=!1:void 0===o.inherit&&(o.inherit="from")}}else!0===A&&null===t.color&&(g.color=av(e.color));!0===A&&null===t.font&&(g.font=av(e.font)),Object.prototype.hasOwnProperty.call(t,"selfReferenceSize")&&(console.warn("The selfReferenceSize property has been deprecated. Please use selfReference property instead. The selfReference can be set like thise selfReference:{size:30, angle:Math.PI / 4}"),g.selfReference.size=t.selfReferenceSize)}}]),g}(),FR=function(){function g(t,A,e){var C,I=this;cn(this,g),this.body=t,this.images=A,this.groups=e,this.body.functions.createEdge=je(C=this.create).call(C,this),this.edgesListeners={add:function(g,t){I.add(t.items)},update:function(g,t){I.update(t.items)},remove:function(g,t){I.remove(t.items)}},this.options={},this.defaultOptions={arrows:{to:{enabled:!1,scaleFactor:1,type:"arrow"},middle:{enabled:!1,scaleFactor:1,type:"arrow"},from:{enabled:!1,scaleFactor:1,type:"arrow"}},endPointOffset:{from:0,to:0},arrowStrikethrough:!0,color:{color:"#848484",highlight:"#848484",hover:"#848484",inherit:"from",opacity:1},dashes:!1,font:{color:"#343434",size:14,face:"arial",background:"none",strokeWidth:2,strokeColor:"#ffffff",align:"horizontal",multi:!1,vadjust:0,bold:{mod:"bold"},boldital:{mod:"bold italic"},ital:{mod:"italic"},mono:{mod:"",size:15,face:"courier new",vadjust:2}},hidden:!1,hoverWidth:1.5,label:void 0,labelHighlightBold:!0,length:void 0,physics:!0,scaling:{min:1,max:15,label:{enabled:!0,min:14,max:30,maxVisible:30,drawThreshold:5},customScalingFunction:function(g,t,A,e){if(t===g)return.5;var C=1/(t-g);return Math.max(0,(e-g)*C)}},selectionWidth:1.5,selfReference:{size:20,angle:Math.PI/4,renderBehindTheNode:!0},shadow:{enabled:!1,color:"rgba(0,0,0,0.5)",size:10,x:5,y:5},background:{enabled:!1,color:"rgba(111,111,111,1)",size:10,dashes:!1},smooth:{enabled:!0,type:"dynamic",forceDirection:"none",roundness:.5},title:void 0,width:1,value:void 0},qf(this.options,this.defaultOptions),this.bindEventListeners()}return kd(g,[{key:"bindEventListeners",value:function(){var g,t,A=this;this.body.emitter.on("_forceDisableDynamicCurves",(function(g){var t=!(arguments.length>1&&void 0!==arguments[1])||arguments[1];"dynamic"===g&&(g="continuous");var e=!1;for(var C in A.body.edges)if(Object.prototype.hasOwnProperty.call(A.body.edges,C)){var I=A.body.edges[C],i=A.body.data.edges.get(C);if(null!=i){var o=i.smooth;void 0!==o&&!0===o.enabled&&"dynamic"===o.type&&(void 0===g?I.setOptions({smooth:!1}):I.setOptions({smooth:{type:g}}),e=!0)}}!0===t&&!0===e&&A.body.emitter.emit("_dataChanged")})),this.body.emitter.on("_dataUpdated",(function(){A.reconnectEdges()})),this.body.emitter.on("refreshEdges",je(g=this.refresh).call(g,this)),this.body.emitter.on("refresh",je(t=this.refresh).call(t,this)),this.body.emitter.on("destroy",(function(){tv(A.edgesListeners,(function(g,t){A.body.data.edges&&A.body.data.edges.off(t,g)})),delete A.body.functions.createEdge,delete A.edgesListeners.add,delete A.edgesListeners.update,delete A.edgesListeners.remove,delete A.edgesListeners}))}},{key:"setOptions",value:function(g){if(void 0!==g){ZR.parseOptions(this.options,g,!0,this.defaultOptions,!0);var t=!1;if(void 0!==g.smooth)for(var A in this.body.edges)Object.prototype.hasOwnProperty.call(this.body.edges,A)&&(t=this.body.edges[A].updateEdgeType()||t);if(void 0!==g.font)for(var e in this.body.edges)Object.prototype.hasOwnProperty.call(this.body.edges,e)&&this.body.edges[e].updateLabelModule();void 0===g.hidden&&void 0===g.physics&&!0!==t||this.body.emitter.emit("_dataChanged")}}},{key:"setData",value:function(g){var t=this,A=arguments.length>1&&void 0!==arguments[1]&&arguments[1],e=this.body.data.edges;if(kD("id",g))this.body.data.edges=g;else if(Rh(g))this.body.data.edges=new mD,this.body.data.edges.add(g);else{if(g)throw new TypeError("Array or DataSet expected");this.body.data.edges=new mD}if(e&&tv(this.edgesListeners,(function(g,t){e.off(t,g)})),this.body.edges={},this.body.data.edges){tv(this.edgesListeners,(function(g,A){t.body.data.edges.on(A,g)}));var C=this.body.data.edges.getIds();this.add(C,!0)}this.body.emitter.emit("_adjustEdgesForHierarchicalLayout"),!1===A&&this.body.emitter.emit("_dataChanged")}},{key:"add",value:function(g){for(var t=arguments.length>1&&void 0!==arguments[1]&&arguments[1],A=this.body.edges,e=this.body.data.edges,C=0;C1&&void 0!==arguments[1])||arguments[1];if(0!==g.length){var A=this.body.edges;tv(g,(function(g){var t=A[g];void 0!==t&&t.remove()})),t&&this.body.emitter.emit("_dataChanged")}}},{key:"refresh",value:function(){var g=this;tv(this.body.edges,(function(t,A){var e=g.body.data.edges.get(A);void 0!==e&&t.setOptions(e)}))}},{key:"create",value:function(g){return new ZR(g,this.body,this.images,this.options,this.defaultOptions)}},{key:"reconnectEdges",value:function(){var g,t=this.body.nodes,A=this.body.edges;for(g in t)Object.prototype.hasOwnProperty.call(t,g)&&(t[g].edges=[]);for(g in A)if(Object.prototype.hasOwnProperty.call(A,g)){var e=A[g];e.from=null,e.to=null,e.connect()}}},{key:"getConnectedNodes",value:function(g){var t=[];if(void 0!==this.body.edges[g]){var A=this.body.edges[g];void 0!==A.fromId&&t.push(A.fromId),void 0!==A.toId&&t.push(A.toId)}return t}},{key:"_updateState",value:function(){this._addMissingEdges(),this._removeInvalidEdges()}},{key:"_removeInvalidEdges",value:function(){var g=this,t=[];tv(this.body.edges,(function(A,e){var C=g.body.nodes[A.toId],I=g.body.nodes[A.fromId];void 0!==C&&!0===C.isCluster||void 0!==I&&!0===I.isCluster||void 0!==C&&void 0!==I||t.push(e)})),this.remove(t,!1)}},{key:"_addMissingEdges",value:function(){var g=this.body.data.edges;if(null!=g){var t=this.body.edges,A=[];Cl(g).call(g,(function(g,e){void 0===t[e]&&A.push(e)})),this.add(A,!0)}}}]),g}(),GR=function(){function g(t,A,e){cn(this,g),this.body=t,this.physicsBody=A,this.barnesHutTree,this.setOptions(e),this._rng=Ff("BARNES HUT SOLVER")}return kd(g,[{key:"setOptions",value:function(g){this.options=g,this.thetaInversed=1/this.options.theta,this.overlapAvoidanceFactor=1-Math.max(0,Math.min(1,this.options.avoidOverlap))}},{key:"solve",value:function(){if(0!==this.options.gravitationalConstant&&this.physicsBody.physicsNodeIndices.length>0){var g,t=this.body.nodes,A=this.physicsBody.physicsNodeIndices,e=A.length,C=this._formBarnesHutTree(t,A);this.barnesHutTree=C;for(var I=0;I0&&this._getForceContributions(C.root,g)}}},{key:"_getForceContributions",value:function(g,t){this._getForceContribution(g.children.NW,t),this._getForceContribution(g.children.NE,t),this._getForceContribution(g.children.SW,t),this._getForceContribution(g.children.SE,t)}},{key:"_getForceContribution",value:function(g,t){if(g.childrenCount>0){var A=g.centerOfMass.x-t.x,e=g.centerOfMass.y-t.y,C=Math.sqrt(A*A+e*e);C*g.calcSize>this.thetaInversed?this._calculateForces(C,A,e,t,g):4===g.childrenCount?this._getForceContributions(g,t):g.children.data.id!=t.id&&this._calculateForces(C,A,e,t,g)}}},{key:"_calculateForces",value:function(g,t,A,e,C){0===g&&(t=g=.1),this.overlapAvoidanceFactor<1&&e.shape.radius&&(g=Math.max(.1+this.overlapAvoidanceFactor*e.shape.radius,g-e.shape.radius));var I=this.options.gravitationalConstant*C.mass*e.options.mass/Math.pow(g,3),i=t*I,o=A*I;this.physicsBody.forces[e.id].x+=i,this.physicsBody.forces[e.id].y+=o}},{key:"_formBarnesHutTree",value:function(g,t){for(var A,e=t.length,C=g[t[0]].x,I=g[t[0]].y,i=g[t[0]].x,o=g[t[0]].y,n=1;n0&&(si&&(i=s),ao&&(o=a))}var d=Math.abs(i-C)-Math.abs(o-I);d>0?(I-=.5*d,o+=.5*d):(C+=.5*d,i-=.5*d);var h=Math.max(1e-5,Math.abs(i-C)),l=.5*h,c=.5*(C+i),u=.5*(I+o),p={root:{centerOfMass:{x:0,y:0},mass:0,range:{minX:c-l,maxX:c+l,minY:u-l,maxY:u+l},size:h,calcSize:1/h,children:{data:null},maxWidth:0,level:0,childrenCount:4}};this._splitBranch(p.root);for(var f=0;f0&&this._placeInTree(p.root,A);return p}},{key:"_updateBranchMass",value:function(g,t){var A=g.centerOfMass,e=g.mass+t.options.mass,C=1/e;A.x=A.x*g.mass+t.x*t.options.mass,A.x*=C,A.y=A.y*g.mass+t.y*t.options.mass,A.y*=C,g.mass=e;var I=Math.max(Math.max(t.height,t.radius),t.width);g.maxWidth=g.maxWidtht.x?C.maxY>t.y?"NW":"SW":C.maxY>t.y?"NE":"SE",this._placeInRegion(g,t,e)}},{key:"_placeInRegion",value:function(g,t,A){var e=g.children[A];switch(e.childrenCount){case 0:e.children.data=t,e.childrenCount=1,this._updateBranchMass(e,t);break;case 1:e.children.data.x===t.x&&e.children.data.y===t.y?(t.x+=this._rng(),t.y+=this._rng()):(this._splitBranch(e),this._placeInTree(e,t));break;case 4:this._placeInTree(e,t)}}},{key:"_splitBranch",value:function(g){var t=null;1===g.childrenCount&&(t=g.children.data,g.mass=0,g.centerOfMass.x=0,g.centerOfMass.y=0),g.childrenCount=4,g.children.data=null,this._insertRegion(g,"NW"),this._insertRegion(g,"NE"),this._insertRegion(g,"SW"),this._insertRegion(g,"SE"),null!=t&&this._placeInTree(g,t)}},{key:"_insertRegion",value:function(g,t){var A,e,C,I,i=.5*g.size;switch(t){case"NW":A=g.range.minX,e=g.range.minX+i,C=g.range.minY,I=g.range.minY+i;break;case"NE":A=g.range.minX+i,e=g.range.maxX,C=g.range.minY,I=g.range.minY+i;break;case"SW":A=g.range.minX,e=g.range.minX+i,C=g.range.minY+i,I=g.range.maxY;break;case"SE":A=g.range.minX+i,e=g.range.maxX,C=g.range.minY+i,I=g.range.maxY}g.children[t]={centerOfMass:{x:0,y:0},mass:0,range:{minX:A,maxX:e,minY:C,maxY:I},size:.5*g.size,calcSize:2*g.calcSize,children:{data:null},maxWidth:0,level:g.level+1,childrenCount:0}}},{key:"_debug",value:function(g,t){void 0!==this.barnesHutTree&&(g.lineWidth=1,this._drawBranch(this.barnesHutTree.root,g,t))}},{key:"_drawBranch",value:function(g,t,A){void 0===A&&(A="#FF0000"),4===g.childrenCount&&(this._drawBranch(g.children.NW,t),this._drawBranch(g.children.NE,t),this._drawBranch(g.children.SE,t),this._drawBranch(g.children.SW,t)),t.strokeStyle=A,t.beginPath(),t.moveTo(g.range.minX,g.range.minY),t.lineTo(g.range.maxX,g.range.minY),t.stroke(),t.beginPath(),t.moveTo(g.range.maxX,g.range.minY),t.lineTo(g.range.maxX,g.range.maxY),t.stroke(),t.beginPath(),t.moveTo(g.range.maxX,g.range.maxY),t.lineTo(g.range.minX,g.range.maxY),t.stroke(),t.beginPath(),t.moveTo(g.range.minX,g.range.maxY),t.lineTo(g.range.minX,g.range.minY),t.stroke()}}]),g}(),jR=function(){function g(t,A,e){cn(this,g),this._rng=Ff("REPULSION SOLVER"),this.body=t,this.physicsBody=A,this.setOptions(e)}return kd(g,[{key:"setOptions",value:function(g){this.options=g}},{key:"solve",value:function(){for(var g,t,A,e,C,I,i,o,n=this.body.nodes,r=this.physicsBody.physicsNodeIndices,s=this.physicsBody.forces,a=this.options.nodeDistance,d=-2/3/a,h=0;h0){var I=C.edges.length+1,i=this.options.centralGravity*I*C.options.mass;e[C.id].x=t*i,e[C.id].y=A*i}}}]),A}(WR),HR=function(){function g(t){cn(this,g),this.body=t,this.physicsBody={physicsNodeIndices:[],physicsEdgeIndices:[],forces:{},velocities:{}},this.physicsEnabled=!0,this.simulationInterval=1e3/60,this.requiresTimeout=!0,this.previousStates={},this.referenceState={},this.freezeCache={},this.renderTimer=void 0,this.adaptiveTimestep=!1,this.adaptiveTimestepEnabled=!1,this.adaptiveCounter=0,this.adaptiveInterval=3,this.stabilized=!1,this.startedStabilization=!1,this.stabilizationIterations=0,this.ready=!1,this.options={},this.defaultOptions={enabled:!0,barnesHut:{theta:.5,gravitationalConstant:-2e3,centralGravity:.3,springLength:95,springConstant:.04,damping:.09,avoidOverlap:0},forceAtlas2Based:{theta:.5,gravitationalConstant:-50,centralGravity:.01,springConstant:.08,springLength:100,damping:.4,avoidOverlap:0},repulsion:{centralGravity:.2,springLength:200,springConstant:.05,nodeDistance:100,damping:.09,avoidOverlap:0},hierarchicalRepulsion:{centralGravity:0,springLength:100,springConstant:.01,nodeDistance:120,damping:.09},maxVelocity:50,minVelocity:.75,solver:"barnesHut",stabilization:{enabled:!0,iterations:1e3,updateInterval:50,onlyDynamicEdges:!1,fit:!0},timestep:.5,adaptiveTimestep:!0,wind:{x:0,y:0}},fe(this.options,this.defaultOptions),this.timestep=.5,this.layoutFailed=!1,this.bindEventListeners()}return kd(g,[{key:"bindEventListeners",value:function(){var g=this;this.body.emitter.on("initPhysics",(function(){g.initPhysics()})),this.body.emitter.on("_layoutFailed",(function(){g.layoutFailed=!0})),this.body.emitter.on("resetPhysics",(function(){g.stopSimulation(),g.ready=!1})),this.body.emitter.on("disablePhysics",(function(){g.physicsEnabled=!1,g.stopSimulation()})),this.body.emitter.on("restorePhysics",(function(){g.setOptions(g.options),!0===g.ready&&g.startSimulation()})),this.body.emitter.on("startSimulation",(function(){!0===g.ready&&g.startSimulation()})),this.body.emitter.on("stopSimulation",(function(){g.stopSimulation()})),this.body.emitter.on("destroy",(function(){g.stopSimulation(!1),g.body.emitter.off()})),this.body.emitter.on("_dataChanged",(function(){g.updatePhysicsData()}))}},{key:"setOptions",value:function(g){if(void 0!==g)if(!1===g)this.options.enabled=!1,this.physicsEnabled=!1,this.stopSimulation();else if(!0===g)this.options.enabled=!0,this.physicsEnabled=!0,this.startSimulation();else{this.physicsEnabled=!0,Jf(["stabilization"],this.options,g),dv(this.options,g,"stabilization"),void 0===g.enabled&&(this.options.enabled=!0),!1===this.options.enabled&&(this.physicsEnabled=!1,this.stopSimulation());var t=this.options.wind;t&&(("number"!=typeof t.x||jm(t.x))&&(t.x=0),("number"!=typeof t.y||jm(t.y))&&(t.y=0)),this.timestep=this.options.timestep}this.init()}},{key:"init",value:function(){var g;"forceAtlas2Based"===this.options.solver?(g=this.options.forceAtlas2Based,this.nodesSolver=new UR(this.body,this.physicsBody,g),this.edgesSolver=new VR(this.body,this.physicsBody,g),this.gravitySolver=new KR(this.body,this.physicsBody,g)):"repulsion"===this.options.solver?(g=this.options.repulsion,this.nodesSolver=new jR(this.body,this.physicsBody,g),this.edgesSolver=new VR(this.body,this.physicsBody,g),this.gravitySolver=new WR(this.body,this.physicsBody,g)):"hierarchicalRepulsion"===this.options.solver?(g=this.options.hierarchicalRepulsion,this.nodesSolver=new LR(this.body,this.physicsBody,g),this.edgesSolver=new YR(this.body,this.physicsBody,g),this.gravitySolver=new WR(this.body,this.physicsBody,g)):(g=this.options.barnesHut,this.nodesSolver=new GR(this.body,this.physicsBody,g),this.edgesSolver=new VR(this.body,this.physicsBody,g),this.gravitySolver=new WR(this.body,this.physicsBody,g)),this.modelOptions=g}},{key:"initPhysics",value:function(){!0===this.physicsEnabled&&!0===this.options.enabled?!0===this.options.stabilization.enabled?this.stabilize():(this.stabilized=!1,this.ready=!0,this.body.emitter.emit("fit",{},this.layoutFailed),this.startSimulation()):(this.ready=!0,this.body.emitter.emit("fit"))}},{key:"startSimulation",value:function(){var g;!0===this.physicsEnabled&&!0===this.options.enabled?(this.stabilized=!1,this.adaptiveTimestep=!1,this.body.emitter.emit("_resizeNodes"),void 0===this.viewFunction&&(this.viewFunction=je(g=this.simulationStep).call(g,this),this.body.emitter.on("initRedraw",this.viewFunction),this.body.emitter.emit("_startRendering"))):this.body.emitter.emit("_redraw")}},{key:"stopSimulation",value:function(){var g=!(arguments.length>0&&void 0!==arguments[0])||arguments[0];this.stabilized=!0,!0===g&&this._emitStabilized(),void 0!==this.viewFunction&&(this.body.emitter.off("initRedraw",this.viewFunction),this.viewFunction=void 0,!0===g&&this.body.emitter.emit("_stopRendering"))}},{key:"simulationStep",value:function(){var g=Qh();this.physicsTick(),(Qh()-g<.4*this.simulationInterval||!0===this.runDoubleSpeed)&&!1===this.stabilized&&(this.physicsTick(),this.runDoubleSpeed=!0),!0===this.stabilized&&this.stopSimulation()}},{key:"_emitStabilized",value:function(){var g=this,t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:this.stabilizationIterations;(this.stabilizationIterations>1||!0===this.startedStabilization)&&wu((function(){g.body.emitter.emit("stabilized",{iterations:t}),g.startedStabilization=!1,g.stabilizationIterations=0}),0)}},{key:"physicsStep",value:function(){this.gravitySolver.solve(),this.nodesSolver.solve(),this.edgesSolver.solve(),this.moveNodes()}},{key:"adjustTimeStep",value:function(){!0===this._evaluateStepQuality()?this.timestep=1.2*this.timestep:this.timestep/1.2.3))return!1;return!0}},{key:"moveNodes",value:function(){for(var g=this.physicsBody.physicsNodeIndices,t=0,A=0,e=0;ee&&(g=g>0?e:-e),g}},{key:"_performStep",value:function(g){var t=this.body.nodes[g],A=this.physicsBody.forces[g];this.options.wind&&(A.x+=this.options.wind.x,A.y+=this.options.wind.y);var e=this.physicsBody.velocities[g];return this.previousStates[g]={x:t.x,y:t.y,vx:e.x,vy:e.y},!1===t.options.fixed.x?(e.x=this.calculateComponentVelocity(e.x,A.x,t.options.mass),t.x+=e.x*this.timestep):(A.x=0,e.x=0),!1===t.options.fixed.y?(e.y=this.calculateComponentVelocity(e.y,A.y,t.options.mass),t.y+=e.y*this.timestep):(A.y=0,e.y=0),Math.sqrt(Math.pow(e.x,2)+Math.pow(e.y,2))}},{key:"_freezeNodes",value:function(){var g=this.body.nodes;for(var t in g)if(Object.prototype.hasOwnProperty.call(g,t)&&g[t].x&&g[t].y){var A=g[t].options.fixed;this.freezeCache[t]={x:A.x,y:A.y},A.x=!0,A.y=!0}}},{key:"_restoreFrozenNodes",value:function(){var g=this.body.nodes;for(var t in g)Object.prototype.hasOwnProperty.call(g,t)&&void 0!==this.freezeCache[t]&&(g[t].options.fixed.x=this.freezeCache[t].x,g[t].options.fixed.y=this.freezeCache[t].y);this.freezeCache={}}},{key:"stabilize",value:function(){var g=this,t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:this.options.stabilization.iterations;"number"!=typeof t&&(t=this.options.stabilization.iterations,console.error("The stabilize method needs a numeric amount of iterations. Switching to default: ",t)),0!==this.physicsBody.physicsNodeIndices.length?(this.adaptiveTimestep=this.options.adaptiveTimestep,this.body.emitter.emit("_resizeNodes"),this.stopSimulation(),this.stabilized=!1,this.body.emitter.emit("_blockRedraw"),this.targetIterations=t,!0===this.options.stabilization.onlyDynamicEdges&&this._freezeNodes(),this.stabilizationIterations=0,wu((function(){return g._stabilizationBatch()}),0)):this.ready=!0}},{key:"_startStabilizing",value:function(){return!0!==this.startedStabilization&&(this.body.emitter.emit("startStabilizing"),this.startedStabilization=!0,!0)}},{key:"_stabilizationBatch",value:function(){var g=this,t=function(){return!1===g.stabilized&&g.stabilizationIterations1&&void 0!==arguments[1]?arguments[1]:[],e=1e9,C=-1e9,I=1e9,i=-1e9;if(A.length>0)for(var o=0;o(t=g[A[o]]).shape.boundingBox.left&&(I=t.shape.boundingBox.left),it.shape.boundingBox.top&&(e=t.shape.boundingBox.top),C1&&void 0!==arguments[1]?arguments[1]:[],e=1e9,C=-1e9,I=1e9,i=-1e9;if(A.length>0)for(var o=0;o(t=g[A[o]]).x&&(I=t.x),it.y&&(e=t.y),C=g&&A.push(C.id)}for(var I=0;I0&&void 0!==arguments[0]?arguments[0]:{},A=!(arguments.length>1&&void 0!==arguments[1])||arguments[1];if(void 0===t.joinCondition)throw new Error("Cannot call clusterByNodeData without a joinCondition function in the options.");t=this._checkOptions(t);var e={},C={};tv(this.body.nodes,(function(A,I){A.options&&!0===t.joinCondition(A.options)&&(e[I]=A,tv(A.edges,(function(t){void 0===g.clusteredEdges[t.id]&&(C[t.id]=t)})))})),this._cluster(e,C,t,A)}},{key:"clusterByEdgeCount",value:function(g,t){var A=this,e=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];t=this._checkOptions(t);for(var C,I,i,o=[],n={},r=function(){var e={},r={},a=A.body.nodeIndices[s],d=A.body.nodes[a];if(void 0===n[a]){i=0,I=[];for(var h=0;h0&&Lh(r).length>0&&!0===c){var f=function(){for(var g=0;g1&&void 0!==arguments[1])||arguments[1];this.clusterByEdgeCount(1,g,t)}},{key:"clusterBridges",value:function(g){var t=!(arguments.length>1&&void 0!==arguments[1])||arguments[1];this.clusterByEdgeCount(2,g,t)}},{key:"clusterByConnection",value:function(g,t){var A,e=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];if(void 0===g)throw new Error("No nodeId supplied to clusterByConnection!");if(void 0===this.body.nodes[g])throw new Error("The nodeId given to clusterByConnection does not exist!");var C=this.body.nodes[g];void 0===(t=this._checkOptions(t,C)).clusterNodeProperties.x&&(t.clusterNodeProperties.x=C.x),void 0===t.clusterNodeProperties.y&&(t.clusterNodeProperties.y=C.y),void 0===t.clusterNodeProperties.fixed&&(t.clusterNodeProperties.fixed={},t.clusterNodeProperties.fixed.x=C.options.fixed.x,t.clusterNodeProperties.fixed.y=C.options.fixed.y);var I={},i={},o=C.id,n=XR.cloneOptions(C);I[o]=C;for(var r=0;r-1&&(i[p.id]=p)}this._cluster(I,i,t,e)}},{key:"_createClusterEdges",value:function(g,t,A,e){for(var C,I,i,o,n,r,s=Lh(g),a=[],d=0;d0&&void 0!==arguments[0]?arguments[0]:{};return void 0===g.clusterEdgeProperties&&(g.clusterEdgeProperties={}),void 0===g.clusterNodeProperties&&(g.clusterNodeProperties={}),g}},{key:"_cluster",value:function(g,t,A){var e=!(arguments.length>3&&void 0!==arguments[3])||arguments[3],C=[];for(var I in g)Object.prototype.hasOwnProperty.call(g,I)&&void 0!==this.clusteredNodes[I]&&C.push(I);for(var i=0;iC?t.x:C,I=t.yi?t.y:i;return{x:.5*(e+C),y:.5*(I+i)}}},{key:"openCluster",value:function(g,t){var A=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];if(void 0===g)throw new Error("No clusterNodeId supplied to openCluster.");var e=this.body.nodes[g];if(void 0===e)throw new Error("The clusterNodeId supplied to openCluster does not exist.");if(!0!==e.isCluster||void 0===e.containedNodes||void 0===e.containedEdges)throw new Error("The node:"+g+" is not a valid cluster.");var C=this.findNode(g),I=Xc(C).call(C,g)-1;if(I>=0){var i=C[I];return this.body.nodes[i]._openChildCluster(g),delete this.body.nodes[g],void(!0===A&&this.body.emitter.emit("_dataChanged"))}var o=e.containedNodes,n=e.containedEdges;if(void 0!==t&&void 0!==t.releaseFunction&&"function"==typeof t.releaseFunction){var r={},s={x:e.x,y:e.y};for(var a in o)if(Object.prototype.hasOwnProperty.call(o,a)){var d=this.body.nodes[a];r[a]={x:d.x,y:d.y}}var h=t.releaseFunction(s,r);for(var l in o)if(Object.prototype.hasOwnProperty.call(o,l)){var c=this.body.nodes[l];void 0!==h[l]&&(c.x=void 0===h[l].x?e.x:h[l].x,c.y=void 0===h[l].y?e.y:h[l].y)}}else tv(o,(function(g){!1===g.options.fixed.x&&(g.x=e.x),!1===g.options.fixed.y&&(g.y=e.y)}));for(var u in o)if(Object.prototype.hasOwnProperty.call(o,u)){var p=this.body.nodes[u];p.vx=e.vx,p.vy=e.vy,p.setOptions({physics:!0}),delete this.clusteredNodes[u]}for(var f=[],v=0;v0&&C<100;){var I=t.pop();if(void 0!==I){var i=this.body.edges[I];if(void 0!==i){C++;var o=i.clusteringEdgeReplacingIds;if(void 0===o)e.push(I);else for(var n=0;ne&&(e=I.edges.length),g+=I.edges.length,t+=Math.pow(I.edges.length,2),A+=1}g/=A;var i=(t/=A)-Math.pow(g,2),o=Math.sqrt(i),n=Math.floor(g+2*o);return n>e&&(n=e),n}},{key:"_createClusteredEdge",value:function(g,t,A,e,C){var I=XR.cloneOptions(A,"edge");qf(I,e),I.from=g,I.to=t,I.id="clusterEdge:"+rD(),void 0!==C&&qf(I,C);var i=this.body.functions.createEdge(I);return i.clusteringEdgeReplacingIds=[A.id],i.connect(),this.body.edges[i.id]=i,i}},{key:"_clusterEdges",value:function(g,t,A,e){if(t instanceof ZR){var C=t,I={};I[C.id]=C,t=I}if(g instanceof jN){var i=g,o={};o[i.id]=i,g=o}if(null==A)throw new Error("_clusterEdges: parameter clusterNode required");for(var n in void 0===e&&(e=A.clusterEdgeProperties),this._createClusterEdges(g,t,A,e),t)if(Object.prototype.hasOwnProperty.call(t,n)&&void 0!==this.body.edges[n]){var r=this.body.edges[n];this._backupEdgeOptions(r),r.setOptions({physics:!1})}for(var s in g)Object.prototype.hasOwnProperty.call(g,s)&&(this.clusteredNodes[s]={clusterId:A.id,node:this.body.nodes[s]},this.body.nodes[s].setOptions({physics:!1}))}},{key:"_getClusterNodeForNode",value:function(g){if(void 0!==g){var t=this.clusteredNodes[g];if(void 0!==t){var A=t.clusterId;if(void 0!==A)return this.body.nodes[A]}}}},{key:"_filter",value:function(g,t){var A=[];return tv(g,(function(g){t(g)&&A.push(g)})),A}},{key:"_updateState",value:function(){var g,t=this,A=[],e={},C=function(g){tv(t.body.nodes,(function(t){!0===t.isCluster&&g(t)}))};for(g in this.clusteredNodes){if(Object.prototype.hasOwnProperty.call(this.clusteredNodes,g))void 0===this.body.nodes[g]&&A.push(g)}C((function(g){for(var t=0;t0}g.endPointsValid()&&C||(e[A]=A)})),C((function(g){tv(e,(function(A){delete g.containedEdges[A],tv(g.edges,(function(C,I){C.id!==A?C.clusteringEdgeReplacingIds=t._filter(C.clusteringEdgeReplacingIds,(function(g){return!e[g]})):g.edges[I]=null})),g.edges=t._filter(g.edges,(function(g){return null!==g}))}))})),tv(e,(function(g){delete t.clusteredEdges[g]})),tv(e,(function(g){delete t.body.edges[g]})),tv(Lh(this.body.edges),(function(g){var A=t.body.edges[g],e=t._isClusteredNode(A.fromId)||t._isClusteredNode(A.toId);if(e!==t._isClusteredEdge(A.id))if(e){var C=t._getClusterNodeForNode(A.fromId);void 0!==C&&t._clusterEdges(t.body.nodes[A.fromId],A,C);var I=t._getClusterNodeForNode(A.toId);void 0!==I&&t._clusterEdges(t.body.nodes[A.toId],A,I)}else delete t._clusterEdges[g],t._restoreEdge(A)}));for(var i=!1,o=!0,n=function(){var g=[];C((function(t){var A=Lh(t.containedNodes).length,e=!0===t.options.allowSingleNodeCluster;(e&&A<1||!e&&A<2)&&g.push(t.id)}));for(var A=0;A0,i=i||o};o;)n();i&&this._updateState()}},{key:"_isClusteredNode",value:function(g){return void 0!==this.clusteredNodes[g]}},{key:"_isClusteredEdge",value:function(g){return void 0!==this.clusteredEdges[g]}}]),g}();var gP=function(){function g(t,A){var e;cn(this,g),void 0!==window&&(e=window.requestAnimationFrame||window.mozRequestAnimationFrame||window.webkitRequestAnimationFrame||window.msRequestAnimationFrame),window.requestAnimationFrame=void 0===e?function(g){g()}:e,this.body=t,this.canvas=A,this.redrawRequested=!1,this.renderTimer=void 0,this.requiresTimeout=!0,this.renderingActive=!1,this.renderRequests=0,this.allowRedraw=!0,this.dragging=!1,this.zooming=!1,this.options={},this.defaultOptions={hideEdgesOnDrag:!1,hideEdgesOnZoom:!1,hideNodesOnDrag:!1},fe(this.options,this.defaultOptions),this._determineBrowserMethod(),this.bindEventListeners()}return kd(g,[{key:"bindEventListeners",value:function(){var g,t=this;this.body.emitter.on("dragStart",(function(){t.dragging=!0})),this.body.emitter.on("dragEnd",(function(){t.dragging=!1})),this.body.emitter.on("zoom",(function(){t.zooming=!0,window.clearTimeout(t.zoomTimeoutId),t.zoomTimeoutId=wu((function(){var g;t.zooming=!1,je(g=t._requestRedraw).call(g,t)()}),250)})),this.body.emitter.on("_resizeNodes",(function(){t._resizeNodes()})),this.body.emitter.on("_redraw",(function(){!1===t.renderingActive&&t._redraw()})),this.body.emitter.on("_blockRedraw",(function(){t.allowRedraw=!1})),this.body.emitter.on("_allowRedraw",(function(){t.allowRedraw=!0,t.redrawRequested=!1})),this.body.emitter.on("_requestRedraw",je(g=this._requestRedraw).call(g,this)),this.body.emitter.on("_startRendering",(function(){t.renderRequests+=1,t.renderingActive=!0,t._startRendering()})),this.body.emitter.on("_stopRendering",(function(){t.renderRequests-=1,t.renderingActive=t.renderRequests>0,t.renderTimer=void 0})),this.body.emitter.on("destroy",(function(){t.renderRequests=0,t.allowRedraw=!1,t.renderingActive=!1,!0===t.requiresTimeout?clearTimeout(t.renderTimer):window.cancelAnimationFrame(t.renderTimer),t.body.emitter.off()}))}},{key:"setOptions",value:function(g){if(void 0!==g){Xf(["hideEdgesOnDrag","hideEdgesOnZoom","hideNodesOnDrag"],this.options,g)}}},{key:"_requestNextFrame",value:function(g,t){if("undefined"!=typeof window){var A,e=window;return!0===this.requiresTimeout?A=wu(g,t):e.requestAnimationFrame&&(A=e.requestAnimationFrame(g)),A}}},{key:"_startRendering",value:function(){var g;!0===this.renderingActive&&(void 0===this.renderTimer&&(this.renderTimer=this._requestNextFrame(je(g=this._renderStep).call(g,this),this.simulationInterval)))}},{key:"_renderStep",value:function(){!0===this.renderingActive&&(this.renderTimer=void 0,!0===this.requiresTimeout&&this._startRendering(),this._redraw(),!1===this.requiresTimeout&&this._startRendering())}},{key:"redraw",value:function(){this.body.emitter.emit("setSize"),this._redraw()}},{key:"_requestRedraw",value:function(){var g=this;!0!==this.redrawRequested&&!1===this.renderingActive&&!0===this.allowRedraw&&(this.redrawRequested=!0,this._requestNextFrame((function(){g._redraw(!1)}),0))}},{key:"_redraw",value:function(){var g=arguments.length>0&&void 0!==arguments[0]&&arguments[0];if(!0===this.allowRedraw){this.body.emitter.emit("initRedraw"),this.redrawRequested=!1;var t={drawExternalLabels:null};0!==this.canvas.frame.canvas.width&&0!==this.canvas.frame.canvas.height||this.canvas.setSize(),this.canvas.setTransform();var A=this.canvas.getContext(),e=this.canvas.frame.canvas.clientWidth,C=this.canvas.frame.canvas.clientHeight;if(A.clearRect(0,0,e,C),0===this.canvas.frame.clientWidth)return;if(A.save(),A.translate(this.body.view.translation.x,this.body.view.translation.y),A.scale(this.body.view.scale,this.body.view.scale),A.beginPath(),this.body.emitter.emit("beforeDrawing",A),A.closePath(),!1===g&&(!1===this.dragging||!0===this.dragging&&!1===this.options.hideEdgesOnDrag)&&(!1===this.zooming||!0===this.zooming&&!1===this.options.hideEdgesOnZoom)&&this._drawEdges(A),!1===this.dragging||!0===this.dragging&&!1===this.options.hideNodesOnDrag){var I=this._drawNodes(A,g).drawExternalLabels;t.drawExternalLabels=I}!1===g&&(!1===this.dragging||!0===this.dragging&&!1===this.options.hideEdgesOnDrag)&&(!1===this.zooming||!0===this.zooming&&!1===this.options.hideEdgesOnZoom)&&this._drawArrows(A),null!=t.drawExternalLabels&&t.drawExternalLabels(),!1===g&&this._drawSelectionBox(A),A.beginPath(),this.body.emitter.emit("afterDrawing",A),A.closePath(),A.restore(),!0===g&&A.clearRect(0,0,e,C)}}},{key:"_resizeNodes",value:function(){this.canvas.setTransform();var g=this.canvas.getContext();g.save(),g.translate(this.body.view.translation.x,this.body.view.translation.y),g.scale(this.body.view.scale,this.body.view.scale);var t,A=this.body.nodes;for(var e in A)Object.prototype.hasOwnProperty.call(A,e)&&((t=A[e]).resize(g),t.updateBoundingBox(g,t.selected));g.restore()}},{key:"_drawNodes",value:function(g){for(var t,A,e=arguments.length>1&&void 0!==arguments[1]&&arguments[1],C=this.body.nodes,I=this.body.nodeIndices,i=[],o=[],n=this.canvas.DOMtoCanvas({x:-20,y:-20}),r=this.canvas.DOMtoCanvas({x:this.canvas.frame.canvas.clientWidth+20,y:this.canvas.frame.canvas.clientHeight+20}),s={top:n.y,left:n.x,bottom:r.y,right:r.x},a=[],d=0;d0&&void 0!==arguments[0]?arguments[0]:this.pixelRatio;!0===this.initialized&&(this.cameraState.previousWidth=this.frame.canvas.width/g,this.cameraState.previousHeight=this.frame.canvas.height/g,this.cameraState.scale=this.body.view.scale,this.cameraState.position=this.DOMtoCanvas({x:.5*this.frame.canvas.width/g,y:.5*this.frame.canvas.height/g}))}},{key:"_setCameraState",value:function(){if(void 0!==this.cameraState.scale&&0!==this.frame.canvas.clientWidth&&0!==this.frame.canvas.clientHeight&&0!==this.pixelRatio&&this.cameraState.previousWidth>0&&this.cameraState.previousHeight>0){var g=this.frame.canvas.width/this.pixelRatio/this.cameraState.previousWidth,t=this.frame.canvas.height/this.pixelRatio/this.cameraState.previousHeight,A=this.cameraState.scale;1!=g&&1!=t?A=.5*this.cameraState.scale*(g+t):1!=g?A=this.cameraState.scale*g:1!=t&&(A=this.cameraState.scale*t),this.body.view.scale=A;var e=this.DOMtoCanvas({x:.5*this.frame.canvas.clientWidth,y:.5*this.frame.canvas.clientHeight}),C={x:e.x-this.cameraState.position.x,y:e.y-this.cameraState.position.y};this.body.view.translation.x+=C.x*this.body.view.scale,this.body.view.translation.y+=C.y*this.body.view.scale}}},{key:"_prepareValue",value:function(g){if("number"==typeof g)return g+"px";if("string"==typeof g){if(-1!==Xc(g).call(g,"%")||-1!==Xc(g).call(g,"px"))return g;if(-1===Xc(g).call(g,"%"))return g+"px"}throw new Error("Could not use the value supplied for width or height:"+g)}},{key:"_create",value:function(){for(;this.body.container.hasChildNodes();)this.body.container.removeChild(this.body.container.firstChild);if(this.frame=document.createElement("div"),this.frame.className="vis-network",this.frame.style.position="relative",this.frame.style.overflow="hidden",this.frame.tabIndex=0,this.frame.canvas=document.createElement("canvas"),this.frame.canvas.style.position="relative",this.frame.appendChild(this.frame.canvas),this.frame.canvas.getContext)this._setPixelRatio(),this.setTransform();else{var g=document.createElement("DIV");g.style.color="red",g.style.fontWeight="bold",g.style.padding="10px",g.innerText="Error: your browser does not support HTML canvas",this.frame.canvas.appendChild(g)}this.body.container.appendChild(this.frame),this.body.view.scale=1,this.body.view.translation={x:.5*this.frame.canvas.clientWidth,y:.5*this.frame.canvas.clientHeight},this._bindHammer()}},{key:"_bindHammer",value:function(){var g=this;void 0!==this.hammer&&this.hammer.destroy(),this.drag={},this.pinch={},this.hammer=new Ev(this.frame.canvas),this.hammer.get("pinch").set({enable:!0}),this.hammer.get("pan").set({threshold:5,direction:Ev.DIRECTION_ALL}),AP(this.hammer,(function(t){g.body.eventListeners.onTouch(t)})),this.hammer.on("tap",(function(t){g.body.eventListeners.onTap(t)})),this.hammer.on("doubletap",(function(t){g.body.eventListeners.onDoubleTap(t)})),this.hammer.on("press",(function(t){g.body.eventListeners.onHold(t)})),this.hammer.on("panstart",(function(t){g.body.eventListeners.onDragStart(t)})),this.hammer.on("panmove",(function(t){g.body.eventListeners.onDrag(t)})),this.hammer.on("panend",(function(t){g.body.eventListeners.onDragEnd(t)})),this.hammer.on("pinch",(function(t){g.body.eventListeners.onPinch(t)})),this.frame.canvas.addEventListener("wheel",(function(t){g.body.eventListeners.onMouseWheel(t)})),this.frame.canvas.addEventListener("mousemove",(function(t){g.body.eventListeners.onMouseMove(t)})),this.frame.canvas.addEventListener("contextmenu",(function(t){g.body.eventListeners.onContext(t)})),this.hammerFrame=new Ev(this.frame),eP(this.hammerFrame,(function(t){g.body.eventListeners.onRelease(t)}))}},{key:"setSize",value:function(){var g=arguments.length>0&&void 0!==arguments[0]?arguments[0]:this.options.width,t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:this.options.height;g=this._prepareValue(g),t=this._prepareValue(t);var A=!1,e=this.frame.canvas.width,C=this.frame.canvas.height,I=this.pixelRatio;if(this._setPixelRatio(),g!=this.options.width||t!=this.options.height||this.frame.style.width!=g||this.frame.style.height!=t)this._getCameraState(I),this.frame.style.width=g,this.frame.style.height=t,this.frame.canvas.style.width="100%",this.frame.canvas.style.height="100%",this.frame.canvas.width=Math.round(this.frame.canvas.clientWidth*this.pixelRatio),this.frame.canvas.height=Math.round(this.frame.canvas.clientHeight*this.pixelRatio),this.options.width=g,this.options.height=t,this.canvasViewCenter={x:.5*this.frame.clientWidth,y:.5*this.frame.clientHeight},A=!0;else{var i=Math.round(this.frame.canvas.clientWidth*this.pixelRatio),o=Math.round(this.frame.canvas.clientHeight*this.pixelRatio);this.frame.canvas.width===i&&this.frame.canvas.height===o||this._getCameraState(I),this.frame.canvas.width!==i&&(this.frame.canvas.width=i,A=!0),this.frame.canvas.height!==o&&(this.frame.canvas.height=o,A=!0)}return!0===A&&(this.body.emitter.emit("resize",{width:Math.round(this.frame.canvas.width/this.pixelRatio),height:Math.round(this.frame.canvas.height/this.pixelRatio),oldWidth:Math.round(e/this.pixelRatio),oldHeight:Math.round(C/this.pixelRatio)}),this._setCameraState()),this.initialized=!0,A}},{key:"getContext",value:function(){return this.frame.canvas.getContext("2d")}},{key:"_determinePixelRatio",value:function(){var g=this.getContext();if(void 0===g)throw new Error("Could not get canvax context");var t=1;return"undefined"!=typeof window&&(t=window.devicePixelRatio||1),t/(g.webkitBackingStorePixelRatio||g.mozBackingStorePixelRatio||g.msBackingStorePixelRatio||g.oBackingStorePixelRatio||g.backingStorePixelRatio||1)}},{key:"_setPixelRatio",value:function(){this.pixelRatio=this._determinePixelRatio()}},{key:"setTransform",value:function(){var g=this.getContext();if(void 0===g)throw new Error("Could not get canvax context");g.setTransform(this.pixelRatio,0,0,this.pixelRatio,0,0)}},{key:"_XconvertDOMtoCanvas",value:function(g){return(g-this.body.view.translation.x)/this.body.view.scale}},{key:"_XconvertCanvasToDOM",value:function(g){return g*this.body.view.scale+this.body.view.translation.x}},{key:"_YconvertDOMtoCanvas",value:function(g){return(g-this.body.view.translation.y)/this.body.view.scale}},{key:"_YconvertCanvasToDOM",value:function(g){return g*this.body.view.scale+this.body.view.translation.y}},{key:"canvasToDOM",value:function(g){return{x:this._XconvertCanvasToDOM(g.x),y:this._YconvertCanvasToDOM(g.y)}}},{key:"DOMtoCanvas",value:function(g){return{x:this._XconvertDOMtoCanvas(g.x),y:this._YconvertDOMtoCanvas(g.y)}}}]),g}();var IP=function(){function g(t,A){var e,C,I=this;cn(this,g),this.body=t,this.canvas=A,this.animationSpeed=1/this.renderRefreshRate,this.animationEasingFunction="easeInOutQuint",this.easingTime=0,this.sourceScale=0,this.targetScale=0,this.sourceTranslation=0,this.targetTranslation=0,this.lockedOnNodeId=void 0,this.lockedOnNodeOffset=void 0,this.touchTime=0,this.viewFunction=void 0,this.body.emitter.on("fit",je(e=this.fit).call(e,this)),this.body.emitter.on("animationFinished",(function(){I.body.emitter.emit("_stopRendering")})),this.body.emitter.on("unlockNode",je(C=this.releaseNode).call(C,this))}return kd(g,[{key:"setOptions",value:function(){var g=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};this.options=g}},{key:"fit",value:function(g){var t=arguments.length>1&&void 0!==arguments[1]&&arguments[1];g=function(g,t){var A=fe({nodes:t,minZoomLevel:Number.MIN_VALUE,maxZoomLevel:1},null!=g?g:{});if(!Rh(A.nodes))throw new TypeError("Nodes has to be an array of ids.");if(0===A.nodes.length&&(A.nodes=t),!("number"==typeof A.minZoomLevel&&A.minZoomLevel>0))throw new TypeError("Min zoom level has to be a number higher than zero.");if(!("number"==typeof A.maxZoomLevel&&A.minZoomLevel<=A.maxZoomLevel))throw new TypeError("Max zoom level has to be a number higher than min zoom level.");return A}(g,this.body.nodeIndices);var A,e,C=this.canvas.frame.canvas.clientWidth,I=this.canvas.frame.canvas.clientHeight;if(0===C||0===I)e=1,A=XR.getRange(this.body.nodes,g.nodes);else if(!0===t){var i=0;for(var o in this.body.nodes){if(Object.prototype.hasOwnProperty.call(this.body.nodes,o))!0===this.body.nodes[o].predefinedPosition&&(i+=1)}if(i>.5*this.body.nodeIndices.length)return void this.fit(g,!1);A=XR.getRange(this.body.nodes,g.nodes),e=12.662/(this.body.nodeIndices.length+7.4147)+.0964822,e*=Math.min(C/600,I/600)}else{this.body.emitter.emit("_resizeNodes"),A=XR.getRange(this.body.nodes,g.nodes);var n=C/(1.1*Math.abs(A.maxX-A.minX)),r=I/(1.1*Math.abs(A.maxY-A.minY));e=n<=r?n:r}e>g.maxZoomLevel?e=g.maxZoomLevel:e1&&void 0!==arguments[1]?arguments[1]:{};if(void 0!==this.body.nodes[g]){var A={x:this.body.nodes[g].x,y:this.body.nodes[g].y};t.position=A,t.lockedOnNode=g,this.moveTo(t)}else console.error("Node: "+g+" cannot be found.")}},{key:"moveTo",value:function(g){if(void 0!==g){if(null!=g.offset){if(null!=g.offset.x){if(g.offset.x=+g.offset.x,!Ym(g.offset.x))throw new TypeError('The option "offset.x" has to be a finite number.')}else g.offset.x=0;if(null!=g.offset.y){if(g.offset.y=+g.offset.y,!Ym(g.offset.y))throw new TypeError('The option "offset.y" has to be a finite number.')}else g.offset.x=0}else g.offset={x:0,y:0};if(null!=g.position){if(null!=g.position.x){if(g.position.x=+g.position.x,!Ym(g.position.x))throw new TypeError('The option "position.x" has to be a finite number.')}else g.position.x=0;if(null!=g.position.y){if(g.position.y=+g.position.y,!Ym(g.position.y))throw new TypeError('The option "position.y" has to be a finite number.')}else g.position.x=0}else g.position=this.getViewPosition();if(null!=g.scale){if(g.scale=+g.scale,!(g.scale>0))throw new TypeError('The option "scale" has to be a number greater than zero.')}else g.scale=this.body.view.scale;void 0===g.animation&&(g.animation={duration:0}),!1===g.animation&&(g.animation={duration:0}),!0===g.animation&&(g.animation={}),void 0===g.animation.duration&&(g.animation.duration=1e3),void 0===g.animation.easingFunction&&(g.animation.easingFunction="easeInOutQuad"),this.animateView(g)}else g={}}},{key:"animateView",value:function(g){if(void 0!==g){this.animationEasingFunction=g.animation.easingFunction,this.releaseNode(),!0===g.locked&&(this.lockedOnNodeId=g.lockedOnNode,this.lockedOnNodeOffset=g.offset),0!=this.easingTime&&this._transitionRedraw(!0),this.sourceScale=this.body.view.scale,this.sourceTranslation=this.body.view.translation,this.targetScale=g.scale,this.body.view.scale=this.targetScale;var t,A,e=this.canvas.DOMtoCanvas({x:.5*this.canvas.frame.canvas.clientWidth,y:.5*this.canvas.frame.canvas.clientHeight}),C=e.x-g.position.x,I=e.y-g.position.y;if(this.targetTranslation={x:this.sourceTranslation.x+C*this.targetScale+g.offset.x,y:this.sourceTranslation.y+I*this.targetScale+g.offset.y},0===g.animation.duration)if(null!=this.lockedOnNodeId)this.viewFunction=je(t=this._lockedRedraw).call(t,this),this.body.emitter.on("initRedraw",this.viewFunction);else this.body.view.scale=this.targetScale,this.body.view.translation=this.targetTranslation,this.body.emitter.emit("_requestRedraw");else this.animationSpeed=1/(60*g.animation.duration*.001)||1/60,this.animationEasingFunction=g.animation.easingFunction,this.viewFunction=je(A=this._transitionRedraw).call(A,this),this.body.emitter.on("initRedraw",this.viewFunction),this.body.emitter.emit("_startRendering")}}},{key:"_lockedRedraw",value:function(){var g=this.body.nodes[this.lockedOnNodeId].x,t=this.body.nodes[this.lockedOnNodeId].y,A=this.canvas.DOMtoCanvas({x:.5*this.canvas.frame.canvas.clientWidth,y:.5*this.canvas.frame.canvas.clientHeight}),e=A.x-g,C=A.y-t,I=this.body.view.translation,i={x:I.x+e*this.body.view.scale+this.lockedOnNodeOffset.x,y:I.y+C*this.body.view.scale+this.lockedOnNodeOffset.y};this.body.view.translation=i}},{key:"releaseNode",value:function(){void 0!==this.lockedOnNodeId&&void 0!==this.viewFunction&&(this.body.emitter.off("initRedraw",this.viewFunction),this.lockedOnNodeId=void 0,this.lockedOnNodeOffset=void 0)}},{key:"_transitionRedraw",value:function(){var g=arguments.length>0&&void 0!==arguments[0]&&arguments[0];this.easingTime+=this.animationSpeed,this.easingTime=!0===g?1:this.easingTime;var t=hv[this.animationEasingFunction](this.easingTime);if(this.body.view.scale=this.sourceScale+(this.targetScale-this.sourceScale)*t,this.body.view.translation={x:this.sourceTranslation.x+(this.targetTranslation.x-this.sourceTranslation.x)*t,y:this.sourceTranslation.y+(this.targetTranslation.y-this.sourceTranslation.y)*t},this.easingTime>=1){var A;if(this.body.emitter.off("initRedraw",this.viewFunction),this.easingTime=0,null!=this.lockedOnNodeId)this.viewFunction=je(A=this._lockedRedraw).call(A,this),this.body.emitter.on("initRedraw",this.viewFunction);this.body.emitter.emit("animationFinished")}}},{key:"getScale",value:function(){return this.body.view.scale}},{key:"getViewPosition",value:function(){return this.canvas.DOMtoCanvas({x:.5*this.canvas.frame.canvas.clientWidth,y:.5*this.canvas.frame.canvas.clientHeight})}}]),g}();function iP(g){var t,A=g&&g.preventDefault||!1,e=g&&g.container||window,C={},I={keydown:{},keyup:{}},i={};for(t=97;t<=122;t++)i[String.fromCharCode(t)]={code:t-97+65,shift:!1};for(t=65;t<=90;t++)i[String.fromCharCode(t)]={code:t,shift:!0};for(t=0;t<=9;t++)i[""+t]={code:48+t,shift:!1};for(t=1;t<=12;t++)i["F"+t]={code:111+t,shift:!1};for(t=0;t<=9;t++)i["num"+t]={code:96+t,shift:!1};i["num*"]={code:106,shift:!1},i["num+"]={code:107,shift:!1},i["num-"]={code:109,shift:!1},i["num/"]={code:111,shift:!1},i["num."]={code:110,shift:!1},i.left={code:37,shift:!1},i.up={code:38,shift:!1},i.right={code:39,shift:!1},i.down={code:40,shift:!1},i.space={code:32,shift:!1},i.enter={code:13,shift:!1},i.shift={code:16,shift:void 0},i.esc={code:27,shift:!1},i.backspace={code:8,shift:!1},i.tab={code:9,shift:!1},i.ctrl={code:17,shift:!1},i.alt={code:18,shift:!1},i.delete={code:46,shift:!1},i.pageup={code:33,shift:!1},i.pagedown={code:34,shift:!1},i["="]={code:187,shift:!1},i["-"]={code:189,shift:!1},i["]"]={code:221,shift:!1},i["["]={code:219,shift:!1};var o=function(g){r(g,"keydown")},n=function(g){r(g,"keyup")},r=function(g,t){if(void 0!==I[t][g.keyCode]){for(var e=I[t][g.keyCode],C=0;C700&&(this.body.emitter.emit("fit",{duration:700}),this.touchTime=(new Date).valueOf())}},{key:"_stopMovement",value:function(){for(var g in this.boundFunctions)Object.prototype.hasOwnProperty.call(this.boundFunctions,g)&&(this.body.emitter.off("initRedraw",this.boundFunctions[g]),this.body.emitter.emit("_stopRendering"));this.boundFunctions={}}},{key:"_moveUp",value:function(){this.body.view.translation.y+=this.options.keyboard.speed.y}},{key:"_moveDown",value:function(){this.body.view.translation.y-=this.options.keyboard.speed.y}},{key:"_moveLeft",value:function(){this.body.view.translation.x+=this.options.keyboard.speed.x}},{key:"_moveRight",value:function(){this.body.view.translation.x-=this.options.keyboard.speed.x}},{key:"_zoomIn",value:function(){var g=this.body.view.scale,t=this.body.view.scale*(1+this.options.keyboard.speed.zoom),A=this.body.view.translation,e=t/g,C=(1-e)*this.canvas.canvasViewCenter.x+A.x*e,I=(1-e)*this.canvas.canvasViewCenter.y+A.y*e;this.body.view.scale=t,this.body.view.translation={x:C,y:I},this.body.emitter.emit("zoom",{direction:"+",scale:this.body.view.scale,pointer:null})}},{key:"_zoomOut",value:function(){var g=this.body.view.scale,t=this.body.view.scale/(1+this.options.keyboard.speed.zoom),A=this.body.view.translation,e=t/g,C=(1-e)*this.canvas.canvasViewCenter.x+A.x*e,I=(1-e)*this.canvas.canvasViewCenter.y+A.y*e;this.body.view.scale=t,this.body.view.translation={x:C,y:I},this.body.emitter.emit("zoom",{direction:"-",scale:this.body.view.scale,pointer:null})}},{key:"configureKeyboardBindings",value:function(){var g,t,A,e,C,I,i,o,n,r,s,a,d,h,l,c,u,p,f,v,y,m,b,w,k=this;(void 0!==this.keycharm&&this.keycharm.destroy(),!0===this.options.keyboard.enabled)&&(!0===this.options.keyboard.bindToWindow?this.keycharm=iP({container:window,preventDefault:!0}):this.keycharm=iP({container:this.canvas.frame,preventDefault:!0}),this.keycharm.reset(),!0===this.activated&&(je(g=this.keycharm).call(g,"up",(function(){k.bindToRedraw("_moveUp")}),"keydown"),je(t=this.keycharm).call(t,"down",(function(){k.bindToRedraw("_moveDown")}),"keydown"),je(A=this.keycharm).call(A,"left",(function(){k.bindToRedraw("_moveLeft")}),"keydown"),je(e=this.keycharm).call(e,"right",(function(){k.bindToRedraw("_moveRight")}),"keydown"),je(C=this.keycharm).call(C,"=",(function(){k.bindToRedraw("_zoomIn")}),"keydown"),je(I=this.keycharm).call(I,"num+",(function(){k.bindToRedraw("_zoomIn")}),"keydown"),je(i=this.keycharm).call(i,"num-",(function(){k.bindToRedraw("_zoomOut")}),"keydown"),je(o=this.keycharm).call(o,"-",(function(){k.bindToRedraw("_zoomOut")}),"keydown"),je(n=this.keycharm).call(n,"[",(function(){k.bindToRedraw("_zoomOut")}),"keydown"),je(r=this.keycharm).call(r,"]",(function(){k.bindToRedraw("_zoomIn")}),"keydown"),je(s=this.keycharm).call(s,"pageup",(function(){k.bindToRedraw("_zoomIn")}),"keydown"),je(a=this.keycharm).call(a,"pagedown",(function(){k.bindToRedraw("_zoomOut")}),"keydown"),je(d=this.keycharm).call(d,"up",(function(){k.unbindFromRedraw("_moveUp")}),"keyup"),je(h=this.keycharm).call(h,"down",(function(){k.unbindFromRedraw("_moveDown")}),"keyup"),je(l=this.keycharm).call(l,"left",(function(){k.unbindFromRedraw("_moveLeft")}),"keyup"),je(c=this.keycharm).call(c,"right",(function(){k.unbindFromRedraw("_moveRight")}),"keyup"),je(u=this.keycharm).call(u,"=",(function(){k.unbindFromRedraw("_zoomIn")}),"keyup"),je(p=this.keycharm).call(p,"num+",(function(){k.unbindFromRedraw("_zoomIn")}),"keyup"),je(f=this.keycharm).call(f,"num-",(function(){k.unbindFromRedraw("_zoomOut")}),"keyup"),je(v=this.keycharm).call(v,"-",(function(){k.unbindFromRedraw("_zoomOut")}),"keyup"),je(y=this.keycharm).call(y,"[",(function(){k.unbindFromRedraw("_zoomOut")}),"keyup"),je(m=this.keycharm).call(m,"]",(function(){k.unbindFromRedraw("_zoomIn")}),"keyup"),je(b=this.keycharm).call(b,"pageup",(function(){k.unbindFromRedraw("_zoomIn")}),"keyup"),je(w=this.keycharm).call(w,"pagedown",(function(){k.unbindFromRedraw("_zoomOut")}),"keyup")))}}]),g}();function nP(g,t){var A=void 0!==uh&&ln(g)||g["@@iterator"];if(!A){if(Rh(g)||(A=function(g,t){var A;if(!g)return;if("string"==typeof g)return rP(g,t);var e=wh(A=Object.prototype.toString.call(g)).call(A,8,-1);"Object"===e&&g.constructor&&(e=g.constructor.name);if("Map"===e||"Set"===e)return Uo(g);if("Arguments"===e||/^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(e))return rP(g,t)}(g))||t&&g&&"number"==typeof g.length){A&&(g=A);var e=0,C=function(){};return{s:C,n:function(){return e>=g.length?{done:!0}:{done:!1,value:g[e++]}},e:function(g){throw g},f:C}}throw new TypeError("Invalid attempt to iterate non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.")}var I,i=!0,o=!1;return{s:function(){A=A.call(g)},n:function(){var g=A.next();return i=g.done,g},e:function(g){o=!0,I=g},f:function(){try{i||null==A.return||A.return()}finally{if(o)throw I}}}}function rP(g,t){(null==t||t>g.length)&&(t=g.length);for(var A=0,e=new Array(t);A50&&(this.drag.pointer=this.getPointer(g.center),this.drag.pinched=!1,this.pinch.scale=this.body.view.scale,this.touchTime=(new Date).valueOf())}},{key:"onTap",value:function(g){var t=this.getPointer(g.center),A=this.selectionHandler.options.multiselect&&(g.changedPointers[0].ctrlKey||g.changedPointers[0].metaKey);this.checkSelectionChanges(t,A),this.selectionHandler.commitAndEmit(t,g),this.selectionHandler.generateClickEvent("click",g,t)}},{key:"onDoubleTap",value:function(g){var t=this.getPointer(g.center);this.selectionHandler.generateClickEvent("doubleClick",g,t)}},{key:"onHold",value:function(g){var t=this.getPointer(g.center),A=this.selectionHandler.options.multiselect;this.checkSelectionChanges(t,A),this.selectionHandler.commitAndEmit(t,g),this.selectionHandler.generateClickEvent("click",g,t),this.selectionHandler.generateClickEvent("hold",g,t)}},{key:"onRelease",value:function(g){if((new Date).valueOf()-this.touchTime>10){var t=this.getPointer(g.center);this.selectionHandler.generateClickEvent("release",g,t),this.touchTime=(new Date).valueOf()}}},{key:"onContext",value:function(g){var t=this.getPointer({x:g.clientX,y:g.clientY});this.selectionHandler.generateClickEvent("oncontext",g,t)}},{key:"checkSelectionChanges",value:function(g){!0===(arguments.length>1&&void 0!==arguments[1]&&arguments[1])?this.selectionHandler.selectAdditionalOnPoint(g):this.selectionHandler.selectOnPoint(g)}},{key:"_determineDifference",value:function(g,t){var A=function(g,t){for(var A=[],e=0;e=C.minX&&A.x<=C.maxX&&A.y>=C.minY&&A.y<=C.maxY}));Cl(I).call(I,(function(g){return t.selectionHandler.selectObject(t.body.nodes[g])}));var i=this.getPointer(g.center);this.selectionHandler.commitAndEmit(i,g),this.selectionHandler.generateClickEvent("dragEnd",g,this.getPointer(g.center),void 0,!0),this.body.emitter.emit("_requestRedraw")}else{var o=this.drag.selection;o&&o.length?(Cl(o).call(o,(function(g){g.node.options.fixed.x=g.xFixed,g.node.options.fixed.y=g.yFixed})),this.selectionHandler.generateClickEvent("dragEnd",g,this.getPointer(g.center)),this.body.emitter.emit("startSimulation")):(this.selectionHandler.generateClickEvent("dragEnd",g,this.getPointer(g.center),void 0,!0),this.body.emitter.emit("_requestRedraw"))}}},{key:"onPinch",value:function(g){var t=this.getPointer(g.center);this.drag.pinched=!0,void 0===this.pinch.scale&&(this.pinch.scale=1);var A=this.pinch.scale*g.scale;this.zoom(A,t)}},{key:"zoom",value:function(g,t){if(!0===this.options.zoomView){var A=this.body.view.scale;g<1e-5&&(g=1e-5),g>10&&(g=10);var e=void 0;void 0!==this.drag&&!0===this.drag.dragging&&(e=this.canvas.DOMtoCanvas(this.drag.pointer));var C=this.body.view.translation,I=g/A,i=(1-I)*t.x+C.x*I,o=(1-I)*t.y+C.y*I;if(this.body.view.scale=g,this.body.view.translation={x:i,y:o},null!=e){var n=this.canvas.canvasToDOM(e);this.drag.pointer.x=n.x,this.drag.pointer.y=n.y}this.body.emitter.emit("_requestRedraw"),A0&&(this.popupObj=r[s[s.length-1]],I=!0)}if(void 0===this.popupObj&&!1===I){for(var d,h=this.body.edgeIndices,l=this.body.edges,c=[],u=0;u0&&(this.popupObj=l[c[c.length-1]],i="edge")}void 0!==this.popupObj?this.popupObj.id!==C&&(void 0===this.popup&&(this.popup=new Ov(this.canvas.frame)),this.popup.popupTargetType=i,this.popup.popupTargetId=this.popupObj.id,this.popup.setPosition(g.x+3,g.y-5),this.popup.setText(this.popupObj.getTitle()),this.popup.show(),this.body.emitter.emit("showPopup",this.popupObj.id)):void 0!==this.popup&&(this.popup.hide(),this.body.emitter.emit("hidePopup"))}},{key:"_checkHidePopup",value:function(g){var t=this.selectionHandler._pointerToPositionObject(g),A=!1;if("node"===this.popup.popupTargetType){if(void 0!==this.body.nodes[this.popup.popupTargetId]&&!0===(A=this.body.nodes[this.popup.popupTargetId].isOverlappingWith(t))){var e=this.selectionHandler.getNodeAt(g);A=void 0!==e&&e.id===this.popup.popupTargetId}}else void 0===this.selectionHandler.getNodeAt(g)&&void 0!==this.body.edges[this.popup.popupTargetId]&&(A=this.body.edges[this.popup.popupTargetId].isOverlappingWith(t));!1===A&&(this.popupObj=void 0,this.popup.hide(),this.body.emitter.emit("hidePopup"))}}]),g}(),aP=u,dP=pm,hP=zy.getWeakData,lP=qy,cP=AA,uP=Q,pP=gg,fP=Hy,vP=qg,yP=GC.set,mP=GC.getterFor,bP=Sr.find,wP=Sr.findIndex,kP=aP([].splice),xP=0,EP=function(g){return g.frozen||(g.frozen=new OP)},OP=function(){this.entries=[]},TP=function(g,t){return bP(g.entries,(function(g){return g[0]===t}))};OP.prototype={get:function(g){var t=TP(this,g);if(t)return t[1]},has:function(g){return!!TP(this,g)},set:function(g,t){var A=TP(this,g);A?A[1]=t:this.entries.push([g,t])},delete:function(g){var t=wP(this.entries,(function(t){return t[0]===g}));return~t&&kP(this.entries,t,1),!!~t}};var DP,NP={getConstructor:function(g,t,A,e){var C=g((function(g,C){lP(g,I),yP(g,{type:t,id:xP++,frozen:void 0}),uP(C)||fP(C,g[e],{that:g,AS_ENTRIES:A})})),I=C.prototype,i=mP(t),o=function(g,t,A){var e=i(g),C=hP(cP(t),!0);return!0===C?EP(e).set(t,A):C[e.id]=A,g};return dP(I,{delete:function(g){var t=i(this);if(!pP(g))return!1;var A=hP(g);return!0===A?EP(t).delete(g):A&&vP(A,t.id)&&delete A[t.id]},has:function(g){var t=i(this);if(!pP(g))return!1;var A=hP(g);return!0===A?EP(t).has(g):A&&vP(A,t.id)}}),dP(I,A?{get:function(g){var t=i(this);if(pP(g)){var A=hP(g);return!0===A?EP(t).get(g):A?A[t.id]:void 0}},set:function(g,t){return o(this,g,t)}}:{add:function(g){return o(this,g,!0)}}),C}},RP=vy,PP=C,MP=u,BP=pm,zP=zy,SP=cm,ZP=NP,FP=gg,GP=GC.enforce,jP=I,LP=bC,VP=Object,YP=Array.isArray,WP=VP.isExtensible,QP=VP.isFrozen,UP=VP.isSealed,_P=VP.freeze,KP=VP.seal,HP={},XP={},JP=!PP.ActiveXObject&&"ActiveXObject"in PP,qP=function(g){return function(){return g(this,arguments.length?arguments[0]:void 0)}},$P=SP("WeakMap",qP,ZP),gM=$P.prototype,tM=MP(gM.set);if(LP)if(JP){DP=ZP.getConstructor(qP,"WeakMap",!0),zP.enable();var AM=MP(gM.delete),eM=MP(gM.has),CM=MP(gM.get);BP(gM,{delete:function(g){if(FP(g)&&!WP(g)){var t=GP(this);return t.frozen||(t.frozen=new DP),AM(this,g)||t.frozen.delete(g)}return AM(this,g)},has:function(g){if(FP(g)&&!WP(g)){var t=GP(this);return t.frozen||(t.frozen=new DP),eM(this,g)||t.frozen.has(g)}return eM(this,g)},get:function(g){if(FP(g)&&!WP(g)){var t=GP(this);return t.frozen||(t.frozen=new DP),eM(this,g)?CM(this,g):t.frozen.get(g)}return CM(this,g)},set:function(g,t){if(FP(g)&&!WP(g)){var A=GP(this);A.frozen||(A.frozen=new DP),eM(this,g)?tM(this,g,t):A.frozen.set(g,t)}else tM(this,g,t);return this}})}else RP&&jP((function(){var g=_P([]);return tM(new $P,g,1),!QP(g)}))&&BP(gM,{set:function(g,t){var A;return YP(g)&&(QP(g)?A=HP:UP(g)&&(A=XP)),tM(this,g,t),A===HP&&_P(g),A===XP&&KP(g),this}});var IM,iM,oM,nM,rM,sM=A(tg.WeakMap);function aM(g,t,A,e){if("a"===A&&!e)throw new TypeError("Private accessor was defined without a getter");if("function"==typeof t?g!==t||!e:!t.has(g))throw new TypeError("Cannot read private member from an object whose class did not declare it");return"m"===A?e:"a"===A?e.call(g):e?e.value:t.get(g)}function dM(g,t,A,e,C){if("m"===e)throw new TypeError("Private method is not writable");if("a"===e&&!C)throw new TypeError("Private accessor was defined without a setter");if("function"==typeof t?g!==t||!C:!t.has(g))throw new TypeError("Cannot write private member to an object whose class did not declare it");return"a"===e?C.call(g,A):C?C.value=A:t.set(g,A),A}function hM(g,t){var A=void 0!==uh&&ln(g)||g["@@iterator"];if(!A){if(Rh(g)||(A=function(g,t){var A;if(!g)return;if("string"==typeof g)return lM(g,t);var e=wh(A=Object.prototype.toString.call(g)).call(A,8,-1);"Object"===e&&g.constructor&&(e=g.constructor.name);if("Map"===e||"Set"===e)return Uo(g);if("Arguments"===e||/^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(e))return lM(g,t)}(g))||t&&g&&"number"==typeof g.length){A&&(g=A);var e=0,C=function(){};return{s:C,n:function(){return e>=g.length?{done:!0}:{done:!1,value:g[e++]}},e:function(g){throw g},f:C}}throw new TypeError("Invalid attempt to iterate non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.")}var I,i=!0,o=!1;return{s:function(){A=A.call(g)},n:function(){var g=A.next();return i=g.done,g},e:function(g){o=!0,I=g},f:function(){try{i||null==A.return||A.return()}finally{if(o)throw I}}}}function lM(g,t){(null==t||t>g.length)&&(t=g.length);for(var A=0,e=new Array(t);A0&&void 0!==arguments[0]?arguments[0]:function(){};cn(this,g),oM.set(this,new uM),nM.set(this,new uM),rM.set(this,void 0),dM(this,rM,t,"f")}return kd(g,[{key:"sizeNodes",get:function(){return aM(this,oM,"f").size}},{key:"sizeEdges",get:function(){return aM(this,nM,"f").size}},{key:"getNodes",value:function(){return aM(this,oM,"f").getSelection()}},{key:"getEdges",value:function(){return aM(this,nM,"f").getSelection()}},{key:"addNodes",value:function(){var g;(g=aM(this,oM,"f")).add.apply(g,arguments)}},{key:"addEdges",value:function(){var g;(g=aM(this,nM,"f")).add.apply(g,arguments)}},{key:"deleteNodes",value:function(g){aM(this,oM,"f").delete(g)}},{key:"deleteEdges",value:function(g){aM(this,nM,"f").delete(g)}},{key:"clear",value:function(){aM(this,oM,"f").clear(),aM(this,nM,"f").clear()}},{key:"commit",value:function(){for(var g,t,A={nodes:aM(this,oM,"f").commit(),edges:aM(this,nM,"f").commit()},e=arguments.length,C=new Array(e),I=0;I=g.length?{done:!0}:{done:!1,value:g[e++]}},e:function(g){throw g},f:C}}throw new TypeError("Invalid attempt to iterate non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.")}var I,i=!0,o=!1;return{s:function(){A=A.call(g)},n:function(){var g=A.next();return i=g.done,g},e:function(g){o=!0,I=g},f:function(){try{i||null==A.return||A.return()}finally{if(o)throw I}}}}function vM(g,t){(null==t||t>g.length)&&(t=g.length);for(var A=0,e=new Array(t);A4&&void 0!==arguments[4]&&arguments[4],I=this._initBaseEvent(t,A);if(!0===C)I.nodes=[],I.edges=[];else{var i=this.getSelection();I.nodes=i.nodes,I.edges=i.edges}void 0!==e&&(I.previousSelection=e),"click"==g&&(I.items=this.getClickedItems(A)),void 0!==t.controlEdge&&(I.controlEdge=t.controlEdge),this.body.emitter.emit(g,I)}},{key:"selectObject",value:function(g){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:this.options.selectConnectedEdges;if(void 0!==g){if(g instanceof jN){var A;if(!0===t)(A=this._selectionAccumulator).addEdges.apply(A,ch(g.edges));this._selectionAccumulator.addNodes(g)}else this._selectionAccumulator.addEdges(g);return!0}return!1}},{key:"deselectObject",value:function(g){!0===g.isSelected()&&(g.selected=!1,this._removeFromSelection(g))}},{key:"_getAllNodesOverlappingWith",value:function(g){for(var t=[],A=this.body.nodes,e=0;e1&&void 0!==arguments[1])||arguments[1],A=this._pointerToPositionObject(g),e=this._getAllNodesOverlappingWith(A);return e.length>0?!0===t?this.body.nodes[e[e.length-1]]:e[e.length-1]:void 0}},{key:"_getEdgesOverlappingWith",value:function(g,t){for(var A=this.body.edges,e=0;e1&&void 0!==arguments[1])||arguments[1],A=this.canvas.DOMtoCanvas(g),e=10,C=null,I=this.body.edges,i=0;i0&&(this.generateClickEvent("deselectEdge",t,g,C),A=!0),e.nodes.deleted.length>0&&(this.generateClickEvent("deselectNode",t,g,C),A=!0),e.nodes.added.length>0&&(this.generateClickEvent("selectNode",t,g),A=!0),e.edges.added.length>0&&(this.generateClickEvent("selectEdge",t,g),A=!0),!0===A&&this.generateClickEvent("select",t,g)}},{key:"getSelection",value:function(){return{nodes:this.getSelectedNodeIds(),edges:this.getSelectedEdgeIds()}}},{key:"getSelectedNodes",value:function(){return this._selectionAccumulator.getNodes()}},{key:"getSelectedEdges",value:function(){return this._selectionAccumulator.getEdges()}},{key:"getSelectedNodeIds",value:function(){var g;return Fh(g=this._selectionAccumulator.getNodes()).call(g,(function(g){return g.id}))}},{key:"getSelectedEdgeIds",value:function(){var g;return Fh(g=this._selectionAccumulator.getEdges()).call(g,(function(g){return g.id}))}},{key:"setSelection",value:function(g){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};if(!g||!g.nodes&&!g.edges)throw new TypeError("Selection must be an object with nodes and/or edges properties");if((t.unselectAll||void 0===t.unselectAll)&&this.unselectAll(),g.nodes){var A,e=fM(g.nodes);try{for(e.s();!(A=e.n()).done;){var C=A.value,I=this.body.nodes[C];if(!I)throw new RangeError('Node with id "'+C+'" not found');this.selectObject(I,t.highlightEdges)}}catch(g){e.e(g)}finally{e.f()}}if(g.edges){var i,o=fM(g.edges);try{for(o.s();!(i=o.n()).done;){var n=i.value,r=this.body.edges[n];if(!r)throw new RangeError('Edge with id "'+n+'" not found');this.selectObject(r)}}catch(g){o.e(g)}finally{o.f()}}this.body.emitter.emit("_requestRedraw"),this._selectionAccumulator.commit()}},{key:"selectNodes",value:function(g){var t=!(arguments.length>1&&void 0!==arguments[1])||arguments[1];if(!g||void 0===g.length)throw"Selection must be an array with ids";this.setSelection({nodes:g},{highlightEdges:t})}},{key:"selectEdges",value:function(g){if(!g||void 0===g.length)throw"Selection must be an array with ids";this.setSelection({edges:g})}},{key:"updateSelection",value:function(){for(var g in this._selectionAccumulator.getNodes())Object.prototype.hasOwnProperty.call(this.body.nodes,g.id)||this._selectionAccumulator.deleteNodes(g);for(var t in this._selectionAccumulator.getEdges())Object.prototype.hasOwnProperty.call(this.body.edges,t.id)||this._selectionAccumulator.deleteEdges(t)}},{key:"getClickedItems",value:function(g){for(var t=this.canvas.DOMtoCanvas(g),A=[],e=this.body.nodeIndices,C=this.body.nodes,I=e.length-1;I>=0;I--){var i=C[e[I]].getItemsOnPoint(t);A.push.apply(A,i)}for(var o=this.body.edgeIndices,n=this.body.edges,r=o.length-1;r>=0;r--){var s=n[o[r]].getItemsOnPoint(t);A.push.apply(A,s)}return A}}]),g}();function mM(g){var t=function(){if("undefined"==typeof Reflect||!MT)return!1;if(MT.sham)return!1;if("function"==typeof Proxy)return!0;try{return Boolean.prototype.valueOf.call(MT(Boolean,[],(function(){}))),!0}catch(g){return!1}}();return function(){var A,e=nb(g);if(t){var C=nb(this).constructor;A=MT(e,arguments,C)}else A=e.apply(this,arguments);return Ib(this,A)}}var bM=function(){function g(){cn(this,g)}return kd(g,[{key:"abstract",value:function(){throw new Error("Can't instantiate abstract class!")}},{key:"fake_use",value:function(){}},{key:"curveType",value:function(){return this.abstract()}},{key:"getPosition",value:function(g){return this.fake_use(g),this.abstract()}},{key:"setPosition",value:function(g,t){var A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:void 0;this.fake_use(g,t,A),this.abstract()}},{key:"getTreeSize",value:function(g){return this.fake_use(g),this.abstract()}},{key:"sort",value:function(g){this.fake_use(g),this.abstract()}},{key:"fix",value:function(g,t){this.fake_use(g,t),this.abstract()}},{key:"shift",value:function(g,t){this.fake_use(g,t),this.abstract()}}]),g}(),wM=function(g){Cb(A,g);var t=mM(A);function A(g){var e;return cn(this,A),(e=t.call(this)).layout=g,e}return kd(A,[{key:"curveType",value:function(){return"horizontal"}},{key:"getPosition",value:function(g){return g.x}},{key:"setPosition",value:function(g,t){var A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:void 0;void 0!==A&&this.layout.hierarchical.addToOrdering(g,A),g.x=t}},{key:"getTreeSize",value:function(g){var t=this.layout.hierarchical.getTreeSize(this.layout.body.nodes,g);return{min:t.min_x,max:t.max_x}}},{key:"sort",value:function(g){WO(g).call(g,(function(g,t){return g.x-t.x}))}},{key:"fix",value:function(g,t){g.y=this.layout.options.hierarchical.levelSeparation*t,g.options.fixed.y=!0}},{key:"shift",value:function(g,t){this.layout.body.nodes[g].x+=t}}]),A}(bM),kM=function(g){Cb(A,g);var t=mM(A);function A(g){var e;return cn(this,A),(e=t.call(this)).layout=g,e}return kd(A,[{key:"curveType",value:function(){return"vertical"}},{key:"getPosition",value:function(g){return g.y}},{key:"setPosition",value:function(g,t){var A=arguments.length>2&&void 0!==arguments[2]?arguments[2]:void 0;void 0!==A&&this.layout.hierarchical.addToOrdering(g,A),g.y=t}},{key:"getTreeSize",value:function(g){var t=this.layout.hierarchical.getTreeSize(this.layout.body.nodes,g);return{min:t.min_y,max:t.max_y}}},{key:"sort",value:function(g){WO(g).call(g,(function(g,t){return g.y-t.y}))}},{key:"fix",value:function(g,t){g.x=this.layout.options.hierarchical.levelSeparation*t,g.options.fixed.x=!0}},{key:"shift",value:function(g,t){this.layout.body.nodes[g].y+=t}}]),A}(bM),xM=Sr.every;TA({target:"Array",proto:!0,forced:!_h("every")},{every:function(g){return xM(this,g,arguments.length>1?arguments[1]:void 0)}});var EM=Me("Array").every,OM=og,TM=EM,DM=Array.prototype,NM=function(g){var t=g.every;return g===DM||OM(DM,g)&&t===DM.every?TM:t},RM=A(NM);function PM(g,t){var A=void 0!==uh&&ln(g)||g["@@iterator"];if(!A){if(Rh(g)||(A=function(g,t){var A;if(!g)return;if("string"==typeof g)return MM(g,t);var e=wh(A=Object.prototype.toString.call(g)).call(A,8,-1);"Object"===e&&g.constructor&&(e=g.constructor.name);if("Map"===e||"Set"===e)return Uo(g);if("Arguments"===e||/^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(e))return MM(g,t)}(g))||t&&g&&"number"==typeof g.length){A&&(g=A);var e=0,C=function(){};return{s:C,n:function(){return e>=g.length?{done:!0}:{done:!1,value:g[e++]}},e:function(g){throw g},f:C}}throw new TypeError("Invalid attempt to iterate non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.")}var I,i=!0,o=!1;return{s:function(){A=A.call(g)},n:function(){var g=A.next();return i=g.done,g},e:function(g){o=!0,I=g},f:function(){try{i||null==A.return||A.return()}finally{if(o)throw I}}}}function MM(g,t){(null==t||t>g.length)&&(t=g.length);for(var A=0,e=new Array(t);A=t[e])&&(t[e]=t[A]+1)})),t}function zM(g,t,A,e){var C,I,i=$c(null),o=LE(C=ch(nT(e).call(e))).call(C,(function(g,t){return g+1+t.edges.length}),0),n=A+"Id",r="to"===A?1:-1,s=PM(e);try{var a,d=function(){var C=lh(I.value,2),s=C[0],a=C[1];if(!e.has(s)||!g(a))return 0;i[s]=0;for(var d,h,l=[a],c=0,u=function(){var g,C;if(!e.has(s))return 0;var I=i[d.id]+r;if(Cl(g=pc(C=d.edges).call(C,(function(g){return g.connected&&g.to!==g.from&&g[A]!==d&&e.has(g.toId)&&e.has(g.fromId)}))).call(g,(function(g){var e=g[n],C=i[e];(null==C||t(I,C))&&(i[e]=I,l.push(g[A]))})),c>o)return{v:{v:BM(e,i)}};++c};d=l.pop();)if(0!==(h=u())&&h)return h.v};for(s.s();!(I=s.n()).done;)if(0!==(a=d())&&a)return a.v}catch(g){s.e(g)}finally{s.f()}return i}var SM=function(){function g(){cn(this,g),this.childrenReference={},this.parentReference={},this.trees={},this.distributionOrdering={},this.levels={},this.distributionIndex={},this.isTree=!1,this.treeIndex=-1}return kd(g,[{key:"addRelation",value:function(g,t){void 0===this.childrenReference[g]&&(this.childrenReference[g]=[]),this.childrenReference[g].push(t),void 0===this.parentReference[t]&&(this.parentReference[t]=[]),this.parentReference[t].push(g)}},{key:"checkIfTree",value:function(){for(var g in this.parentReference)if(this.parentReference[g].length>1)return void(this.isTree=!1);this.isTree=!0}},{key:"numTrees",value:function(){return this.treeIndex+1}},{key:"setTreeIndex",value:function(g,t){void 0!==t&&void 0===this.trees[g.id]&&(this.trees[g.id]=t,this.treeIndex=Math.max(t,this.treeIndex))}},{key:"ensureLevel",value:function(g){void 0===this.levels[g]&&(this.levels[g]=0)}},{key:"getMaxLevel",value:function(g){var t=this,A={};return function g(e){if(void 0!==A[e])return A[e];var C=t.levels[e];if(t.childrenReference[e]){var I=t.childrenReference[e];if(I.length>0)for(var i=0;i0&&(A.levelSeparation*=-1):A.levelSeparation<0&&(A.levelSeparation*=-1),this.setDirectionStrategy(),this.body.emitter.emit("_resetHierarchicalLayout"),this.adaptAllOptionsForHierarchicalLayout(t);if(!0===e)return this.body.emitter.emit("refresh"),qf(t,this.optionsBackup)}return t}},{key:"_resetRNG",value:function(g){this.initialRandomSeed=g,this._rng=Ff(this.initialRandomSeed)}},{key:"adaptAllOptionsForHierarchicalLayout",value:function(g){if(!0===this.options.hierarchical.enabled){var t=this.optionsBackup.physics;void 0===g.physics||!0===g.physics?(g.physics={enabled:void 0===t.enabled||t.enabled,solver:"hierarchicalRepulsion"},t.enabled=void 0===t.enabled||t.enabled,t.solver=t.solver||"barnesHut"):"object"===yd(g.physics)?(t.enabled=void 0===g.physics.enabled||g.physics.enabled,t.solver=g.physics.solver||"barnesHut",g.physics.solver="hierarchicalRepulsion"):!1!==g.physics&&(t.solver="barnesHut",g.physics={solver:"hierarchicalRepulsion"});var A=this.direction.curveType();if(void 0===g.edges)this.optionsBackup.edges={smooth:{enabled:!0,type:"dynamic"}},g.edges={smooth:!1};else if(void 0===g.edges.smooth)this.optionsBackup.edges={smooth:{enabled:!0,type:"dynamic"}},g.edges.smooth=!1;else if("boolean"==typeof g.edges.smooth)this.optionsBackup.edges={smooth:g.edges.smooth},g.edges.smooth={enabled:g.edges.smooth,type:A};else{var e=g.edges.smooth;void 0!==e.type&&"dynamic"!==e.type&&(A=e.type),this.optionsBackup.edges={smooth:{enabled:void 0===e.enabled||e.enabled,type:void 0===e.type?"dynamic":e.type,roundness:void 0===e.roundness?.5:e.roundness,forceDirection:void 0!==e.forceDirection&&e.forceDirection}},g.edges.smooth={enabled:void 0===e.enabled||e.enabled,type:A,roundness:void 0===e.roundness?.5:e.roundness,forceDirection:void 0!==e.forceDirection&&e.forceDirection}}this.body.emitter.emit("_forceDisableDynamicCurves",A)}return g}},{key:"positionInitially",value:function(g){if(!0!==this.options.hierarchical.enabled){this._resetRNG(this.initialRandomSeed);for(var t=g.length+50,A=0;AC){for(var i=g.length;g.length>C&&e<=10;){e+=1;var o=g.length;if(e%3==0?this.body.modules.clustering.clusterBridges(I):this.body.modules.clustering.clusterOutliers(I),o==g.length&&e%3!=0)return this._declusterAll(),this.body.emitter.emit("_layoutFailed"),void console.info("This network could not be positioned by this version of the improved layout algorithm. Please disable improvedLayout for better performance.")}this.body.modules.kamadaKawai.setOptions({springLength:Math.max(150,2*i)})}e>10&&console.info("The clustering didn't succeed within the amount of interations allowed, progressing with partial result."),this.body.modules.kamadaKawai.solve(g,this.body.edgeIndices,!0),this._shiftToCenter();for(var n=0;n0){var g,t,A=!1,e=!1;for(t in this.lastNodeOnLevel={},this.hierarchical=new SM,this.body.nodes)Object.prototype.hasOwnProperty.call(this.body.nodes,t)&&(void 0!==(g=this.body.nodes[t]).options.level?(A=!0,this.hierarchical.levels[t]=g.options.level):e=!0);if(!0===e&&!0===A)throw new Error("To use the hierarchical layout, nodes require either no predefined levels or levels have to be defined for all nodes.");if(!0===e){var C=this.options.hierarchical.sortMethod;"hubsize"===C?this._determineLevelsByHubsize():"directed"===C?this._determineLevelsDirected():"custom"===C&&this._determineLevelsCustomCallback()}for(var I in this.body.nodes)Object.prototype.hasOwnProperty.call(this.body.nodes,I)&&this.hierarchical.ensureLevel(I);var i=this._getDistribution();this._generateMap(),this._placeNodesByHierarchy(i),this._condenseHierarchy(),this._shiftToCenter()}}},{key:"_condenseHierarchy",value:function(){var g=this,t=!1,A={},e=function(t,A){var e=g.hierarchical.trees;for(var C in e)Object.prototype.hasOwnProperty.call(e,C)&&e[C]===t&&g.direction.shift(C,A)},C=function(){for(var t=[],A=0;A0)for(var I=0;I1&&void 0!==arguments[1]?arguments[1]:1e9,e=1e9,C=1e9,I=1e9,i=-1e9;for(var o in t)if(Object.prototype.hasOwnProperty.call(t,o)){var n=g.body.nodes[o],r=g.hierarchical.levels[n.id],s=g.direction.getPosition(n),a=lh(g._getSpaceAroundNode(n,t),2),d=a[0],h=a[1];e=Math.min(d,e),C=Math.min(h,C),r<=A&&(I=Math.min(s,I),i=Math.max(s,i))}return[I,i,e,C]},o=function(t,A,e){for(var C=g.hierarchical,I=0;I1)for(var n=0;n2&&void 0!==arguments[2]&&arguments[2],o=g.direction.getPosition(A),n=g.direction.getPosition(e),r=Math.abs(n-o),s=g.options.hierarchical.nodeSpacing;if(r>s){var a={},d={};I(A,a),I(e,d);var h=function(t,A){var e=g.hierarchical.getMaxLevel(t.id),C=g.hierarchical.getMaxLevel(A.id);return Math.min(e,C)}(A,e),l=i(a,h),c=i(d,h),u=l[1],p=c[0],f=c[2];if(Math.abs(u-p)>s){var v=u-p+s;v<-f+s&&(v=-f+s),v<0&&(g._shiftBlock(e.id,v),t=!0,!0===C&&g._centerParent(e))}}},r=function(e,C){for(var o=C.id,n=C.edges,r=g.hierarchical.levels[C.id],s=g.options.hierarchical.levelSeparation*g.options.hierarchical.levelSeparation,a={},d=[],h=0;h0?h=Math.min(d,a-g.options.hierarchical.nodeSpacing):d<0&&(h=-Math.min(-d,s-g.options.hierarchical.nodeSpacing)),0!=h&&(g._shiftBlock(C.id,h),t=!0)}(v),function(A){var e=g.direction.getPosition(C),I=lh(g._getSpaceAroundNode(C),2),i=I[0],o=I[1],n=A-e,r=e;n>0?r=Math.min(e+(o-g.options.hierarchical.nodeSpacing),A):n<0&&(r=Math.max(e-(i-g.options.hierarchical.nodeSpacing),A)),r!==e&&(g.direction.setPosition(C,r),t=!0)}(v=f(e,n))};!0===this.options.hierarchical.blockShifting&&(function(A){var e=g.hierarchical.getLevels();e=cl(e).call(e);for(var C=0;C0&&Math.abs(a)0&&(n=this.direction.getPosition(e[I-1])+o),this.direction.setPosition(i,n,t),this._validatePositionAndContinue(i,t,n),C++}}}}},{key:"_placeBranchNodes",value:function(g,t){var A,e=this.hierarchical.childrenReference[g];if(void 0!==e){for(var C=[],I=0;It&&void 0===this.positionedNodes[o.id]))return;var r=this.options.hierarchical.nodeSpacing,s=void 0;s=0===i?this.direction.getPosition(this.body.nodes[g]):this.direction.getPosition(C[i-1])+r,this.direction.setPosition(o,s,n),this._validatePositionAndContinue(o,n,s)}var a=this._getCenterPosition(C);this.direction.setPosition(this.body.nodes[g],a,t)}}},{key:"_validatePositionAndContinue",value:function(g,t,A){if(this.hierarchical.isTree){if(void 0!==this.lastNodeOnLevel[t]){var e=this.direction.getPosition(this.body.nodes[this.lastNodeOnLevel[t]]);if(A-eg}),"from",g)}(A),this.hierarchical.setMinLevelToZero(this.body.nodes)}},{key:"_generateMap",value:function(){var g=this;this._crawlNetwork((function(t,A){g.hierarchical.levels[A.id]>g.hierarchical.levels[t.id]&&g.hierarchical.addRelation(t.id,A.id)})),this.hierarchical.checkIfTree()}},{key:"_crawlNetwork",value:function(){var g=this,t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:function(){},A=arguments.length>1?arguments[1]:void 0,e={},C=function A(C,I){if(void 0===e[C.id]){var i;g.hierarchical.setTreeIndex(C,I),e[C.id]=!0;for(var o=g._getActiveEdges(C),n=0;n=g.length?{done:!0}:{done:!1,value:g[e++]}},e:function(g){throw g},f:C}}throw new TypeError("Invalid attempt to iterate non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.")}var I,i=!0,o=!1;return{s:function(){A=A.call(g)},n:function(){var g=A.next();return i=g.done,g},e:function(g){o=!0,I=g},f:function(){try{i||null==A.return||A.return()}finally{if(o)throw I}}}}function GM(g,t){(null==t||t>g.length)&&(t=g.length);for(var A=0,e=new Array(t);A0&&!1!==this.options.deleteNode||0===A&&!1!==this.options.deleteEdge)&&(!0===i&&this._createSeperator(4),this._createDeleteButton(I)),this._bindElementEvents(this.closeDiv,je(g=this.toggleEditMode).call(g,this)),this._temporaryBindEvent("select",je(t=this.showManipulatorToolbar).call(t,this))}this.body.emitter.emit("_redraw")}},{key:"addNodeMode",value:function(){var g;if(!0!==this.editMode&&this.enableEditMode(),this._clean(),this.inMode="addNode",!0===this.guiEnabled){var t,A=this.options.locales[this.options.locale];this.manipulationDOM={},this._createBackButton(A),this._createSeperator(),this._createDescription(A.addDescription||this.options.locales.en.addDescription),this._bindElementEvents(this.closeDiv,je(t=this.toggleEditMode).call(t,this))}this._temporaryBindEvent("click",je(g=this._performAddNode).call(g,this))}},{key:"editNode",value:function(){var g=this;!0!==this.editMode&&this.enableEditMode(),this._clean();var t=this.selectionHandler.getSelectedNodes()[0];if(void 0!==t){if(this.inMode="editNode","function"!=typeof this.options.editNode)throw new Error("No function has been configured to handle the editing of nodes.");if(!0!==t.isCluster){var A=qf({},t.options,!1);if(A.x=t.x,A.y=t.y,2!==this.options.editNode.length)throw new Error("The function for edit does not support two arguments (data, callback)");this.options.editNode(A,(function(t){null!=t&&"editNode"===g.inMode&&g.body.data.nodes.getDataSet().update(t),g.showManipulatorToolbar()}))}else alert(this.options.locales[this.options.locale].editClusterError||this.options.locales.en.editClusterError)}else this.showManipulatorToolbar()}},{key:"addEdgeMode",value:function(){var g,t,A,e,C;if(!0!==this.editMode&&this.enableEditMode(),this._clean(),this.inMode="addEdge",!0===this.guiEnabled){var I,i=this.options.locales[this.options.locale];this.manipulationDOM={},this._createBackButton(i),this._createSeperator(),this._createDescription(i.edgeDescription||this.options.locales.en.edgeDescription),this._bindElementEvents(this.closeDiv,je(I=this.toggleEditMode).call(I,this))}this._temporaryBindUI("onTouch",je(g=this._handleConnect).call(g,this)),this._temporaryBindUI("onDragEnd",je(t=this._finishConnect).call(t,this)),this._temporaryBindUI("onDrag",je(A=this._dragControlNode).call(A,this)),this._temporaryBindUI("onRelease",je(e=this._finishConnect).call(e,this)),this._temporaryBindUI("onDragStart",je(C=this._dragStartEdge).call(C,this)),this._temporaryBindUI("onHold",(function(){}))}},{key:"editEdgeMode",value:function(){if(!0!==this.editMode&&this.enableEditMode(),this._clean(),this.inMode="editEdge","object"!==yd(this.options.editEdge)||"function"!=typeof this.options.editEdge.editWithoutDrag||(this.edgeBeingEditedId=this.selectionHandler.getSelectedEdgeIds()[0],void 0===this.edgeBeingEditedId)){if(!0===this.guiEnabled){var g,t=this.options.locales[this.options.locale];this.manipulationDOM={},this._createBackButton(t),this._createSeperator(),this._createDescription(t.editEdgeDescription||this.options.locales.en.editEdgeDescription),this._bindElementEvents(this.closeDiv,je(g=this.toggleEditMode).call(g,this))}if(this.edgeBeingEditedId=this.selectionHandler.getSelectedEdgeIds()[0],void 0!==this.edgeBeingEditedId){var A,e,C,I,i=this.body.edges[this.edgeBeingEditedId],o=this._getNewTargetNode(i.from.x,i.from.y),n=this._getNewTargetNode(i.to.x,i.to.y);this.temporaryIds.nodes.push(o.id),this.temporaryIds.nodes.push(n.id),this.body.nodes[o.id]=o,this.body.nodeIndices.push(o.id),this.body.nodes[n.id]=n,this.body.nodeIndices.push(n.id),this._temporaryBindUI("onTouch",je(A=this._controlNodeTouch).call(A,this)),this._temporaryBindUI("onTap",(function(){})),this._temporaryBindUI("onHold",(function(){})),this._temporaryBindUI("onDragStart",je(e=this._controlNodeDragStart).call(e,this)),this._temporaryBindUI("onDrag",je(C=this._controlNodeDrag).call(C,this)),this._temporaryBindUI("onDragEnd",je(I=this._controlNodeDragEnd).call(I,this)),this._temporaryBindUI("onMouseMove",(function(){})),this._temporaryBindEvent("beforeDrawing",(function(g){var t=i.edgeType.findBorderPositions(g);!1===o.selected&&(o.x=t.from.x,o.y=t.from.y),!1===n.selected&&(n.x=t.to.x,n.y=t.to.y)})),this.body.emitter.emit("_redraw")}else this.showManipulatorToolbar()}else{var r=this.body.edges[this.edgeBeingEditedId];this._performEditEdge(r.from.id,r.to.id)}}},{key:"deleteSelected",value:function(){var g=this;!0!==this.editMode&&this.enableEditMode(),this._clean(),this.inMode="delete";var t=this.selectionHandler.getSelectedNodeIds(),A=this.selectionHandler.getSelectedEdgeIds(),e=void 0;if(t.length>0){for(var C=0;C0&&"function"==typeof this.options.deleteEdge&&(e=this.options.deleteEdge);if("function"==typeof e){var I={nodes:t,edges:A};if(2!==e.length)throw new Error("The function for delete does not support two arguments (data, callback)");e(I,(function(t){null!=t&&"delete"===g.inMode?(g.body.data.edges.getDataSet().remove(t.edges),g.body.data.nodes.getDataSet().remove(t.nodes),g.body.emitter.emit("startSimulation"),g.showManipulatorToolbar()):(g.body.emitter.emit("startSimulation"),g.showManipulatorToolbar())}))}else this.body.data.edges.getDataSet().remove(A),this.body.data.nodes.getDataSet().remove(t),this.body.emitter.emit("startSimulation"),this.showManipulatorToolbar()}},{key:"_setup",value:function(){!0===this.options.enabled?(this.guiEnabled=!0,this._createWrappers(),!1===this.editMode?this._createEditButton():this.showManipulatorToolbar()):(this._removeManipulationDOM(),this.guiEnabled=!1)}},{key:"_createWrappers",value:function(){var g,t;(void 0===this.manipulationDiv&&(this.manipulationDiv=document.createElement("div"),this.manipulationDiv.className="vis-manipulation",!0===this.editMode?this.manipulationDiv.style.display="block":this.manipulationDiv.style.display="none",this.canvas.frame.appendChild(this.manipulationDiv)),void 0===this.editModeDiv&&(this.editModeDiv=document.createElement("div"),this.editModeDiv.className="vis-edit-mode",!0===this.editMode?this.editModeDiv.style.display="none":this.editModeDiv.style.display="block",this.canvas.frame.appendChild(this.editModeDiv)),void 0===this.closeDiv)&&(this.closeDiv=document.createElement("button"),this.closeDiv.className="vis-close",this.closeDiv.setAttribute("aria-label",null!==(g=null===(t=this.options.locales[this.options.locale])||void 0===t?void 0:t.close)&&void 0!==g?g:this.options.locales.en.close),this.closeDiv.style.display=this.manipulationDiv.style.display,this.canvas.frame.appendChild(this.closeDiv))}},{key:"_getNewTargetNode",value:function(g,t){var A=qf({},this.options.controlNodeStyle);A.id="targetNode"+rD(),A.hidden=!1,A.physics=!1,A.x=g,A.y=t;var e=this.body.functions.createNode(A);return e.shape.boundingBox={left:g,right:g,top:t,bottom:t},e}},{key:"_createEditButton",value:function(){var g;this._clean(),this.manipulationDOM={},Qf(this.editModeDiv);var t=this.options.locales[this.options.locale],A=this._createButton("editMode","vis-edit vis-edit-mode",t.edit||this.options.locales.en.edit);this.editModeDiv.appendChild(A),this._bindElementEvents(A,je(g=this.toggleEditMode).call(g,this))}},{key:"_clean",value:function(){this.inMode=!1,!0===this.guiEnabled&&(Qf(this.editModeDiv),Qf(this.manipulationDiv),this._cleanupDOMEventListeners()),this._cleanupTemporaryNodesAndEdges(),this._unbindTemporaryUIs(),this._unbindTemporaryEvents(),this.body.emitter.emit("restorePhysics")}},{key:"_cleanupDOMEventListeners",value:function(){var g,t,A=FM(Zl(g=this._domEventListenerCleanupQueue).call(g,0));try{for(A.s();!(t=A.n()).done;){(0,t.value)()}}catch(g){A.e(g)}finally{A.f()}}},{key:"_removeManipulationDOM",value:function(){this._clean(),Qf(this.manipulationDiv),Qf(this.editModeDiv),Qf(this.closeDiv),this.manipulationDiv&&this.canvas.frame.removeChild(this.manipulationDiv),this.editModeDiv&&this.canvas.frame.removeChild(this.editModeDiv),this.closeDiv&&this.canvas.frame.removeChild(this.closeDiv),this.manipulationDiv=void 0,this.editModeDiv=void 0,this.closeDiv=void 0}},{key:"_createSeperator",value:function(){var g=arguments.length>0&&void 0!==arguments[0]?arguments[0]:1;this.manipulationDOM["seperatorLineDiv"+g]=document.createElement("div"),this.manipulationDOM["seperatorLineDiv"+g].className="vis-separator-line",this.manipulationDiv.appendChild(this.manipulationDOM["seperatorLineDiv"+g])}},{key:"_createAddNodeButton",value:function(g){var t,A=this._createButton("addNode","vis-add",g.addNode||this.options.locales.en.addNode);this.manipulationDiv.appendChild(A),this._bindElementEvents(A,je(t=this.addNodeMode).call(t,this))}},{key:"_createAddEdgeButton",value:function(g){var t,A=this._createButton("addEdge","vis-connect",g.addEdge||this.options.locales.en.addEdge);this.manipulationDiv.appendChild(A),this._bindElementEvents(A,je(t=this.addEdgeMode).call(t,this))}},{key:"_createEditNodeButton",value:function(g){var t,A=this._createButton("editNode","vis-edit",g.editNode||this.options.locales.en.editNode);this.manipulationDiv.appendChild(A),this._bindElementEvents(A,je(t=this.editNode).call(t,this))}},{key:"_createEditEdgeButton",value:function(g){var t,A=this._createButton("editEdge","vis-edit",g.editEdge||this.options.locales.en.editEdge);this.manipulationDiv.appendChild(A),this._bindElementEvents(A,je(t=this.editEdgeMode).call(t,this))}},{key:"_createDeleteButton",value:function(g){var t,A;A=this.options.rtl?"vis-delete-rtl":"vis-delete";var e=this._createButton("delete",A,g.del||this.options.locales.en.del);this.manipulationDiv.appendChild(e),this._bindElementEvents(e,je(t=this.deleteSelected).call(t,this))}},{key:"_createBackButton",value:function(g){var t,A=this._createButton("back","vis-back",g.back||this.options.locales.en.back);this.manipulationDiv.appendChild(A),this._bindElementEvents(A,je(t=this.showManipulatorToolbar).call(t,this))}},{key:"_createButton",value:function(g,t,A){var e=arguments.length>3&&void 0!==arguments[3]?arguments[3]:"vis-label";return this.manipulationDOM[g+"Div"]=document.createElement("button"),this.manipulationDOM[g+"Div"].className="vis-button "+t,this.manipulationDOM[g+"Label"]=document.createElement("div"),this.manipulationDOM[g+"Label"].className=e,this.manipulationDOM[g+"Label"].innerText=A,this.manipulationDOM[g+"Div"].appendChild(this.manipulationDOM[g+"Label"]),this.manipulationDOM[g+"Div"]}},{key:"_createDescription",value:function(g){this.manipulationDOM.descriptionLabel=document.createElement("div"),this.manipulationDOM.descriptionLabel.className="vis-none",this.manipulationDOM.descriptionLabel.innerText=g,this.manipulationDiv.appendChild(this.manipulationDOM.descriptionLabel)}},{key:"_temporaryBindEvent",value:function(g,t){this.temporaryEventFunctions.push({event:g,boundFunction:t}),this.body.emitter.on(g,t)}},{key:"_temporaryBindUI",value:function(g,t){if(void 0===this.body.eventListeners[g])throw new Error("This UI function does not exist. Typo? You tried: "+g+" possible are: "+eu(Lh(this.body.eventListeners)));this.temporaryUIFunctions[g]=this.body.eventListeners[g],this.body.eventListeners[g]=t}},{key:"_unbindTemporaryUIs",value:function(){for(var g in this.temporaryUIFunctions)Object.prototype.hasOwnProperty.call(this.temporaryUIFunctions,g)&&(this.body.eventListeners[g]=this.temporaryUIFunctions[g],delete this.temporaryUIFunctions[g]);this.temporaryUIFunctions={}}},{key:"_unbindTemporaryEvents",value:function(){for(var g=0;g=0;i--)if(C[i]!==this.selectedControlNode.id){I=this.body.nodes[C[i]];break}if(void 0!==I&&void 0!==this.selectedControlNode)if(!0===I.isCluster)alert(this.options.locales[this.options.locale].createEdgeError||this.options.locales.en.createEdgeError);else{var o=this.body.nodes[this.temporaryIds.nodes[0]];this.selectedControlNode.id===o.id?this._performEditEdge(I.id,e.to.id):this._performEditEdge(e.from.id,I.id)}else e.updateEdgeType(),this.body.emitter.emit("restorePhysics");this.body.emitter.emit("_redraw")}}},{key:"_handleConnect",value:function(g){if((new Date).valueOf()-this.touchTime>100){this.lastTouch=this.body.functions.getPointer(g.center),this.lastTouch.translation=fe({},this.body.view.translation),this.interactionHandler.drag.pointer=this.lastTouch,this.interactionHandler.drag.translation=this.lastTouch.translation;var t=this.lastTouch,A=this.selectionHandler.getNodeAt(t);if(void 0!==A)if(!0===A.isCluster)alert(this.options.locales[this.options.locale].createEdgeError||this.options.locales.en.createEdgeError);else{var e=this._getNewTargetNode(A.x,A.y);this.body.nodes[e.id]=e,this.body.nodeIndices.push(e.id);var C=this.body.functions.createEdge({id:"connectionEdge"+rD(),from:A.id,to:e.id,physics:!1,smooth:{enabled:!0,type:"continuous",roundness:.5}});this.body.edges[C.id]=C,this.body.edgeIndices.push(C.id),this.temporaryIds.nodes.push(e.id),this.temporaryIds.edges.push(C.id)}this.touchTime=(new Date).valueOf()}}},{key:"_dragControlNode",value:function(g){var t=this.body.functions.getPointer(g.center),A=this.selectionHandler._pointerToPositionObject(t),e=void 0;void 0!==this.temporaryIds.edges[0]&&(e=this.body.edges[this.temporaryIds.edges[0]].fromId);for(var C=this.selectionHandler._getAllNodesOverlappingWith(A),I=void 0,i=C.length-1;i>=0;i--){var o;if(-1===Xc(o=this.temporaryIds.nodes).call(o,C[i])){I=this.body.nodes[C[i]];break}}if(g.controlEdge={from:e,to:I?I.id:void 0},this.selectionHandler.generateClickEvent("controlNodeDragging",g,t),void 0!==this.temporaryIds.nodes[0]){var n=this.body.nodes[this.temporaryIds.nodes[0]];n.x=this.canvas._XconvertDOMtoCanvas(t.x),n.y=this.canvas._YconvertDOMtoCanvas(t.y),this.body.emitter.emit("_redraw")}else this.interactionHandler.onDrag(g)}},{key:"_finishConnect",value:function(g){var t=this.body.functions.getPointer(g.center),A=this.selectionHandler._pointerToPositionObject(t),e=void 0;void 0!==this.temporaryIds.edges[0]&&(e=this.body.edges[this.temporaryIds.edges[0]].fromId);for(var C=this.selectionHandler._getAllNodesOverlappingWith(A),I=void 0,i=C.length-1;i>=0;i--){var o;if(-1===Xc(o=this.temporaryIds.nodes).call(o,C[i])){I=this.body.nodes[C[i]];break}}this._cleanupTemporaryNodesAndEdges(),void 0!==I&&(!0===I.isCluster?alert(this.options.locales[this.options.locale].createEdgeError||this.options.locales.en.createEdgeError):void 0!==this.body.nodes[e]&&void 0!==this.body.nodes[I.id]&&this._performAddEdge(e,I.id)),g.controlEdge={from:e,to:I?I.id:void 0},this.selectionHandler.generateClickEvent("controlNodeDragEnd",g,t),this.body.emitter.emit("_redraw")}},{key:"_dragStartEdge",value:function(g){var t=this.lastTouch;this.selectionHandler.generateClickEvent("dragStart",g,t,void 0,!0)}},{key:"_performAddNode",value:function(g){var t=this,A={id:rD(),x:g.pointer.canvas.x,y:g.pointer.canvas.y,label:"new"};if("function"==typeof this.options.addNode){if(2!==this.options.addNode.length)throw this.showManipulatorToolbar(),new Error("The function for add does not support two arguments (data,callback)");this.options.addNode(A,(function(g){null!=g&&"addNode"===t.inMode&&t.body.data.nodes.getDataSet().add(g),t.showManipulatorToolbar()}))}else this.body.data.nodes.getDataSet().add(A),this.showManipulatorToolbar()}},{key:"_performAddEdge",value:function(g,t){var A=this,e={from:g,to:t};if("function"==typeof this.options.addEdge){if(2!==this.options.addEdge.length)throw new Error("The function for connect does not support two arguments (data,callback)");this.options.addEdge(e,(function(g){null!=g&&"addEdge"===A.inMode&&(A.body.data.edges.getDataSet().add(g),A.selectionHandler.unselectAll(),A.showManipulatorToolbar())}))}else this.body.data.edges.getDataSet().add(e),this.selectionHandler.unselectAll(),this.showManipulatorToolbar()}},{key:"_performEditEdge",value:function(g,t){var A=this,e={id:this.edgeBeingEditedId,from:g,to:t,label:this.body.data.edges.get(this.edgeBeingEditedId).label},C=this.options.editEdge;if("object"===yd(C)&&(C=C.editWithoutDrag),"function"==typeof C){if(2!==C.length)throw new Error("The function for edit does not support two arguments (data, callback)");C(e,(function(g){null==g||"editEdge"!==A.inMode?(A.body.edges[e.id].updateEdgeType(),A.body.emitter.emit("_redraw"),A.showManipulatorToolbar()):(A.body.data.edges.getDataSet().update(g),A.selectionHandler.unselectAll(),A.showManipulatorToolbar())}))}else this.body.data.edges.getDataSet().update(e),this.selectionHandler.unselectAll(),this.showManipulatorToolbar()}}]),g}(),LM="string",VM="boolean",YM="number",WM="array",QM="object",UM=["arrow","bar","box","circle","crow","curve","diamond","image","inv_curve","inv_triangle","triangle","vee"],_M={borderWidth:{number:YM},borderWidthSelected:{number:YM,undefined:"undefined"},brokenImage:{string:LM,undefined:"undefined"},chosen:{label:{boolean:VM,function:"function"},node:{boolean:VM,function:"function"},__type__:{object:QM,boolean:VM}},color:{border:{string:LM},background:{string:LM},highlight:{border:{string:LM},background:{string:LM},__type__:{object:QM,string:LM}},hover:{border:{string:LM},background:{string:LM},__type__:{object:QM,string:LM}},__type__:{object:QM,string:LM}},opacity:{number:YM,undefined:"undefined"},fixed:{x:{boolean:VM},y:{boolean:VM},__type__:{object:QM,boolean:VM}},font:{align:{string:LM},color:{string:LM},size:{number:YM},face:{string:LM},background:{string:LM},strokeWidth:{number:YM},strokeColor:{string:LM},vadjust:{number:YM},multi:{boolean:VM,string:LM},bold:{color:{string:LM},size:{number:YM},face:{string:LM},mod:{string:LM},vadjust:{number:YM},__type__:{object:QM,string:LM}},boldital:{color:{string:LM},size:{number:YM},face:{string:LM},mod:{string:LM},vadjust:{number:YM},__type__:{object:QM,string:LM}},ital:{color:{string:LM},size:{number:YM},face:{string:LM},mod:{string:LM},vadjust:{number:YM},__type__:{object:QM,string:LM}},mono:{color:{string:LM},size:{number:YM},face:{string:LM},mod:{string:LM},vadjust:{number:YM},__type__:{object:QM,string:LM}},__type__:{object:QM,string:LM}},group:{string:LM,number:YM,undefined:"undefined"},heightConstraint:{minimum:{number:YM},valign:{string:LM},__type__:{object:QM,boolean:VM,number:YM}},hidden:{boolean:VM},icon:{face:{string:LM},code:{string:LM},size:{number:YM},color:{string:LM},weight:{string:LM,number:YM},__type__:{object:QM}},id:{string:LM,number:YM},image:{selected:{string:LM,undefined:"undefined"},unselected:{string:LM,undefined:"undefined"},__type__:{object:QM,string:LM}},imagePadding:{top:{number:YM},right:{number:YM},bottom:{number:YM},left:{number:YM},__type__:{object:QM,number:YM}},label:{string:LM,undefined:"undefined"},labelHighlightBold:{boolean:VM},level:{number:YM,undefined:"undefined"},margin:{top:{number:YM},right:{number:YM},bottom:{number:YM},left:{number:YM},__type__:{object:QM,number:YM}},mass:{number:YM},physics:{boolean:VM},scaling:{min:{number:YM},max:{number:YM},label:{enabled:{boolean:VM},min:{number:YM},max:{number:YM},maxVisible:{number:YM},drawThreshold:{number:YM},__type__:{object:QM,boolean:VM}},customScalingFunction:{function:"function"},__type__:{object:QM}},shadow:{enabled:{boolean:VM},color:{string:LM},size:{number:YM},x:{number:YM},y:{number:YM},__type__:{object:QM,boolean:VM}},shape:{string:["custom","ellipse","circle","database","box","text","image","circularImage","diamond","dot","star","triangle","triangleDown","square","icon","hexagon"]},ctxRenderer:{function:"function"},shapeProperties:{borderDashes:{boolean:VM,array:WM},borderRadius:{number:YM},interpolation:{boolean:VM},useImageSize:{boolean:VM},useBorderWithImage:{boolean:VM},coordinateOrigin:{string:["center","top-left"]},__type__:{object:QM}},size:{number:YM},title:{string:LM,dom:"dom",undefined:"undefined"},value:{number:YM,undefined:"undefined"},widthConstraint:{minimum:{number:YM},maximum:{number:YM},__type__:{object:QM,boolean:VM,number:YM}},x:{number:YM},y:{number:YM},__type__:{object:QM}},KM={configure:{enabled:{boolean:VM},filter:{boolean:VM,string:LM,array:WM,function:"function"},container:{dom:"dom"},showButton:{boolean:VM},__type__:{object:QM,boolean:VM,string:LM,array:WM,function:"function"}},edges:{arrows:{to:{enabled:{boolean:VM},scaleFactor:{number:YM},type:{string:UM},imageHeight:{number:YM},imageWidth:{number:YM},src:{string:LM},__type__:{object:QM,boolean:VM}},middle:{enabled:{boolean:VM},scaleFactor:{number:YM},type:{string:UM},imageWidth:{number:YM},imageHeight:{number:YM},src:{string:LM},__type__:{object:QM,boolean:VM}},from:{enabled:{boolean:VM},scaleFactor:{number:YM},type:{string:UM},imageWidth:{number:YM},imageHeight:{number:YM},src:{string:LM},__type__:{object:QM,boolean:VM}},__type__:{string:["from","to","middle"],object:QM}},endPointOffset:{from:{number:YM},to:{number:YM},__type__:{object:QM,number:YM}},arrowStrikethrough:{boolean:VM},background:{enabled:{boolean:VM},color:{string:LM},size:{number:YM},dashes:{boolean:VM,array:WM},__type__:{object:QM,boolean:VM}},chosen:{label:{boolean:VM,function:"function"},edge:{boolean:VM,function:"function"},__type__:{object:QM,boolean:VM}},color:{color:{string:LM},highlight:{string:LM},hover:{string:LM},inherit:{string:["from","to","both"],boolean:VM},opacity:{number:YM},__type__:{object:QM,string:LM}},dashes:{boolean:VM,array:WM},font:{color:{string:LM},size:{number:YM},face:{string:LM},background:{string:LM},strokeWidth:{number:YM},strokeColor:{string:LM},align:{string:["horizontal","top","middle","bottom"]},vadjust:{number:YM},multi:{boolean:VM,string:LM},bold:{color:{string:LM},size:{number:YM},face:{string:LM},mod:{string:LM},vadjust:{number:YM},__type__:{object:QM,string:LM}},boldital:{color:{string:LM},size:{number:YM},face:{string:LM},mod:{string:LM},vadjust:{number:YM},__type__:{object:QM,string:LM}},ital:{color:{string:LM},size:{number:YM},face:{string:LM},mod:{string:LM},vadjust:{number:YM},__type__:{object:QM,string:LM}},mono:{color:{string:LM},size:{number:YM},face:{string:LM},mod:{string:LM},vadjust:{number:YM},__type__:{object:QM,string:LM}},__type__:{object:QM,string:LM}},hidden:{boolean:VM},hoverWidth:{function:"function",number:YM},label:{string:LM,undefined:"undefined"},labelHighlightBold:{boolean:VM},length:{number:YM,undefined:"undefined"},physics:{boolean:VM},scaling:{min:{number:YM},max:{number:YM},label:{enabled:{boolean:VM},min:{number:YM},max:{number:YM},maxVisible:{number:YM},drawThreshold:{number:YM},__type__:{object:QM,boolean:VM}},customScalingFunction:{function:"function"},__type__:{object:QM}},selectionWidth:{function:"function",number:YM},selfReferenceSize:{number:YM},selfReference:{size:{number:YM},angle:{number:YM},renderBehindTheNode:{boolean:VM},__type__:{object:QM}},shadow:{enabled:{boolean:VM},color:{string:LM},size:{number:YM},x:{number:YM},y:{number:YM},__type__:{object:QM,boolean:VM}},smooth:{enabled:{boolean:VM},type:{string:["dynamic","continuous","discrete","diagonalCross","straightCross","horizontal","vertical","curvedCW","curvedCCW","cubicBezier"]},roundness:{number:YM},forceDirection:{string:["horizontal","vertical","none"],boolean:VM},__type__:{object:QM,boolean:VM}},title:{string:LM,undefined:"undefined"},width:{number:YM},widthConstraint:{maximum:{number:YM},__type__:{object:QM,boolean:VM,number:YM}},value:{number:YM,undefined:"undefined"},__type__:{object:QM}},groups:{useDefaultGroups:{boolean:VM},__any__:_M,__type__:{object:QM}},interaction:{dragNodes:{boolean:VM},dragView:{boolean:VM},hideEdgesOnDrag:{boolean:VM},hideEdgesOnZoom:{boolean:VM},hideNodesOnDrag:{boolean:VM},hover:{boolean:VM},keyboard:{enabled:{boolean:VM},speed:{x:{number:YM},y:{number:YM},zoom:{number:YM},__type__:{object:QM}},bindToWindow:{boolean:VM},autoFocus:{boolean:VM},__type__:{object:QM,boolean:VM}},multiselect:{boolean:VM},navigationButtons:{boolean:VM},selectable:{boolean:VM},selectConnectedEdges:{boolean:VM},hoverConnectedEdges:{boolean:VM},tooltipDelay:{number:YM},zoomView:{boolean:VM},zoomSpeed:{number:YM},__type__:{object:QM}},layout:{randomSeed:{undefined:"undefined",number:YM,string:LM},improvedLayout:{boolean:VM},clusterThreshold:{number:YM},hierarchical:{enabled:{boolean:VM},levelSeparation:{number:YM},nodeSpacing:{number:YM},treeSpacing:{number:YM},blockShifting:{boolean:VM},edgeMinimization:{boolean:VM},parentCentralization:{boolean:VM},direction:{string:["UD","DU","LR","RL"]},sortMethod:{string:["hubsize","directed"]},shakeTowards:{string:["leaves","roots"]},__type__:{object:QM,boolean:VM}},__type__:{object:QM}},manipulation:{enabled:{boolean:VM},initiallyActive:{boolean:VM},addNode:{boolean:VM,function:"function"},addEdge:{boolean:VM,function:"function"},editNode:{function:"function"},editEdge:{editWithoutDrag:{function:"function"},__type__:{object:QM,boolean:VM,function:"function"}},deleteNode:{boolean:VM,function:"function"},deleteEdge:{boolean:VM,function:"function"},controlNodeStyle:_M,__type__:{object:QM,boolean:VM}},nodes:_M,physics:{enabled:{boolean:VM},barnesHut:{theta:{number:YM},gravitationalConstant:{number:YM},centralGravity:{number:YM},springLength:{number:YM},springConstant:{number:YM},damping:{number:YM},avoidOverlap:{number:YM},__type__:{object:QM}},forceAtlas2Based:{theta:{number:YM},gravitationalConstant:{number:YM},centralGravity:{number:YM},springLength:{number:YM},springConstant:{number:YM},damping:{number:YM},avoidOverlap:{number:YM},__type__:{object:QM}},repulsion:{centralGravity:{number:YM},springLength:{number:YM},springConstant:{number:YM},nodeDistance:{number:YM},damping:{number:YM},__type__:{object:QM}},hierarchicalRepulsion:{centralGravity:{number:YM},springLength:{number:YM},springConstant:{number:YM},nodeDistance:{number:YM},damping:{number:YM},avoidOverlap:{number:YM},__type__:{object:QM}},maxVelocity:{number:YM},minVelocity:{number:YM},solver:{string:["barnesHut","repulsion","hierarchicalRepulsion","forceAtlas2Based"]},stabilization:{enabled:{boolean:VM},iterations:{number:YM},updateInterval:{number:YM},onlyDynamicEdges:{boolean:VM},fit:{boolean:VM},__type__:{object:QM,boolean:VM}},timestep:{number:YM},adaptiveTimestep:{boolean:VM},wind:{x:{number:YM},y:{number:YM},__type__:{object:QM}},__type__:{object:QM,boolean:VM}},autoResize:{boolean:VM},clickToUse:{boolean:VM},locale:{string:LM},locales:{__any__:{any:"any"},__type__:{object:QM}},height:{string:LM},width:{string:LM},__type__:{object:QM}},HM={nodes:{borderWidth:[1,0,10,1],borderWidthSelected:[2,0,10,1],color:{border:["color","#2B7CE9"],background:["color","#97C2FC"],highlight:{border:["color","#2B7CE9"],background:["color","#D2E5FF"]},hover:{border:["color","#2B7CE9"],background:["color","#D2E5FF"]}},opacity:[0,0,1,.1],fixed:{x:!1,y:!1},font:{color:["color","#343434"],size:[14,0,100,1],face:["arial","verdana","tahoma"],background:["color","none"],strokeWidth:[0,0,50,1],strokeColor:["color","#ffffff"]},hidden:!1,labelHighlightBold:!0,physics:!0,scaling:{min:[10,0,200,1],max:[30,0,200,1],label:{enabled:!1,min:[14,0,200,1],max:[30,0,200,1],maxVisible:[30,0,200,1],drawThreshold:[5,0,20,1]}},shadow:{enabled:!1,color:"rgba(0,0,0,0.5)",size:[10,0,20,1],x:[5,-30,30,1],y:[5,-30,30,1]},shape:["ellipse","box","circle","database","diamond","dot","square","star","text","triangle","triangleDown","hexagon"],shapeProperties:{borderDashes:!1,borderRadius:[6,0,20,1],interpolation:!0,useImageSize:!1},size:[25,0,200,1]},edges:{arrows:{to:{enabled:!1,scaleFactor:[1,0,3,.05],type:"arrow"},middle:{enabled:!1,scaleFactor:[1,0,3,.05],type:"arrow"},from:{enabled:!1,scaleFactor:[1,0,3,.05],type:"arrow"}},endPointOffset:{from:[0,-10,10,1],to:[0,-10,10,1]},arrowStrikethrough:!0,color:{color:["color","#848484"],highlight:["color","#848484"],hover:["color","#848484"],inherit:["from","to","both",!0,!1],opacity:[1,0,1,.05]},dashes:!1,font:{color:["color","#343434"],size:[14,0,100,1],face:["arial","verdana","tahoma"],background:["color","none"],strokeWidth:[2,0,50,1],strokeColor:["color","#ffffff"],align:["horizontal","top","middle","bottom"]},hidden:!1,hoverWidth:[1.5,0,5,.1],labelHighlightBold:!0,physics:!0,scaling:{min:[1,0,100,1],max:[15,0,100,1],label:{enabled:!0,min:[14,0,200,1],max:[30,0,200,1],maxVisible:[30,0,200,1],drawThreshold:[5,0,20,1]}},selectionWidth:[1.5,0,5,.1],selfReferenceSize:[20,0,200,1],selfReference:{size:[20,0,200,1],angle:[Math.PI/2,-6*Math.PI,6*Math.PI,Math.PI/8],renderBehindTheNode:!0},shadow:{enabled:!1,color:"rgba(0,0,0,0.5)",size:[10,0,20,1],x:[5,-30,30,1],y:[5,-30,30,1]},smooth:{enabled:!0,type:["dynamic","continuous","discrete","diagonalCross","straightCross","horizontal","vertical","curvedCW","curvedCCW","cubicBezier"],forceDirection:["horizontal","vertical","none"],roundness:[.5,0,1,.05]},width:[1,0,30,1]},layout:{hierarchical:{enabled:!1,levelSeparation:[150,20,500,5],nodeSpacing:[100,20,500,5],treeSpacing:[200,20,500,5],blockShifting:!0,edgeMinimization:!0,parentCentralization:!0,direction:["UD","DU","LR","RL"],sortMethod:["hubsize","directed"],shakeTowards:["leaves","roots"]}},interaction:{dragNodes:!0,dragView:!0,hideEdgesOnDrag:!1,hideEdgesOnZoom:!1,hideNodesOnDrag:!1,hover:!1,keyboard:{enabled:!1,speed:{x:[10,0,40,1],y:[10,0,40,1],zoom:[.02,0,.1,.005]},bindToWindow:!0,autoFocus:!0},multiselect:!1,navigationButtons:!1,selectable:!0,selectConnectedEdges:!0,hoverConnectedEdges:!0,tooltipDelay:[300,0,1e3,25],zoomView:!0,zoomSpeed:[1,.1,2,.1]},manipulation:{enabled:!1,initiallyActive:!1},physics:{enabled:!0,barnesHut:{theta:[.5,.1,1,.05],gravitationalConstant:[-2e3,-3e4,0,50],centralGravity:[.3,0,10,.05],springLength:[95,0,500,5],springConstant:[.04,0,1.2,.005],damping:[.09,0,1,.01],avoidOverlap:[0,0,1,.01]},forceAtlas2Based:{theta:[.5,.1,1,.05],gravitationalConstant:[-50,-500,0,1],centralGravity:[.01,0,1,.005],springLength:[95,0,500,5],springConstant:[.08,0,1.2,.005],damping:[.4,0,1,.01],avoidOverlap:[0,0,1,.01]},repulsion:{centralGravity:[.2,0,10,.05],springLength:[200,0,500,5],springConstant:[.05,0,1.2,.005],nodeDistance:[100,0,500,5],damping:[.09,0,1,.01]},hierarchicalRepulsion:{centralGravity:[.2,0,10,.05],springLength:[100,0,500,5],springConstant:[.01,0,1.2,.005],nodeDistance:[120,0,500,5],damping:[.09,0,1,.01],avoidOverlap:[0,0,1,.01]},maxVelocity:[50,0,150,1],minVelocity:[.1,.01,.5,.01],solver:["barnesHut","forceAtlas2Based","repulsion","hierarchicalRepulsion"],timestep:[.5,.01,1,.01],wind:{x:[0,-10,10,.1],y:[0,-10,10,.1]}}},XM=function(g,t,A){var e;return!(!Ic(g).call(g,"physics")||!Ic(e=HM.physics.solver).call(e,t)||A.physics.solver===t||"wind"===t)},JM=Object.freeze({__proto__:null,allOptions:KM,configuratorHideOption:XM,configureOptions:HM}),qM=function(){function g(){cn(this,g)}return kd(g,[{key:"getDistances",value:function(g,t,A){for(var e={},C=g.edges,I=0;I2&&void 0!==arguments[2]&&arguments[2],e=this.distanceSolver.getDistances(this.body,g,t);this._createL_matrix(e),this._createK_matrix(e),this._createE_matrix();for(var C=0,I=Math.max(1e3,Math.min(10*this.body.nodeIndices.length,6e3)),i=1e9,o=0,n=0,r=0,s=0,a=0;i>.01&&C1&&a<5;){a+=1,this._moveNode(o,n,r);var h=lh(this._getEnergy(o),3);s=h[0],n=h[1],r=h[2]}}}},{key:"_getHighestEnergyNode",value:function(g){for(var t=this.body.nodeIndices,A=this.body.nodes,e=0,C=t[0],I=0,i=0,o=0;o=0.2.26", + "glirel>=1.2.1", + "huggingface-hub>=1.13.0", + "jupyter>=1.1.1", + "jupyter-collaboration>=4.3.0", + "jupyter-mcp-server>=1.0.2", + "jupyterlab>=4.5.7", + "loguru>=0.7.3", + "matplotlib>=3.10.9", + "numpy>=2.4.4", + "pandas>=3.0.2", + "torch>=2.11.0", + "transformers>=5.1.0", +] diff --git a/results.json b/results.json new file mode 100644 index 0000000..33e0793 --- /dev/null +++ b/results.json @@ -0,0 +1,21981 @@ +{ + "gliner_threshold_sweep": { + "es_corporate": { + "generic_en": { + "scored_at_t0": [ + [ + "Pablo Isla", + "person", + 0.9911110997200012, + 0, + 10 + ], + [ + ",", + "person", + 1.6853056195031968e-06, + 10, + 11 + ], + [ + "expresidente", + "person", + 0.021584728732705116, + 12, + 24 + ], + [ + "de", + "person", + 6.913928245921852e-06, + 25, + 27 + ], + [ + "Inditex", + "organization", + 0.9930588006973267, + 28, + 35 + ], + [ + ",", + "location", + 4.370950591692235e-06, + 35, + 36 + ], + [ + "ha sido nombrado", + "person", + 4.862711193709401e-06, + 37, + 53 + ], + [ + "consejero", + "person", + 0.004995330702513456, + 54, + 63 + ], + [ + "de", + "person", + 2.4509423383278772e-05, + 64, + 66 + ], + [ + "Telefonica", + "organization", + 0.9577405452728271, + 67, + 77 + ], + [ + ".", + "location", + 4.368502050056122e-05, + 77, + 78 + ], + [ + "La", + "location", + 2.3745260477880947e-06, + 79, + 81 + ], + [ + "operacion", + "location", + 6.816197856096551e-05, + 82, + 91 + ], + [ + "fue", + "location", + 1.7475873903549655e-07, + 92, + 95 + ], + [ + "anunciada", + "location", + 8.555597901249712e-07, + 96, + 105 + ], + [ + "por", + "person", + 3.2284933695336804e-06, + 106, + 109 + ], + [ + "el", + "person", + 4.3937320697295945e-06, + 110, + 112 + ], + [ + "presidente", + "person", + 0.011064347811043262, + 113, + 123 + ], + [ + "Jose Maria Alvarez-Pallete", + "person", + 0.930793285369873, + 124, + 150 + ], + [ + "en", + "location", + 1.4325776646728627e-05, + 151, + 153 + ], + [ + "Madrid", + "location", + 0.937868058681488, + 154, + 160 + ], + [ + "el", + "location", + 6.321909609141585e-07, + 161, + 163 + ], + [ + "pasado", + "location", + 2.1469898570103396e-07, + 164, + 170 + ], + [ + "lunes", + "location", + 0.0009435326210223138, + 171, + 176 + ], + [ + ".", + "location", + 5.148024865775369e-05, + 176, + 177 + ], + [ + "Inditex", + "organization", + 0.9940288066864014, + 178, + 185 + ], + [ + "factura", + "location", + 7.691662176512182e-05, + 186, + 193 + ], + [ + "mas de", + "location", + 1.9888393580913544e-06, + 194, + 200 + ], + [ + "30.000 millones", + "location", + 1.813305243558716e-05, + 201, + 216 + ], + [ + "anuales", + "location", + 8.353593875654042e-05, + 217, + 224 + ], + [ + "y", + "location", + 0.00011144810559926555, + 225, + 226 + ], + [ + "tiene", + "location", + 3.401484264031751e-06, + 227, + 232 + ], + [ + "su", + "location", + 1.1745783012884203e-06, + 233, + 235 + ], + [ + "sede", + "location", + 0.0002565919130574912, + 236, + 240 + ], + [ + "en", + "location", + 2.7078674975200556e-05, + 241, + 243 + ], + [ + "Arteixo", + "location", + 0.9632577300071716, + 244, + 251 + ], + [ + ",", + "location", + 5.296558356349124e-06, + 251, + 252 + ], + [ + "A Coruna", + "location", + 0.9184865951538086, + 253, + 261 + ], + [ + ".", + "location", + 2.2771551812184043e-05, + 261, + 262 + ] + ], + "t=0.1": [ + [ + "Pablo Isla", + "person", + 0.9911110997200012, + 0, + 10 + ], + [ + "Inditex", + "organization", + 0.9930588006973267, + 28, + 35 + ], + [ + "Telefonica", + "organization", + 0.9577405452728271, + 67, + 77 + ], + [ + "Jose Maria Alvarez-Pallete", + "person", + 0.930793285369873, + 124, + 150 + ], + [ + "Madrid", + "location", + 0.937868058681488, + 154, + 160 + ], + [ + "Inditex", + "organization", + 0.9940288066864014, + 178, + 185 + ], + [ + "Arteixo", + "location", + 0.9632577300071716, + 244, + 251 + ], + [ + "A Coruna", + "location", + 0.9184865951538086, + 253, + 261 + ] + ], + "t=0.3": [ + [ + "Pablo Isla", + "person", + 0.9911110997200012, + 0, + 10 + ], + [ + "Inditex", + "organization", + 0.9930588006973267, + 28, + 35 + ], + [ + "Telefonica", + "organization", + 0.9577405452728271, + 67, + 77 + ], + [ + "Jose Maria Alvarez-Pallete", + "person", + 0.930793285369873, + 124, + 150 + ], + [ + "Madrid", + "location", + 0.937868058681488, + 154, + 160 + ], + [ + "Inditex", + "organization", + 0.9940288066864014, + 178, + 185 + ], + [ + "Arteixo", + "location", + 0.9632577300071716, + 244, + 251 + ], + [ + "A Coruna", + "location", + 0.9184865951538086, + 253, + 261 + ] + ], + "t=0.5": [ + [ + "Pablo Isla", + "person", + 0.9911110997200012, + 0, + 10 + ], + [ + "Inditex", + "organization", + 0.9930588006973267, + 28, + 35 + ], + [ + "Telefonica", + "organization", + 0.9577405452728271, + 67, + 77 + ], + [ + "Jose Maria Alvarez-Pallete", + "person", + 0.930793285369873, + 124, + 150 + ], + [ + "Madrid", + "location", + 0.937868058681488, + 154, + 160 + ], + [ + "Inditex", + "organization", + 0.9940288066864014, + 178, + 185 + ], + [ + "Arteixo", + "location", + 0.9632577300071716, + 244, + 251 + ], + [ + "A Coruna", + "location", + 0.9184865951538086, + 253, + 261 + ] + ], + "t=0.7": [ + [ + "Pablo Isla", + "person", + 0.9911110997200012, + 0, + 10 + ], + [ + "Inditex", + "organization", + 0.9930588006973267, + 28, + 35 + ], + [ + "Telefonica", + "organization", + 0.9577405452728271, + 67, + 77 + ], + [ + "Jose Maria Alvarez-Pallete", + "person", + 0.930793285369873, + 124, + 150 + ], + [ + "Madrid", + "location", + 0.937868058681488, + 154, + 160 + ], + [ + "Inditex", + "organization", + 0.9940288066864014, + 178, + 185 + ], + [ + "Arteixo", + "location", + 0.9632577300071716, + 244, + 251 + ], + [ + "A Coruna", + "location", + 0.9184865951538086, + 253, + 261 + ] + ], + "t=0.9": [ + [ + "Pablo Isla", + "person", + 0.9911110997200012, + 0, + 10 + ], + [ + "Inditex", + "organization", + 0.9930588006973267, + 28, + 35 + ], + [ + "Telefonica", + "organization", + 0.9577405452728271, + 67, + 77 + ], + [ + "Jose Maria Alvarez-Pallete", + "person", + 0.930793285369873, + 124, + 150 + ], + [ + "Madrid", + "location", + 0.937868058681488, + 154, + 160 + ], + [ + "Inditex", + "organization", + 0.9940288066864014, + 178, + 185 + ], + [ + "Arteixo", + "location", + 0.9632577300071716, + 244, + 251 + ], + [ + "A Coruna", + "location", + 0.9184865951538086, + 253, + 261 + ] + ] + }, + "generic_es": { + "scored_at_t0": [ + [ + "Pablo Isla", + "persona", + 0.9903932213783264, + 0, + 10 + ], + [ + ",", + "persona", + 1.7537462326799869e-06, + 10, + 11 + ], + [ + "expresidente", + "persona", + 0.01917741820216179, + 12, + 24 + ], + [ + "de", + "persona", + 7.2318844104302116e-06, + 25, + 27 + ], + [ + "Inditex", + "organizacion", + 0.9879774451255798, + 28, + 35 + ], + [ + ",", + "lugar", + 4.1888242776622064e-06, + 35, + 36 + ], + [ + "ha sido nombrado", + "persona", + 6.279437911871355e-06, + 37, + 53 + ], + [ + "consejero", + "persona", + 0.005231588147580624, + 54, + 63 + ], + [ + "de", + "persona", + 2.8927930543432012e-05, + 64, + 66 + ], + [ + "Telefonica", + "organizacion", + 0.9448623657226562, + 67, + 77 + ], + [ + ".", + "lugar", + 4.270602948963642e-05, + 77, + 78 + ], + [ + "La", + "lugar", + 2.0286961444071494e-06, + 79, + 81 + ], + [ + "operacion", + "lugar", + 7.016087329247966e-05, + 82, + 91 + ], + [ + "fue", + "lugar", + 1.3586051750280603e-07, + 92, + 95 + ], + [ + "anunciada", + "lugar", + 7.640914532203169e-07, + 96, + 105 + ], + [ + "por", + "persona", + 3.6059632293472532e-06, + 106, + 109 + ], + [ + "el", + "persona", + 6.189765827002702e-06, + 110, + 112 + ], + [ + "presidente", + "persona", + 0.013509081676602364, + 113, + 123 + ], + [ + "Jose Maria Alvarez-Pallete", + "persona", + 0.9311891198158264, + 124, + 150 + ], + [ + "en", + "lugar", + 1.1979211194557138e-05, + 151, + 153 + ], + [ + "Madrid", + "lugar", + 0.9362973570823669, + 154, + 160 + ], + [ + "el", + "lugar", + 5.664022637574817e-07, + 161, + 163 + ], + [ + "pasado", + "lugar", + 1.6568134242334054e-07, + 164, + 170 + ], + [ + "lunes", + "lugar", + 0.0009855992393568158, + 171, + 176 + ], + [ + ".", + "lugar", + 4.9337599193677306e-05, + 176, + 177 + ], + [ + "Inditex", + "organizacion", + 0.9900063872337341, + 178, + 185 + ], + [ + "factura", + "lugar", + 7.096219633240253e-05, + 186, + 193 + ], + [ + "mas de", + "lugar", + 1.5881098534009652e-06, + 194, + 200 + ], + [ + "30.000 millones", + "lugar", + 1.5641817299183458e-05, + 201, + 216 + ], + [ + "anuales", + "lugar", + 8.289254765259102e-05, + 217, + 224 + ], + [ + "y", + "lugar", + 0.0001040301431203261, + 225, + 226 + ], + [ + "tiene", + "lugar", + 3.1867689358477946e-06, + 227, + 232 + ], + [ + "su", + "lugar", + 1.0182993719354272e-06, + 233, + 235 + ], + [ + "sede", + "lugar", + 0.00025734605151228607, + 236, + 240 + ], + [ + "en", + "lugar", + 1.9880350009771064e-05, + 241, + 243 + ], + [ + "Arteixo", + "lugar", + 0.9599928855895996, + 244, + 251 + ], + [ + ",", + "lugar", + 4.036833615828073e-06, + 251, + 252 + ], + [ + "A Coruna", + "lugar", + 0.9021164774894714, + 253, + 261 + ], + [ + ".", + "lugar", + 1.7919799574883655e-05, + 261, + 262 + ] + ], + "t=0.1": [ + [ + "Pablo Isla", + "persona", + 0.9903932213783264, + 0, + 10 + ], + [ + "Inditex", + "organizacion", + 0.9879774451255798, + 28, + 35 + ], + [ + "Telefonica", + "organizacion", + 0.9448623657226562, + 67, + 77 + ], + [ + "Jose Maria Alvarez-Pallete", + "persona", + 0.9311891198158264, + 124, + 150 + ], + [ + "Madrid", + "lugar", + 0.9362973570823669, + 154, + 160 + ], + [ + "Inditex", + "organizacion", + 0.9900063872337341, + 178, + 185 + ], + [ + "Arteixo", + "lugar", + 0.9599928855895996, + 244, + 251 + ], + [ + "A Coruna", + "lugar", + 0.9021164774894714, + 253, + 261 + ] + ], + "t=0.3": [ + [ + "Pablo Isla", + "persona", + 0.9903932213783264, + 0, + 10 + ], + [ + "Inditex", + "organizacion", + 0.9879774451255798, + 28, + 35 + ], + [ + "Telefonica", + "organizacion", + 0.9448623657226562, + 67, + 77 + ], + [ + "Jose Maria Alvarez-Pallete", + "persona", + 0.9311891198158264, + 124, + 150 + ], + [ + "Madrid", + "lugar", + 0.9362973570823669, + 154, + 160 + ], + [ + "Inditex", + "organizacion", + 0.9900063872337341, + 178, + 185 + ], + [ + "Arteixo", + "lugar", + 0.9599928855895996, + 244, + 251 + ], + [ + "A Coruna", + "lugar", + 0.9021164774894714, + 253, + 261 + ] + ], + "t=0.5": [ + [ + "Pablo Isla", + "persona", + 0.9903932213783264, + 0, + 10 + ], + [ + "Inditex", + "organizacion", + 0.9879774451255798, + 28, + 35 + ], + [ + "Telefonica", + "organizacion", + 0.9448623657226562, + 67, + 77 + ], + [ + "Jose Maria Alvarez-Pallete", + "persona", + 0.9311891198158264, + 124, + 150 + ], + [ + "Madrid", + "lugar", + 0.9362973570823669, + 154, + 160 + ], + [ + "Inditex", + "organizacion", + 0.9900063872337341, + 178, + 185 + ], + [ + "Arteixo", + "lugar", + 0.9599928855895996, + 244, + 251 + ], + [ + "A Coruna", + "lugar", + 0.9021164774894714, + 253, + 261 + ] + ], + "t=0.7": [ + [ + "Pablo Isla", + "persona", + 0.9903932213783264, + 0, + 10 + ], + [ + "Inditex", + "organizacion", + 0.9879774451255798, + 28, + 35 + ], + [ + "Telefonica", + "organizacion", + 0.9448623657226562, + 67, + 77 + ], + [ + "Jose Maria Alvarez-Pallete", + "persona", + 0.9311891198158264, + 124, + 150 + ], + [ + "Madrid", + "lugar", + 0.9362973570823669, + 154, + 160 + ], + [ + "Inditex", + "organizacion", + 0.9900063872337341, + 178, + 185 + ], + [ + "Arteixo", + "lugar", + 0.9599928855895996, + 244, + 251 + ], + [ + "A Coruna", + "lugar", + 0.9021164774894714, + 253, + 261 + ] + ], + "t=0.9": [ + [ + "Pablo Isla", + "persona", + 0.9903932213783264, + 0, + 10 + ], + [ + "Inditex", + "organizacion", + 0.9879774451255798, + 28, + 35 + ], + [ + "Telefonica", + "organizacion", + 0.9448623657226562, + 67, + 77 + ], + [ + "Jose Maria Alvarez-Pallete", + "persona", + 0.9311891198158264, + 124, + 150 + ], + [ + "Madrid", + "lugar", + 0.9362973570823669, + 154, + 160 + ], + [ + "Inditex", + "organizacion", + 0.9900063872337341, + 178, + 185 + ], + [ + "Arteixo", + "lugar", + 0.9599928855895996, + 244, + 251 + ], + [ + "A Coruna", + "lugar", + 0.9021164774894714, + 253, + 261 + ] + ] + } + }, + "en_corporate": { + "generic_en": { + "scored_at_t0": [ + [ + "Pablo Isla", + "person", + 0.9893019795417786, + 0, + 10 + ], + [ + ",", + "person", + 2.7556693567021284e-06, + 10, + 11 + ], + [ + "the", + "person", + 4.661894763557939e-06, + 12, + 15 + ], + [ + "former chairman", + "person", + 0.02128879725933075, + 16, + 31 + ], + [ + "of", + "person", + 4.865512892138213e-06, + 32, + 34 + ], + [ + "Inditex", + "organization", + 0.9923794269561768, + 35, + 42 + ], + [ + ",", + "location", + 4.558582077152096e-06, + 42, + 43 + ], + [ + "has been appointed", + "person", + 2.7452347239886876e-06, + 44, + 62 + ], + [ + "as", + "location", + 1.5391195518077438e-07, + 63, + 65 + ], + [ + "a", + "person", + 3.8638327168882824e-06, + 66, + 67 + ], + [ + "director", + "person", + 0.01317476388067007, + 68, + 76 + ], + [ + "of", + "person", + 9.818654689297546e-06, + 77, + 79 + ], + [ + "Telefonica", + "organization", + 0.9926980137825012, + 80, + 90 + ], + [ + ".", + "location", + 1.2453729141270742e-05, + 90, + 91 + ], + [ + "The", + "location", + 3.205386065019411e-06, + 92, + 95 + ], + [ + "announcement", + "location", + 1.2319373126956634e-05, + 96, + 108 + ], + [ + "was", + "location", + 1.4020783112300705e-07, + 109, + 112 + ], + [ + "made", + "location", + 1.924582164747335e-07, + 113, + 117 + ], + [ + "by", + "person", + 2.914254991992493e-06, + 118, + 120 + ], + [ + "Jose Maria Alvarez-Pallete", + "person", + 0.9755329489707947, + 121, + 147 + ], + [ + ",", + "person", + 5.881748279534804e-07, + 147, + 148 + ], + [ + "the", + "person", + 1.593539764144225e-06, + 149, + 152 + ], + [ + "chairman", + "person", + 0.012248773127794266, + 153, + 161 + ], + [ + "of", + "person", + 2.2381234430213226e-06, + 162, + 164 + ], + [ + "Telefonica", + "organization", + 0.990853488445282, + 165, + 175 + ], + [ + ",", + "location", + 1.8045193428406492e-05, + 175, + 176 + ], + [ + "in", + "location", + 3.187761103617959e-05, + 177, + 179 + ], + [ + "Madrid", + "location", + 0.9660689830780029, + 180, + 186 + ], + [ + "last", + "location", + 2.199637037847424e-06, + 187, + 191 + ], + [ + "Monday", + "location", + 0.0002045426081167534, + 192, + 198 + ], + [ + ".", + "location", + 1.989204974961467e-05, + 198, + 199 + ], + [ + "Inditex", + "organization", + 0.9946492314338684, + 200, + 207 + ], + [ + "has", + "location", + 3.4629334550118074e-05, + 208, + 211 + ], + [ + "its", + "location", + 3.838355041807517e-06, + 212, + 215 + ], + [ + "headquarters", + "location", + 0.0012061605229973793, + 216, + 228 + ], + [ + "in", + "location", + 2.2206546418601647e-05, + 229, + 231 + ], + [ + "Arteixo", + "location", + 0.9689206480979919, + 232, + 239 + ], + [ + ",", + "location", + 2.353664513066178e-06, + 239, + 240 + ], + [ + "A Coruna", + "location", + 0.9204289317131042, + 241, + 249 + ], + [ + ".", + "location", + 1.4836260561423842e-05, + 249, + 250 + ] + ], + "t=0.1": [ + [ + "Pablo Isla", + "person", + 0.9893019795417786, + 0, + 10 + ], + [ + "Inditex", + "organization", + 0.9923794269561768, + 35, + 42 + ], + [ + "Telefonica", + "organization", + 0.9926980137825012, + 80, + 90 + ], + [ + "Jose Maria Alvarez-Pallete", + "person", + 0.9755329489707947, + 121, + 147 + ], + [ + "Telefonica", + "organization", + 0.990853488445282, + 165, + 175 + ], + [ + "Madrid", + "location", + 0.9660689830780029, + 180, + 186 + ], + [ + "Inditex", + "organization", + 0.9946492314338684, + 200, + 207 + ], + [ + "Arteixo", + "location", + 0.9689206480979919, + 232, + 239 + ], + [ + "A Coruna", + "location", + 0.9204289317131042, + 241, + 249 + ] + ], + "t=0.3": [ + [ + "Pablo Isla", + "person", + 0.9893019795417786, + 0, + 10 + ], + [ + "Inditex", + "organization", + 0.9923794269561768, + 35, + 42 + ], + [ + "Telefonica", + "organization", + 0.9926980137825012, + 80, + 90 + ], + [ + "Jose Maria Alvarez-Pallete", + "person", + 0.9755329489707947, + 121, + 147 + ], + [ + "Telefonica", + "organization", + 0.990853488445282, + 165, + 175 + ], + [ + "Madrid", + "location", + 0.9660689830780029, + 180, + 186 + ], + [ + "Inditex", + "organization", + 0.9946492314338684, + 200, + 207 + ], + [ + "Arteixo", + "location", + 0.9689206480979919, + 232, + 239 + ], + [ + "A Coruna", + "location", + 0.9204289317131042, + 241, + 249 + ] + ], + "t=0.5": [ + [ + "Pablo Isla", + "person", + 0.9893019795417786, + 0, + 10 + ], + [ + "Inditex", + "organization", + 0.9923794269561768, + 35, + 42 + ], + [ + "Telefonica", + "organization", + 0.9926980137825012, + 80, + 90 + ], + [ + "Jose Maria Alvarez-Pallete", + "person", + 0.9755329489707947, + 121, + 147 + ], + [ + "Telefonica", + "organization", + 0.990853488445282, + 165, + 175 + ], + [ + "Madrid", + "location", + 0.9660689830780029, + 180, + 186 + ], + [ + "Inditex", + "organization", + 0.9946492314338684, + 200, + 207 + ], + [ + "Arteixo", + "location", + 0.9689206480979919, + 232, + 239 + ], + [ + "A Coruna", + "location", + 0.9204289317131042, + 241, + 249 + ] + ], + "t=0.7": [ + [ + "Pablo Isla", + "person", + 0.9893019795417786, + 0, + 10 + ], + [ + "Inditex", + "organization", + 0.9923794269561768, + 35, + 42 + ], + [ + "Telefonica", + "organization", + 0.9926980137825012, + 80, + 90 + ], + [ + "Jose Maria Alvarez-Pallete", + "person", + 0.9755329489707947, + 121, + 147 + ], + [ + "Telefonica", + "organization", + 0.990853488445282, + 165, + 175 + ], + [ + "Madrid", + "location", + 0.9660689830780029, + 180, + 186 + ], + [ + "Inditex", + "organization", + 0.9946492314338684, + 200, + 207 + ], + [ + "Arteixo", + "location", + 0.9689206480979919, + 232, + 239 + ], + [ + "A Coruna", + "location", + 0.9204289317131042, + 241, + 249 + ] + ], + "t=0.9": [ + [ + "Pablo Isla", + "person", + 0.9893019795417786, + 0, + 10 + ], + [ + "Inditex", + "organization", + 0.9923794269561768, + 35, + 42 + ], + [ + "Telefonica", + "organization", + 0.9926980137825012, + 80, + 90 + ], + [ + "Jose Maria Alvarez-Pallete", + "person", + 0.9755329489707947, + 121, + 147 + ], + [ + "Telefonica", + "organization", + 0.990853488445282, + 165, + 175 + ], + [ + "Madrid", + "location", + 0.9660689830780029, + 180, + 186 + ], + [ + "Inditex", + "organization", + 0.9946492314338684, + 200, + 207 + ], + [ + "Arteixo", + "location", + 0.9689206480979919, + 232, + 239 + ], + [ + "A Coruna", + "location", + 0.9204289317131042, + 241, + 249 + ] + ] + }, + "specific_en": { + "scored_at_t0": [ + [ + "Pablo Isla", + "executive", + 0.9257709980010986, + 0, + 10 + ], + [ + ",", + "country", + 4.597236966219498e-06, + 10, + 11 + ], + [ + "the", + "country", + 8.381376801480656e-07, + 12, + 15 + ], + [ + "former chairman", + "executive", + 0.03001151792705059, + 16, + 31 + ], + [ + "of", + "country", + 1.7600543742446462e-06, + 32, + 34 + ], + [ + "Inditex", + "company", + 0.9842042326927185, + 35, + 42 + ], + [ + ",", + "country", + 7.013108188402839e-06, + 42, + 43 + ], + [ + "has been appointed", + "country", + 2.358052597628557e-06, + 44, + 62 + ], + [ + "as", + "country", + 2.192042956039586e-07, + 63, + 65 + ], + [ + "a", + "country", + 1.1128407777505345e-06, + 66, + 67 + ], + [ + "director", + "executive", + 0.02118716388940811, + 68, + 76 + ], + [ + "of", + "country", + 9.427359145774972e-06, + 77, + 79 + ], + [ + "Telefonica", + "company", + 0.9871489405632019, + 80, + 90 + ], + [ + ".", + "country", + 1.0686669156712014e-05, + 90, + 91 + ], + [ + "The", + "country", + 2.830287712640711e-06, + 92, + 95 + ], + [ + "announcement", + "country", + 3.454680836512125e-06, + 96, + 108 + ], + [ + "was", + "country", + 1.033129919392195e-07, + 109, + 112 + ], + [ + "made", + "country", + 7.474000796037217e-08, + 113, + 117 + ], + [ + "by", + "executive", + 2.2674576030112803e-06, + 118, + 120 + ], + [ + "Jose Maria Alvarez-Pallete", + "executive", + 0.9090309143066406, + 121, + 147 + ], + [ + ",", + "country", + 1.0618326768963016e-06, + 147, + 148 + ], + [ + "the", + "country", + 3.533995425186731e-07, + 149, + 152 + ], + [ + "chairman", + "executive", + 0.020491207018494606, + 153, + 161 + ], + [ + "of", + "country", + 1.2943163483214448e-06, + 162, + 164 + ], + [ + "Telefonica", + "company", + 0.9825392961502075, + 165, + 175 + ], + [ + ",", + "country", + 5.937208698014729e-06, + 175, + 176 + ], + [ + "in", + "country", + 1.1564780834305566e-05, + 177, + 179 + ], + [ + "Madrid", + "city", + 0.9572996497154236, + 180, + 186 + ], + [ + "last", + "country", + 7.701283379901724e-07, + 187, + 191 + ], + [ + "Monday", + "country", + 2.0789175323443487e-05, + 192, + 198 + ], + [ + ".", + "country", + 2.3054519260767847e-05, + 198, + 199 + ], + [ + "Inditex", + "company", + 0.9914904236793518, + 200, + 207 + ], + [ + "has", + "country", + 1.2460356629162561e-05, + 208, + 211 + ], + [ + "its", + "country", + 2.0582726847351296e-06, + 212, + 215 + ], + [ + "headquarters", + "country", + 1.3742592273047194e-05, + 216, + 228 + ], + [ + "in", + "country", + 4.814105068362551e-06, + 229, + 231 + ], + [ + "Arteixo", + "city", + 0.9426121115684509, + 232, + 239 + ], + [ + ",", + "country", + 1.5250698197633028e-06, + 239, + 240 + ], + [ + "A Coruna", + "city", + 0.8490824103355408, + 241, + 249 + ], + [ + ".", + "country", + 7.827539775462355e-06, + 249, + 250 + ] + ], + "t=0.1": [ + [ + "Pablo Isla", + "executive", + 0.9257709980010986, + 0, + 10 + ], + [ + "Inditex", + "company", + 0.9842042326927185, + 35, + 42 + ], + [ + "Telefonica", + "company", + 0.9871489405632019, + 80, + 90 + ], + [ + "Jose Maria Alvarez-Pallete", + "executive", + 0.9090309143066406, + 121, + 147 + ], + [ + "Telefonica", + "company", + 0.9825392961502075, + 165, + 175 + ], + [ + "Madrid", + "city", + 0.9572996497154236, + 180, + 186 + ], + [ + "Inditex", + "company", + 0.9914904236793518, + 200, + 207 + ], + [ + "Arteixo", + "city", + 0.9426121115684509, + 232, + 239 + ], + [ + "A Coruna", + "city", + 0.8490824103355408, + 241, + 249 + ] + ], + "t=0.3": [ + [ + "Pablo Isla", + "executive", + 0.9257709980010986, + 0, + 10 + ], + [ + "Inditex", + "company", + 0.9842042326927185, + 35, + 42 + ], + [ + "Telefonica", + "company", + 0.9871489405632019, + 80, + 90 + ], + [ + "Jose Maria Alvarez-Pallete", + "executive", + 0.9090309143066406, + 121, + 147 + ], + [ + "Telefonica", + "company", + 0.9825392961502075, + 165, + 175 + ], + [ + "Madrid", + "city", + 0.9572996497154236, + 180, + 186 + ], + [ + "Inditex", + "company", + 0.9914904236793518, + 200, + 207 + ], + [ + "Arteixo", + "city", + 0.9426121115684509, + 232, + 239 + ], + [ + "A Coruna", + "city", + 0.8490824103355408, + 241, + 249 + ] + ], + "t=0.5": [ + [ + "Pablo Isla", + "executive", + 0.9257709980010986, + 0, + 10 + ], + [ + "Inditex", + "company", + 0.9842042326927185, + 35, + 42 + ], + [ + "Telefonica", + "company", + 0.9871489405632019, + 80, + 90 + ], + [ + "Jose Maria Alvarez-Pallete", + "executive", + 0.9090309143066406, + 121, + 147 + ], + [ + "Telefonica", + "company", + 0.9825392961502075, + 165, + 175 + ], + [ + "Madrid", + "city", + 0.9572996497154236, + 180, + 186 + ], + [ + "Inditex", + "company", + 0.9914904236793518, + 200, + 207 + ], + [ + "Arteixo", + "city", + 0.9426121115684509, + 232, + 239 + ], + [ + "A Coruna", + "city", + 0.8490824103355408, + 241, + 249 + ] + ], + "t=0.7": [ + [ + "Pablo Isla", + "executive", + 0.9257709980010986, + 0, + 10 + ], + [ + "Inditex", + "company", + 0.9842042326927185, + 35, + 42 + ], + [ + "Telefonica", + "company", + 0.9871489405632019, + 80, + 90 + ], + [ + "Jose Maria Alvarez-Pallete", + "executive", + 0.9090309143066406, + 121, + 147 + ], + [ + "Telefonica", + "company", + 0.9825392961502075, + 165, + 175 + ], + [ + "Madrid", + "city", + 0.9572996497154236, + 180, + 186 + ], + [ + "Inditex", + "company", + 0.9914904236793518, + 200, + 207 + ], + [ + "Arteixo", + "city", + 0.9426121115684509, + 232, + 239 + ], + [ + "A Coruna", + "city", + 0.8490824103355408, + 241, + 249 + ] + ], + "t=0.9": [ + [ + "Pablo Isla", + "executive", + 0.9257709980010986, + 0, + 10 + ], + [ + "Inditex", + "company", + 0.9842042326927185, + 35, + 42 + ], + [ + "Telefonica", + "company", + 0.9871489405632019, + 80, + 90 + ], + [ + "Jose Maria Alvarez-Pallete", + "executive", + 0.9090309143066406, + 121, + 147 + ], + [ + "Telefonica", + "company", + 0.9825392961502075, + 165, + 175 + ], + [ + "Madrid", + "city", + 0.9572996497154236, + 180, + 186 + ], + [ + "Inditex", + "company", + 0.9914904236793518, + 200, + 207 + ], + [ + "Arteixo", + "city", + 0.9426121115684509, + 232, + 239 + ] + ] + } + }, + "en_osint": { + "generic_en": { + "scored_at_t0": [ + [ + "On", + "location", + 0.000242614682065323, + 0, + 2 + ], + [ + "2024-08-15", + "location", + 0.01849178783595562, + 3, + 13 + ], + [ + ",", + "location", + 2.3853570382925682e-05, + 13, + 14 + ], + [ + "attacker", + "person", + 0.49933260679244995, + 15, + 23 + ], + [ + "IP", + "location", + 0.019924964755773544, + 24, + 26 + ], + [ + "185.220.101.45", + "location", + 0.24652555584907532, + 27, + 41 + ], + [ + "connected to", + "location", + 4.082354644197039e-05, + 42, + 54 + ], + [ + "victim host 10.0.5.22", + "location", + 0.26901763677597046, + 55, + 76 + ], + [ + "over", + "location", + 7.589137385366485e-05, + 77, + 81 + ], + [ + "TLS", + "location", + 0.12784942984580994, + 82, + 85 + ], + [ + ".", + "location", + 3.922734686057083e-05, + 85, + 86 + ], + [ + "Reverse DNS", + "location", + 0.012762893922626972, + 87, + 98 + ], + [ + "pointed", + "location", + 3.116135121672414e-05, + 99, + 106 + ], + [ + "to", + "location", + 1.9505430827848613e-05, + 107, + 109 + ], + [ + "tor-exit-relay-3.onionrouter.net", + "location", + 0.32278329133987427, + 110, + 142 + ], + [ + ".", + "location", + 2.8590728106792085e-05, + 142, + 143 + ], + [ + "Operator", + "person", + 0.2004188448190689, + 144, + 152 + ], + [ + "handle", + "person", + 0.004020289517939091, + 153, + 159 + ], + [ + "@phantomzero", + "person", + 0.33905452489852905, + 160, + 172 + ], + [ + "claimed", + "location", + 2.382917227805592e-05, + 173, + 180 + ], + [ + "responsibility", + "person", + 0.0017743870848789811, + 181, + 195 + ], + [ + "on", + "location", + 1.773460826370865e-05, + 196, + 198 + ], + [ + "a", + "location", + 4.799267844646238e-05, + 199, + 200 + ], + [ + "forum", + "location", + 0.1228615865111351, + 201, + 206 + ], + [ + ".", + "location", + 4.631220508599654e-05, + 206, + 207 + ], + [ + "The", + "location", + 3.412727528484538e-05, + 208, + 211 + ], + [ + "C2 panel", + "location", + 0.17108745872974396, + 212, + 220 + ], + [ + "was", + "location", + 6.138174649095163e-05, + 221, + 224 + ], + [ + "hosted", + "location", + 0.0004303480382077396, + 225, + 231 + ], + [ + "on", + "location", + 0.00021554992417804897, + 232, + 234 + ], + [ + "hxxps", + "organization", + 0.3875216245651245, + 235, + 240 + ], + [ + ":", + "location", + 0.0002051633200608194, + 240, + 241 + ], + [ + "/", + "location", + 0.00017831021978054196, + 241, + 242 + ], + [ + "/", + "location", + 0.0003602904034778476, + 242, + 243 + ], + [ + "malwareops", + "organization", + 0.4583947956562042, + 243, + 253 + ], + [ + "[.]", + "location", + 0.0002661934995558113, + 253, + 256 + ], + [ + "biz", + "location", + 0.013022921979427338, + 256, + 259 + ], + [ + "/", + "location", + 0.0004903397057205439, + 259, + 260 + ], + [ + "control", + "location", + 0.049613434821367264, + 260, + 267 + ], + [ + "behind", + "location", + 0.0005323891527950764, + 268, + 274 + ], + [ + "Cloudflare", + "organization", + 0.6036984324455261, + 275, + 285 + ], + [ + ".", + "location", + 0.00016318625421263278, + 285, + 286 + ] + ], + "t=0.1": [ + [ + "attacker", + "person", + 0.49933260679244995, + 15, + 23 + ], + [ + "185.220.101.45", + "location", + 0.24652555584907532, + 27, + 41 + ], + [ + "victim host 10.0.5.22", + "location", + 0.26901763677597046, + 55, + 76 + ], + [ + "TLS", + "location", + 0.12784942984580994, + 82, + 85 + ], + [ + "tor-exit-relay-3.onionrouter.net", + "location", + 0.32278329133987427, + 110, + 142 + ], + [ + "Operator", + "person", + 0.2004188448190689, + 144, + 152 + ], + [ + "@phantomzero", + "person", + 0.33905452489852905, + 160, + 172 + ], + [ + "forum", + "location", + 0.1228615865111351, + 201, + 206 + ], + [ + "C2 panel", + "location", + 0.17108745872974396, + 212, + 220 + ], + [ + "hxxps", + "organization", + 0.3875216245651245, + 235, + 240 + ], + [ + "malwareops", + "organization", + 0.4583947956562042, + 243, + 253 + ], + [ + "Cloudflare", + "organization", + 0.6036984324455261, + 275, + 285 + ] + ], + "t=0.3": [ + [ + "attacker", + "person", + 0.49933260679244995, + 15, + 23 + ], + [ + "tor-exit-relay-3.onionrouter.net", + "location", + 0.32278329133987427, + 110, + 142 + ], + [ + "@phantomzero", + "person", + 0.33905452489852905, + 160, + 172 + ], + [ + "hxxps", + "organization", + 0.3875216245651245, + 235, + 240 + ], + [ + "malwareops", + "organization", + 0.4583947956562042, + 243, + 253 + ], + [ + "Cloudflare", + "organization", + 0.6036984324455261, + 275, + 285 + ] + ], + "t=0.5": [ + [ + "Cloudflare", + "organization", + 0.6036984324455261, + 275, + 285 + ] + ], + "t=0.7": [], + "t=0.9": [] + }, + "osint_en": { + "scored_at_t0": [ + [ + "On", + "date", + 0.00042652490083128214, + 0, + 2 + ], + [ + "2024-08-15", + "date", + 0.9228310585021973, + 3, + 13 + ], + [ + ",", + "organization", + 1.3819706055073766e-06, + 13, + 14 + ], + [ + "attacker", + "person", + 0.15986879169940948, + 15, + 23 + ], + [ + "IP", + "ip_address", + 0.017772480845451355, + 24, + 26 + ], + [ + "185.220.101.45", + "ip_address", + 0.9531722664833069, + 27, + 41 + ], + [ + "connected to", + "person", + 4.935699053021381e-06, + 42, + 54 + ], + [ + "victim", + "organization", + 0.016750099137425423, + 55, + 61 + ], + [ + "host", + "domain", + 0.00705950241535902, + 62, + 66 + ], + [ + "10.0.5.22", + "ip_address", + 0.6794759631156921, + 67, + 76 + ], + [ + "over", + "ip_address", + 4.9688196668284945e-06, + 77, + 81 + ], + [ + "TLS", + "url", + 0.025105435401201248, + 82, + 85 + ], + [ + ".", + "ip_address", + 8.169183161044202e-07, + 85, + 86 + ], + [ + "Reverse DNS", + "url", + 0.01021672785282135, + 87, + 98 + ], + [ + "pointed", + "url", + 5.440783297672169e-06, + 99, + 106 + ], + [ + "to", + "url", + 1.9211838662158698e-06, + 107, + 109 + ], + [ + "tor-exit-relay-3", + "domain", + 0.420555979013443, + 110, + 126 + ], + [ + ".", + "url", + 3.228019340895116e-05, + 126, + 127 + ], + [ + "onionrouter.net", + "domain", + 0.5889706015586853, + 127, + 142 + ], + [ + ".", + "organization", + 1.0823470120158163e-06, + 142, + 143 + ], + [ + "Operator handle", + "person", + 0.1748666614294052, + 144, + 159 + ], + [ + "@phantomzero", + "username", + 0.685979962348938, + 160, + 172 + ], + [ + "claimed", + "person", + 3.5640950954984874e-05, + 173, + 180 + ], + [ + "responsibility", + "person", + 0.0012845858000218868, + 181, + 195 + ], + [ + "on", + "person", + 4.799655471288133e-06, + 196, + 198 + ], + [ + "a", + "person", + 0.00018450109928380698, + 199, + 200 + ], + [ + "forum", + "organization", + 0.10086772590875626, + 201, + 206 + ], + [ + ".", + "organization", + 4.102962520846631e-06, + 206, + 207 + ], + [ + "The", + "person", + 2.2754706151317805e-05, + 208, + 211 + ], + [ + "C2 panel", + "organization", + 0.1485409140586853, + 212, + 220 + ], + [ + "was", + "organization", + 6.032581040926743e-06, + 221, + 224 + ], + [ + "hosted", + "url", + 8.234028791775927e-05, + 225, + 231 + ], + [ + "on", + "organization", + 4.027276736451313e-05, + 232, + 234 + ], + [ + "hxxps", + "organization", + 0.29609251022338867, + 235, + 240 + ], + [ + ":", + "organization", + 0.00021037795522715896, + 240, + 241 + ], + [ + "/", + "organization", + 0.00046151806600391865, + 241, + 242 + ], + [ + "/", + "organization", + 0.0007663745782338083, + 242, + 243 + ], + [ + "malwareops", + "organization", + 0.3236088752746582, + 243, + 253 + ], + [ + "[.]", + "organization", + 0.0001879613264463842, + 253, + 256 + ], + [ + "biz", + "organization", + 0.0021231994032859802, + 256, + 259 + ], + [ + "/", + "organization", + 0.00042948819464072585, + 259, + 260 + ], + [ + "control", + "organization", + 0.016889091581106186, + 260, + 267 + ], + [ + "behind", + "person", + 9.027814667206258e-05, + 268, + 274 + ], + [ + "Cloudflare", + "organization", + 0.5057231187820435, + 275, + 285 + ], + [ + ".", + "organization", + 1.536921081424225e-05, + 285, + 286 + ] + ], + "t=0.1": [ + [ + "2024-08-15", + "date", + 0.9228310585021973, + 3, + 13 + ], + [ + "attacker", + "person", + 0.15986879169940948, + 15, + 23 + ], + [ + "185.220.101.45", + "ip_address", + 0.9531722664833069, + 27, + 41 + ], + [ + "10.0.5.22", + "ip_address", + 0.6794759631156921, + 67, + 76 + ], + [ + "tor-exit-relay-3", + "domain", + 0.420555979013443, + 110, + 126 + ], + [ + "onionrouter.net", + "domain", + 0.5889706015586853, + 127, + 142 + ], + [ + "Operator handle", + "person", + 0.1748666614294052, + 144, + 159 + ], + [ + "@phantomzero", + "username", + 0.685979962348938, + 160, + 172 + ], + [ + "forum", + "organization", + 0.10086772590875626, + 201, + 206 + ], + [ + "C2 panel", + "organization", + 0.1485409140586853, + 212, + 220 + ], + [ + "hxxps", + "organization", + 0.29609251022338867, + 235, + 240 + ], + [ + "malwareops", + "organization", + 0.3236088752746582, + 243, + 253 + ], + [ + "Cloudflare", + "organization", + 0.5057231187820435, + 275, + 285 + ] + ], + "t=0.3": [ + [ + "2024-08-15", + "date", + 0.9228310585021973, + 3, + 13 + ], + [ + "185.220.101.45", + "ip_address", + 0.9531722664833069, + 27, + 41 + ], + [ + "10.0.5.22", + "ip_address", + 0.6794759631156921, + 67, + 76 + ], + [ + "tor-exit-relay-3", + "domain", + 0.420555979013443, + 110, + 126 + ], + [ + "onionrouter.net", + "domain", + 0.5889706015586853, + 127, + 142 + ], + [ + "@phantomzero", + "username", + 0.685979962348938, + 160, + 172 + ], + [ + "malwareops", + "organization", + 0.3236088752746582, + 243, + 253 + ], + [ + "Cloudflare", + "organization", + 0.5057231187820435, + 275, + 285 + ] + ], + "t=0.5": [ + [ + "2024-08-15", + "date", + 0.9228310585021973, + 3, + 13 + ], + [ + "185.220.101.45", + "ip_address", + 0.9531722664833069, + 27, + 41 + ], + [ + "10.0.5.22", + "ip_address", + 0.6794759631156921, + 67, + 76 + ], + [ + "onionrouter.net", + "domain", + 0.5889706015586853, + 127, + 142 + ], + [ + "@phantomzero", + "username", + 0.685979962348938, + 160, + 172 + ], + [ + "Cloudflare", + "organization", + 0.5057231187820435, + 275, + 285 + ] + ], + "t=0.7": [ + [ + "2024-08-15", + "date", + 0.9228310585021973, + 3, + 13 + ], + [ + "185.220.101.45", + "ip_address", + 0.9531722664833069, + 27, + 41 + ] + ], + "t=0.9": [ + [ + "2024-08-15", + "date", + 0.9228310585021973, + 3, + 13 + ], + [ + "185.220.101.45", + "ip_address", + 0.9531722664833069, + 27, + 41 + ] + ] + } + }, + "es_journalism": { + "generic_en": { + "scored_at_t0": [ + [ + "Iberdrola", + "organization", + 0.9946948885917664, + 0, + 9 + ], + [ + "y", + "organization", + 0.00023032042372506112, + 10, + 11 + ], + [ + "Endesa", + "organization", + 0.9914913773536682, + 12, + 18 + ], + [ + "firmaron", + "location", + 2.4177776140277274e-05, + 19, + 27 + ], + [ + "un", + "organization", + 1.2501818673626985e-06, + 28, + 30 + ], + [ + "acuerdo de colaboracion", + "organization", + 0.0002799391222652048, + 31, + 54 + ], + [ + "en", + "location", + 5.184060114515887e-07, + 55, + 57 + ], + [ + "proyectos eolicos", + "organization", + 0.0023724669590592384, + 58, + 75 + ], + [ + "en", + "location", + 3.5116459912387654e-05, + 76, + 78 + ], + [ + "Galicia", + "location", + 0.9058752655982971, + 79, + 86 + ], + [ + ".", + "location", + 5.385310487326933e-06, + 86, + 87 + ], + [ + "El", + "person", + 0.00010220819967798889, + 88, + 90 + ], + [ + "presidente", + "person", + 0.04453543573617935, + 91, + 101 + ], + [ + "de", + "person", + 1.224213519890327e-06, + 102, + 104 + ], + [ + "Iberdrola", + "organization", + 0.9919660091400146, + 105, + 114 + ], + [ + ",", + "organization", + 4.677057461321965e-07, + 114, + 115 + ], + [ + "Ignacio Galan", + "person", + 0.9825153350830078, + 116, + 129 + ], + [ + ",", + "organization", + 2.087946313622524e-06, + 129, + 130 + ], + [ + "se reunio", + "person", + 2.876654889405472e-06, + 131, + 140 + ], + [ + "con", + "person", + 5.251427865005098e-06, + 141, + 144 + ], + [ + "la", + "person", + 0.00012154570868005976, + 145, + 147 + ], + [ + "CEO", + "person", + 0.12000319361686707, + 148, + 151 + ], + [ + "de", + "person", + 4.231242201058194e-06, + 152, + 154 + ], + [ + "Endesa", + "organization", + 0.9894720911979675, + 155, + 161 + ], + [ + ",", + "person", + 1.4098916381044546e-06, + 161, + 162 + ], + [ + "Marina Serrano", + "person", + 0.9759129285812378, + 163, + 177 + ], + [ + ",", + "location", + 7.031223958620103e-07, + 177, + 178 + ], + [ + "en", + "location", + 3.413862714296556e-06, + 179, + 181 + ], + [ + "Bilbao", + "location", + 0.9712718725204468, + 182, + 188 + ], + [ + ".", + "location", + 2.7299640350975096e-05, + 188, + 189 + ], + [ + "El", + "person", + 4.882223038293887e-06, + 190, + 192 + ], + [ + "acuerdo", + "organization", + 0.000937526230700314, + 193, + 200 + ], + [ + "movilizara", + "organization", + 4.643600277631776e-06, + 201, + 211 + ], + [ + "2.000 millones de euros", + "organization", + 4.725847247755155e-05, + 212, + 235 + ], + [ + "en", + "location", + 3.940295414395223e-07, + 236, + 238 + ], + [ + "cinco anos", + "location", + 0.00012958743900526315, + 239, + 249 + ], + [ + ".", + "location", + 2.7320995286572725e-05, + 249, + 250 + ] + ], + "t=0.1": [ + [ + "Iberdrola", + "organization", + 0.9946948885917664, + 0, + 9 + ], + [ + "Endesa", + "organization", + 0.9914913773536682, + 12, + 18 + ], + [ + "Galicia", + "location", + 0.9058752655982971, + 79, + 86 + ], + [ + "Iberdrola", + "organization", + 0.9919660091400146, + 105, + 114 + ], + [ + "Ignacio Galan", + "person", + 0.9825153350830078, + 116, + 129 + ], + [ + "CEO", + "person", + 0.12000319361686707, + 148, + 151 + ], + [ + "Endesa", + "organization", + 0.9894720911979675, + 155, + 161 + ], + [ + "Marina Serrano", + "person", + 0.9759129285812378, + 163, + 177 + ], + [ + "Bilbao", + "location", + 0.9712718725204468, + 182, + 188 + ] + ], + "t=0.3": [ + [ + "Iberdrola", + "organization", + 0.9946948885917664, + 0, + 9 + ], + [ + "Endesa", + "organization", + 0.9914913773536682, + 12, + 18 + ], + [ + "Galicia", + "location", + 0.9058752655982971, + 79, + 86 + ], + [ + "Iberdrola", + "organization", + 0.9919660091400146, + 105, + 114 + ], + [ + "Ignacio Galan", + "person", + 0.9825153350830078, + 116, + 129 + ], + [ + "Endesa", + "organization", + 0.9894720911979675, + 155, + 161 + ], + [ + "Marina Serrano", + "person", + 0.9759129285812378, + 163, + 177 + ], + [ + "Bilbao", + "location", + 0.9712718725204468, + 182, + 188 + ] + ], + "t=0.5": [ + [ + "Iberdrola", + "organization", + 0.9946948885917664, + 0, + 9 + ], + [ + "Endesa", + "organization", + 0.9914913773536682, + 12, + 18 + ], + [ + "Galicia", + "location", + 0.9058752655982971, + 79, + 86 + ], + [ + "Iberdrola", + "organization", + 0.9919660091400146, + 105, + 114 + ], + [ + "Ignacio Galan", + "person", + 0.9825153350830078, + 116, + 129 + ], + [ + "Endesa", + "organization", + 0.9894720911979675, + 155, + 161 + ], + [ + "Marina Serrano", + "person", + 0.9759129285812378, + 163, + 177 + ], + [ + "Bilbao", + "location", + 0.9712718725204468, + 182, + 188 + ] + ], + "t=0.7": [ + [ + "Iberdrola", + "organization", + 0.9946948885917664, + 0, + 9 + ], + [ + "Endesa", + "organization", + 0.9914913773536682, + 12, + 18 + ], + [ + "Galicia", + "location", + 0.9058752655982971, + 79, + 86 + ], + [ + "Iberdrola", + "organization", + 0.9919660091400146, + 105, + 114 + ], + [ + "Ignacio Galan", + "person", + 0.9825153350830078, + 116, + 129 + ], + [ + "Endesa", + "organization", + 0.9894720911979675, + 155, + 161 + ], + [ + "Marina Serrano", + "person", + 0.9759129285812378, + 163, + 177 + ], + [ + "Bilbao", + "location", + 0.9712718725204468, + 182, + 188 + ] + ], + "t=0.9": [ + [ + "Iberdrola", + "organization", + 0.9946948885917664, + 0, + 9 + ], + [ + "Endesa", + "organization", + 0.9914913773536682, + 12, + 18 + ], + [ + "Galicia", + "location", + 0.9058752655982971, + 79, + 86 + ], + [ + "Iberdrola", + "organization", + 0.9919660091400146, + 105, + 114 + ], + [ + "Ignacio Galan", + "person", + 0.9825153350830078, + 116, + 129 + ], + [ + "Endesa", + "organization", + 0.9894720911979675, + 155, + 161 + ], + [ + "Marina Serrano", + "person", + 0.9759129285812378, + 163, + 177 + ], + [ + "Bilbao", + "location", + 0.9712718725204468, + 182, + 188 + ] + ] + }, + "generic_es": { + "scored_at_t0": [ + [ + "Iberdrola", + "organizacion", + 0.9915748238563538, + 0, + 9 + ], + [ + "y", + "organizacion", + 0.00018236694450024515, + 10, + 11 + ], + [ + "Endesa", + "organizacion", + 0.9872617125511169, + 12, + 18 + ], + [ + "firmaron", + "lugar", + 2.4353157641598955e-05, + 19, + 27 + ], + [ + "un", + "organizacion", + 1.0363721685280325e-06, + 28, + 30 + ], + [ + "acuerdo de colaboracion", + "organizacion", + 0.0005728413234464824, + 31, + 54 + ], + [ + "en", + "lugar", + 3.1632103514311893e-07, + 55, + 57 + ], + [ + "proyectos eolicos", + "organizacion", + 0.004740549251437187, + 58, + 75 + ], + [ + "en", + "lugar", + 1.4528617612086236e-05, + 76, + 78 + ], + [ + "Galicia", + "lugar", + 0.8614968061447144, + 79, + 86 + ], + [ + ".", + "lugar", + 3.677491349662887e-06, + 86, + 87 + ], + [ + "El", + "persona", + 0.00011760903726099059, + 88, + 90 + ], + [ + "presidente", + "persona", + 0.044480614364147186, + 91, + 101 + ], + [ + "de", + "persona", + 1.1419874681450892e-06, + 102, + 104 + ], + [ + "Iberdrola", + "organizacion", + 0.9865161180496216, + 105, + 114 + ], + [ + ",", + "persona", + 4.298048565942736e-07, + 114, + 115 + ], + [ + "Ignacio Galan", + "persona", + 0.9806535840034485, + 116, + 129 + ], + [ + ",", + "organizacion", + 1.6552816077819443e-06, + 129, + 130 + ], + [ + "se reunio", + "persona", + 2.6985017029801384e-06, + 131, + 140 + ], + [ + "con", + "persona", + 5.0930248107761145e-06, + 141, + 144 + ], + [ + "la", + "persona", + 0.00012035227700835094, + 145, + 147 + ], + [ + "CEO", + "persona", + 0.11722171306610107, + 148, + 151 + ], + [ + "de", + "persona", + 4.310241820348892e-06, + 152, + 154 + ], + [ + "Endesa", + "organizacion", + 0.9830920100212097, + 155, + 161 + ], + [ + ",", + "persona", + 1.6989464484140626e-06, + 161, + 162 + ], + [ + "Marina Serrano", + "persona", + 0.9751072525978088, + 163, + 177 + ], + [ + ",", + "lugar", + 5.414750035015459e-07, + 177, + 178 + ], + [ + "en", + "lugar", + 2.6072148102684878e-06, + 179, + 181 + ], + [ + "Bilbao", + "lugar", + 0.9743235111236572, + 182, + 188 + ], + [ + ".", + "lugar", + 2.7333113393979147e-05, + 188, + 189 + ], + [ + "El", + "persona", + 4.368937879917212e-06, + 190, + 192 + ], + [ + "acuerdo", + "organizacion", + 0.0009257702040486038, + 193, + 200 + ], + [ + "movilizara", + "organizacion", + 3.552337602741318e-06, + 201, + 211 + ], + [ + "2.000 millones de euros", + "organizacion", + 3.897631904692389e-05, + 212, + 235 + ], + [ + "en", + "lugar", + 2.5330263042633305e-07, + 236, + 238 + ], + [ + "cinco anos", + "lugar", + 0.00012764496204908937, + 239, + 249 + ], + [ + ".", + "lugar", + 1.7865571862785146e-05, + 249, + 250 + ] + ], + "t=0.1": [ + [ + "Iberdrola", + "organizacion", + 0.9915748238563538, + 0, + 9 + ], + [ + "Endesa", + "organizacion", + 0.9872617125511169, + 12, + 18 + ], + [ + "Galicia", + "lugar", + 0.8614968061447144, + 79, + 86 + ], + [ + "Iberdrola", + "organizacion", + 0.9865161180496216, + 105, + 114 + ], + [ + "Ignacio Galan", + "persona", + 0.9806535840034485, + 116, + 129 + ], + [ + "CEO", + "persona", + 0.11722171306610107, + 148, + 151 + ], + [ + "Endesa", + "organizacion", + 0.9830920100212097, + 155, + 161 + ], + [ + "Marina Serrano", + "persona", + 0.9751072525978088, + 163, + 177 + ], + [ + "Bilbao", + "lugar", + 0.9743235111236572, + 182, + 188 + ] + ], + "t=0.3": [ + [ + "Iberdrola", + "organizacion", + 0.9915748238563538, + 0, + 9 + ], + [ + "Endesa", + "organizacion", + 0.9872617125511169, + 12, + 18 + ], + [ + "Galicia", + "lugar", + 0.8614968061447144, + 79, + 86 + ], + [ + "Iberdrola", + "organizacion", + 0.9865161180496216, + 105, + 114 + ], + [ + "Ignacio Galan", + "persona", + 0.9806535840034485, + 116, + 129 + ], + [ + "Endesa", + "organizacion", + 0.9830920100212097, + 155, + 161 + ], + [ + "Marina Serrano", + "persona", + 0.9751072525978088, + 163, + 177 + ], + [ + "Bilbao", + "lugar", + 0.9743235111236572, + 182, + 188 + ] + ], + "t=0.5": [ + [ + "Iberdrola", + "organizacion", + 0.9915748238563538, + 0, + 9 + ], + [ + "Endesa", + "organizacion", + 0.9872617125511169, + 12, + 18 + ], + [ + "Galicia", + "lugar", + 0.8614968061447144, + 79, + 86 + ], + [ + "Iberdrola", + "organizacion", + 0.9865161180496216, + 105, + 114 + ], + [ + "Ignacio Galan", + "persona", + 0.9806535840034485, + 116, + 129 + ], + [ + "Endesa", + "organizacion", + 0.9830920100212097, + 155, + 161 + ], + [ + "Marina Serrano", + "persona", + 0.9751072525978088, + 163, + 177 + ], + [ + "Bilbao", + "lugar", + 0.9743235111236572, + 182, + 188 + ] + ], + "t=0.7": [ + [ + "Iberdrola", + "organizacion", + 0.9915748238563538, + 0, + 9 + ], + [ + "Endesa", + "organizacion", + 0.9872617125511169, + 12, + 18 + ], + [ + "Galicia", + "lugar", + 0.8614968061447144, + 79, + 86 + ], + [ + "Iberdrola", + "organizacion", + 0.9865161180496216, + 105, + 114 + ], + [ + "Ignacio Galan", + "persona", + 0.9806535840034485, + 116, + 129 + ], + [ + "Endesa", + "organizacion", + 0.9830920100212097, + 155, + 161 + ], + [ + "Marina Serrano", + "persona", + 0.9751072525978088, + 163, + 177 + ], + [ + "Bilbao", + "lugar", + 0.9743235111236572, + 182, + 188 + ] + ], + "t=0.9": [ + [ + "Iberdrola", + "organizacion", + 0.9915748238563538, + 0, + 9 + ], + [ + "Endesa", + "organizacion", + 0.9872617125511169, + 12, + 18 + ], + [ + "Iberdrola", + "organizacion", + 0.9865161180496216, + 105, + 114 + ], + [ + "Ignacio Galan", + "persona", + 0.9806535840034485, + 116, + 129 + ], + [ + "Endesa", + "organizacion", + 0.9830920100212097, + 155, + 161 + ], + [ + "Marina Serrano", + "persona", + 0.9751072525978088, + 163, + 177 + ], + [ + "Bilbao", + "lugar", + 0.9743235111236572, + 182, + 188 + ] + ] + } + } + }, + "glirel_score_distribution": { + "es_corporate": { + "entities": [ + [ + "Pablo Isla", + "persona", + 0.99 + ], + [ + "Inditex", + "organizacion", + 0.988 + ], + [ + "Telefonica", + "organizacion", + 0.945 + ], + [ + "Jose Maria Alvarez-Pallete", + "persona", + 0.931 + ], + [ + "Madrid", + "lugar", + 0.936 + ], + [ + "Inditex", + "organizacion", + 0.99 + ], + [ + "Arteixo", + "lugar", + 0.96 + ], + [ + "A Coruna", + "lugar", + 0.902 + ] + ], + "ner": [ + [ + 0, + 2, + "persona" + ], + [ + 4, + 5, + "organizacion" + ], + [ + 10, + 11, + "organizacion" + ], + [ + 18, + 21, + "persona" + ], + [ + 22, + 23, + "lugar" + ], + [ + 26, + 27, + "organizacion" + ], + [ + 38, + 39, + "lugar" + ], + [ + 39, + 41, + "lugar" + ] + ], + "styles": { + "snake_short": [ + { + "label": "located_in", + "score": 0.1694, + "head_text": "Arteixo, A", + "tail_text": "Inditex, ha" + }, + { + "label": "located_in", + "score": 0.1576, + "head_text": "Telefonica. La", + "tail_text": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.1531, + "head_text": "Arteixo, A", + "tail_text": "Inditex factura" + }, + { + "label": "located_in", + "score": 0.1515, + "head_text": "Inditex factura", + "tail_text": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.1458, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.1393, + "head_text": "Inditex, ha", + "tail_text": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.1371, + "head_text": "Arteixo, A", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "located_in", + "score": 0.1371, + "head_text": "Arteixo, A", + "tail_text": "A Coruna." + }, + { + "label": "located_in", + "score": 0.1252, + "head_text": "Madrid el", + "tail_text": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.1229, + "head_text": "Arteixo, A", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "located_in", + "score": 0.1191, + "head_text": "Arteixo, A", + "tail_text": "Telefonica. La" + }, + { + "label": "located_in", + "score": 0.1141, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.1141, + "head_text": "A Coruna.", + "tail_text": "Arteixo, A" + }, + { + "label": "ceo_of", + "score": 0.1127, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Inditex, ha" + }, + { + "label": "appointed_as", + "score": 0.1103, + "head_text": "Telefonica. La", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "appointed_as", + "score": 0.1091, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Telefonica. La" + }, + { + "label": "appointed_as", + "score": 0.0992, + "head_text": "Telefonica. La", + "tail_text": "Inditex, ha" + }, + { + "label": "appointed_as", + "score": 0.099, + "head_text": "Telefonica. La", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "appointed_as", + "score": 0.099, + "head_text": "Telefonica. La", + "tail_text": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.0949, + "head_text": "Madrid el", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "appointed_as", + "score": 0.0949, + "head_text": "Madrid el", + "tail_text": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.0945, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.0924, + "head_text": "Arteixo, A", + "tail_text": "Madrid el" + }, + { + "label": "appointed_as", + "score": 0.0873, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Inditex, ha" + }, + { + "label": "ceo_of", + "score": 0.0868, + "head_text": "Inditex, ha", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "appointed_as", + "score": 0.0863, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Telefonica. La" + }, + { + "label": "appointed_as", + "score": 0.0863, + "head_text": "A Coruna.", + "tail_text": "Telefonica. La" + }, + { + "label": "headquartered_in", + "score": 0.0862, + "head_text": "Telefonica. La", + "tail_text": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.0856, + "head_text": "Madrid el", + "tail_text": "Telefonica. La" + }, + { + "label": "ceo_of", + "score": 0.0838, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Inditex factura" + }, + { + "label": "appointed_as", + "score": 0.0838, + "head_text": "Inditex factura", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "ceo_of", + "score": 0.0827, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "ceo_of", + "score": 0.0827, + "head_text": "A Coruna.", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "appointed_as", + "score": 0.0826, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "appointed_as", + "score": 0.0826, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.0816, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "appointed_as", + "score": 0.0816, + "head_text": "A Coruna.", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "headquartered_in", + "score": 0.0813, + "head_text": "Arteixo, A", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "ceo_of", + "score": 0.0809, + "head_text": "Inditex factura", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "appointed_as", + "score": 0.0793, + "head_text": "Inditex, ha", + "tail_text": "Telefonica. La" + }, + { + "label": "ceo_of", + "score": 0.0785, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "ceo_of", + "score": 0.0785, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.076, + "head_text": "Inditex, ha", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "appointed_as", + "score": 0.0759, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Inditex factura" + }, + { + "label": "headquartered_in", + "score": 0.0758, + "head_text": "Arteixo, A", + "tail_text": "Inditex, ha" + }, + { + "label": "headquartered_in", + "score": 0.0744, + "head_text": "Arteixo, A", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "headquartered_in", + "score": 0.0744, + "head_text": "Arteixo, A", + "tail_text": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0721, + "head_text": "Telefonica. La", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "headquartered_in", + "score": 0.0681, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.0681, + "head_text": "A Coruna.", + "tail_text": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.0679, + "head_text": "Inditex, ha", + "tail_text": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.0672, + "head_text": "Inditex factura", + "tail_text": "Telefonica. La" + }, + { + "label": "headquartered_in", + "score": 0.0668, + "head_text": "Madrid el", + "tail_text": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.0624, + "head_text": "Arteixo, A", + "tail_text": "Telefonica. La" + }, + { + "label": "headquartered_in", + "score": 0.062, + "head_text": "Inditex factura", + "tail_text": "Arteixo, A" + }, + { + "label": "ceo_of", + "score": 0.0587, + "head_text": "Telefonica. La", + "tail_text": "Inditex, ha" + }, + { + "label": "headquartered_in", + "score": 0.0579, + "head_text": "Arteixo, A", + "tail_text": "Inditex factura" + }, + { + "label": "appointed_as", + "score": 0.0574, + "head_text": "Telefonica. La", + "tail_text": "Inditex factura" + }, + { + "label": "ceo_of", + "score": 0.0558, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Inditex, ha" + }, + { + "label": "ceo_of", + "score": 0.0558, + "head_text": "A Coruna.", + "tail_text": "Inditex, ha" + }, + { + "label": "ceo_of", + "score": 0.0554, + "head_text": "Madrid el", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "ceo_of", + "score": 0.0554, + "head_text": "Madrid el", + "tail_text": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0537, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Telefonica. La" + }, + { + "label": "headquartered_in", + "score": 0.05, + "head_text": "Arteixo, A", + "tail_text": "Madrid el" + }, + { + "label": "ceo_of", + "score": 0.0498, + "head_text": "Telefonica. La", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "ceo_of", + "score": 0.0498, + "head_text": "Telefonica. La", + "tail_text": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.049, + "head_text": "Madrid el", + "tail_text": "Inditex, ha" + }, + { + "label": "appointed_as", + "score": 0.045, + "head_text": "Madrid el", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "ceo_of", + "score": 0.0438, + "head_text": "Inditex, ha", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "ceo_of", + "score": 0.0438, + "head_text": "Inditex, ha", + "tail_text": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.0421, + "head_text": "Inditex factura", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "appointed_as", + "score": 0.0421, + "head_text": "Inditex factura", + "tail_text": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.042, + "head_text": "Madrid el", + "tail_text": "Inditex, ha" + }, + { + "label": "appointed_as", + "score": 0.0414, + "head_text": "Pablo Isla, expresidente", + "tail_text": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.0414, + "head_text": "A Coruna.", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "ceo_of", + "score": 0.0408, + "head_text": "Inditex factura", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "ceo_of", + "score": 0.0408, + "head_text": "Inditex factura", + "tail_text": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0406, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Inditex factura" + }, + { + "label": "ceo_of", + "score": 0.0406, + "head_text": "A Coruna.", + "tail_text": "Inditex factura" + }, + { + "label": "appointed_as", + "score": 0.039, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Inditex, ha" + }, + { + "label": "appointed_as", + "score": 0.039, + "head_text": "A Coruna.", + "tail_text": "Inditex, ha" + }, + { + "label": "appointed_as", + "score": 0.0383, + "head_text": "Inditex, ha", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "appointed_as", + "score": 0.0383, + "head_text": "Inditex, ha", + "tail_text": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0378, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Telefonica. La" + }, + { + "label": "ceo_of", + "score": 0.0378, + "head_text": "A Coruna.", + "tail_text": "Telefonica. La" + }, + { + "label": "located_in", + "score": 0.0368, + "head_text": "Madrid el", + "tail_text": "Inditex, ha" + }, + { + "label": "appointed_as", + "score": 0.0363, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Madrid el" + }, + { + "label": "appointed_as", + "score": 0.0363, + "head_text": "A Coruna.", + "tail_text": "Madrid el" + }, + { + "label": "ceo_of", + "score": 0.0353, + "head_text": "Pablo Isla, expresidente", + "tail_text": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0353, + "head_text": "A Coruna.", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "appointed_as", + "score": 0.0347, + "head_text": "Telefonica. La", + "tail_text": "Madrid el" + }, + { + "label": "ceo_of", + "score": 0.0344, + "head_text": "Madrid el", + "tail_text": "Inditex, ha" + }, + { + "label": "ceo_of", + "score": 0.0334, + "head_text": "Inditex, ha", + "tail_text": "Telefonica. La" + }, + { + "label": "ceo_of", + "score": 0.0331, + "head_text": "Telefonica. La", + "tail_text": "Inditex factura" + }, + { + "label": "ceo_of", + "score": 0.0312, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Arteixo, A" + }, + { + "label": "ceo_of", + "score": 0.0312, + "head_text": "A Coruna.", + "tail_text": "Arteixo, A" + }, + { + "label": "ceo_of", + "score": 0.0311, + "head_text": "Arteixo, A", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "ceo_of", + "score": 0.0311, + "head_text": "Arteixo, A", + "tail_text": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.031, + "head_text": "Madrid el", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "headquartered_in", + "score": 0.0309, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Inditex factura" + }, + { + "label": "appointed_as", + "score": 0.0305, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Inditex factura" + }, + { + "label": "appointed_as", + "score": 0.0305, + "head_text": "A Coruna.", + "tail_text": "Inditex factura" + }, + { + "label": "ceo_of", + "score": 0.0304, + "head_text": "Arteixo, A", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "headquartered_in", + "score": 0.0293, + "head_text": "Inditex factura", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "ceo_of", + "score": 0.0292, + "head_text": "Inditex factura", + "tail_text": "Inditex, ha" + }, + { + "label": "ceo_of", + "score": 0.0284, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Arteixo, A" + }, + { + "label": "ceo_of", + "score": 0.028, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Madrid el" + }, + { + "label": "ceo_of", + "score": 0.028, + "head_text": "A Coruna.", + "tail_text": "Madrid el" + }, + { + "label": "ceo_of", + "score": 0.0275, + "head_text": "Telefonica. La", + "tail_text": "Arteixo, A" + }, + { + "label": "ceo_of", + "score": 0.0259, + "head_text": "Inditex, ha", + "tail_text": "Inditex factura" + }, + { + "label": "ceo_of", + "score": 0.0256, + "head_text": "Inditex factura", + "tail_text": "Telefonica. La" + }, + { + "label": "appointed_as", + "score": 0.0255, + "head_text": "Inditex factura", + "tail_text": "Inditex, ha" + }, + { + "label": "ceo_of", + "score": 0.0255, + "head_text": "Madrid el", + "tail_text": "Telefonica. La" + }, + { + "label": "located_in", + "score": 0.0247, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Inditex factura" + }, + { + "label": "works_at", + "score": 0.0239, + "head_text": "Telefonica. La", + "tail_text": "Inditex factura" + }, + { + "label": "located_in", + "score": 0.0239, + "head_text": "Inditex factura", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "headquartered_in", + "score": 0.0237, + "head_text": "Madrid el", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "headquartered_in", + "score": 0.0237, + "head_text": "Madrid el", + "tail_text": "A Coruna." + }, + { + "label": "located_in", + "score": 0.0236, + "head_text": "Madrid el", + "tail_text": "Inditex factura" + }, + { + "label": "headquartered_in", + "score": 0.0222, + "head_text": "Inditex, ha", + "tail_text": "Madrid el" + }, + { + "label": "headquartered_in", + "score": 0.022, + "head_text": "Madrid el", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "works_at", + "score": 0.022, + "head_text": "Madrid el", + "tail_text": "Telefonica. La" + }, + { + "label": "ceo_of", + "score": 0.0216, + "head_text": "Arteixo, A", + "tail_text": "Telefonica. La" + }, + { + "label": "appointed_as", + "score": 0.0207, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Madrid el" + }, + { + "label": "appointed_as", + "score": 0.0206, + "head_text": "Inditex, ha", + "tail_text": "Inditex factura" + }, + { + "label": "works_at", + "score": 0.0202, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Inditex factura" + }, + { + "label": "located_in", + "score": 0.0202, + "head_text": "Inditex factura", + "tail_text": "Inditex, ha" + }, + { + "label": "works_at", + "score": 0.0199, + "head_text": "Telefonica. La", + "tail_text": "Inditex, ha" + }, + { + "label": "appointed_as", + "score": 0.0197, + "head_text": "Inditex, ha", + "tail_text": "Madrid el" + }, + { + "label": "headquartered_in", + "score": 0.0194, + "head_text": "Madrid el", + "tail_text": "Inditex factura" + }, + { + "label": "located_in", + "score": 0.0193, + "head_text": "Inditex, ha", + "tail_text": "Madrid el" + }, + { + "label": "headquartered_in", + "score": 0.0192, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Madrid el" + }, + { + "label": "works_at", + "score": 0.0192, + "head_text": "Inditex factura", + "tail_text": "Telefonica. La" + }, + { + "label": "headquartered_in", + "score": 0.0192, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Inditex, ha" + }, + { + "label": "headquartered_in", + "score": 0.0188, + "head_text": "Madrid el", + "tail_text": "Telefonica. La" + }, + { + "label": "headquartered_in", + "score": 0.0181, + "head_text": "Inditex factura", + "tail_text": "Inditex, ha" + }, + { + "label": "located_in", + "score": 0.0176, + "head_text": "Madrid el", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "located_in", + "score": 0.0176, + "head_text": "Madrid el", + "tail_text": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0175, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Madrid el" + }, + { + "label": "located_in", + "score": 0.0174, + "head_text": "Madrid el", + "tail_text": "Telefonica. La" + }, + { + "label": "located_in", + "score": 0.0174, + "head_text": "Inditex factura", + "tail_text": "Madrid el" + }, + { + "label": "appointed_as", + "score": 0.0171, + "head_text": "Arteixo, A", + "tail_text": "Telefonica. La" + }, + { + "label": "ceo_of", + "score": 0.0168, + "head_text": "Telefonica. La", + "tail_text": "Madrid el" + }, + { + "label": "headquartered_in", + "score": 0.0168, + "head_text": "Telefonica. La", + "tail_text": "Madrid el" + }, + { + "label": "works_at", + "score": 0.0168, + "head_text": "Madrid el", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "works_at", + "score": 0.0168, + "head_text": "Madrid el", + "tail_text": "A Coruna." + }, + { + "label": "located_in", + "score": 0.0166, + "head_text": "Madrid el", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "works_at", + "score": 0.0164, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Telefonica. La" + }, + { + "label": "works_at", + "score": 0.0162, + "head_text": "Telefonica. La", + "tail_text": "Madrid el" + }, + { + "label": "located_in", + "score": 0.0158, + "head_text": "Inditex, ha", + "tail_text": "Inditex factura" + }, + { + "label": "works_at", + "score": 0.0158, + "head_text": "Telefonica. La", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "ceo_of", + "score": 0.0155, + "head_text": "Inditex, ha", + "tail_text": "Madrid el" + }, + { + "label": "works_at", + "score": 0.0155, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Madrid el" + }, + { + "label": "works_at", + "score": 0.0155, + "head_text": "A Coruna.", + "tail_text": "Madrid el" + }, + { + "label": "works_at", + "score": 0.0155, + "head_text": "Inditex, ha", + "tail_text": "Telefonica. La" + }, + { + "label": "headquartered_in", + "score": 0.0153, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Madrid el" + }, + { + "label": "headquartered_in", + "score": 0.0153, + "head_text": "A Coruna.", + "tail_text": "Madrid el" + }, + { + "label": "headquartered_in", + "score": 0.0151, + "head_text": "Inditex, ha", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "located_in", + "score": 0.0151, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Madrid el" + }, + { + "label": "works_at", + "score": 0.0151, + "head_text": "Inditex factura", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "located_in", + "score": 0.0147, + "head_text": "Telefonica. La", + "tail_text": "Madrid el" + }, + { + "label": "headquartered_in", + "score": 0.0145, + "head_text": "Inditex, ha", + "tail_text": "Inditex factura" + }, + { + "label": "works_at", + "score": 0.0145, + "head_text": "Madrid el", + "tail_text": "Inditex, ha" + }, + { + "label": "headquartered_in", + "score": 0.0145, + "head_text": "Inditex factura", + "tail_text": "Madrid el" + }, + { + "label": "headquartered_in", + "score": 0.0135, + "head_text": "Telefonica. La", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "works_at", + "score": 0.0133, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Telefonica. La" + }, + { + "label": "works_at", + "score": 0.0133, + "head_text": "A Coruna.", + "tail_text": "Telefonica. La" + }, + { + "label": "located_in", + "score": 0.0131, + "head_text": "Telefonica. La", + "tail_text": "Inditex factura" + }, + { + "label": "headquartered_in", + "score": 0.0129, + "head_text": "Telefonica. La", + "tail_text": "Inditex factura" + }, + { + "label": "located_in", + "score": 0.0124, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Inditex, ha" + }, + { + "label": "works_at", + "score": 0.0123, + "head_text": "Arteixo, A", + "tail_text": "Telefonica. La" + }, + { + "label": "appointed_as", + "score": 0.0115, + "head_text": "Madrid el", + "tail_text": "Inditex factura" + }, + { + "label": "appointed_as", + "score": 0.0113, + "head_text": "Arteixo, A", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "works_at", + "score": 0.0113, + "head_text": "Telefonica. La", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "works_at", + "score": 0.0113, + "head_text": "Telefonica. La", + "tail_text": "A Coruna." + }, + { + "label": "located_in", + "score": 0.0109, + "head_text": "Inditex factura", + "tail_text": "Telefonica. La" + }, + { + "label": "located_in", + "score": 0.0108, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Madrid el" + }, + { + "label": "located_in", + "score": 0.0108, + "head_text": "A Coruna.", + "tail_text": "Madrid el" + }, + { + "label": "headquartered_in", + "score": 0.0107, + "head_text": "Telefonica. La", + "tail_text": "Inditex, ha" + }, + { + "label": "ceo_of", + "score": 0.0107, + "head_text": "Inditex, ha", + "tail_text": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.0106, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Telefonica. La" + }, + { + "label": "works_at", + "score": 0.0104, + "head_text": "Inditex, ha", + "tail_text": "Inditex factura" + }, + { + "label": "headquartered_in", + "score": 0.0102, + "head_text": "Inditex factura", + "tail_text": "Telefonica. La" + }, + { + "label": "works_at", + "score": 0.0102, + "head_text": "Inditex, ha", + "tail_text": "Madrid el" + }, + { + "label": "located_in", + "score": 0.01, + "head_text": "Inditex, ha", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "ceo_of", + "score": 0.0098, + "head_text": "Arteixo, A", + "tail_text": "Inditex, ha" + }, + { + "label": "located_in", + "score": 0.0097, + "head_text": "Telefonica. La", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "works_at", + "score": 0.0096, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Inditex, ha" + }, + { + "label": "works_at", + "score": 0.0096, + "head_text": "Inditex factura", + "tail_text": "Inditex, ha" + }, + { + "label": "works_at", + "score": 0.0095, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Inditex factura" + }, + { + "label": "works_at", + "score": 0.0095, + "head_text": "A Coruna.", + "tail_text": "Inditex factura" + }, + { + "label": "located_in", + "score": 0.0091, + "head_text": "Telefonica. La", + "tail_text": "Inditex, ha" + }, + { + "label": "works_at", + "score": 0.0082, + "head_text": "Inditex, ha", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "located_in", + "score": 0.0081, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Telefonica. La" + }, + { + "label": "works_at", + "score": 0.0081, + "head_text": "Telefonica. La", + "tail_text": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.0073, + "head_text": "Arteixo, A", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "appointed_as", + "score": 0.0073, + "head_text": "Arteixo, A", + "tail_text": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.0072, + "head_text": "Inditex factura", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "headquartered_in", + "score": 0.0072, + "head_text": "Inditex factura", + "tail_text": "A Coruna." + }, + { + "label": "works_at", + "score": 0.0072, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "works_at", + "score": 0.0072, + "head_text": "A Coruna.", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "appointed_as", + "score": 0.0069, + "head_text": "Inditex factura", + "tail_text": "Madrid el" + }, + { + "label": "works_at", + "score": 0.0069, + "head_text": "Inditex factura", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "works_at", + "score": 0.0069, + "head_text": "Inditex factura", + "tail_text": "A Coruna." + }, + { + "label": "works_at", + "score": 0.0068, + "head_text": "Madrid el", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "ceo_of", + "score": 0.0068, + "head_text": "Madrid el", + "tail_text": "Inditex factura" + }, + { + "label": "headquartered_in", + "score": 0.0067, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "headquartered_in", + "score": 0.0067, + "head_text": "A Coruna.", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "headquartered_in", + "score": 0.0067, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "headquartered_in", + "score": 0.0067, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.0067, + "head_text": "Inditex, ha", + "tail_text": "Telefonica. La" + }, + { + "label": "works_at", + "score": 0.0063, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Madrid el" + }, + { + "label": "located_in", + "score": 0.0062, + "head_text": "Inditex factura", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "located_in", + "score": 0.0062, + "head_text": "Inditex factura", + "tail_text": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.0061, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Inditex factura" + }, + { + "label": "headquartered_in", + "score": 0.0061, + "head_text": "A Coruna.", + "tail_text": "Inditex factura" + }, + { + "label": "located_in", + "score": 0.006, + "head_text": "Inditex, ha", + "tail_text": "Telefonica. La" + }, + { + "label": "works_at", + "score": 0.0057, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "works_at", + "score": 0.0057, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "A Coruna." + }, + { + "label": "works_at", + "score": 0.0055, + "head_text": "Madrid el", + "tail_text": "Inditex factura" + }, + { + "label": "works_at", + "score": 0.0052, + "head_text": "Arteixo, A", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "headquartered_in", + "score": 0.0049, + "head_text": "Telefonica. La", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "headquartered_in", + "score": 0.0049, + "head_text": "Telefonica. La", + "tail_text": "A Coruna." + }, + { + "label": "located_in", + "score": 0.0047, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Inditex factura" + }, + { + "label": "located_in", + "score": 0.0047, + "head_text": "A Coruna.", + "tail_text": "Inditex factura" + }, + { + "label": "works_at", + "score": 0.0046, + "head_text": "Arteixo, A", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "works_at", + "score": 0.0046, + "head_text": "Arteixo, A", + "tail_text": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0046, + "head_text": "Inditex factura", + "tail_text": "Arteixo, A" + }, + { + "label": "ceo_of", + "score": 0.0043, + "head_text": "Inditex factura", + "tail_text": "Madrid el" + }, + { + "label": "ceo_of", + "score": 0.0043, + "head_text": "Madrid el", + "tail_text": "Arteixo, A" + }, + { + "label": "works_at", + "score": 0.0042, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Inditex, ha" + }, + { + "label": "works_at", + "score": 0.0042, + "head_text": "A Coruna.", + "tail_text": "Inditex, ha" + }, + { + "label": "works_at", + "score": 0.0042, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Arteixo, A" + }, + { + "label": "works_at", + "score": 0.0042, + "head_text": "A Coruna.", + "tail_text": "Arteixo, A" + }, + { + "label": "works_at", + "score": 0.0042, + "head_text": "Inditex factura", + "tail_text": "Madrid el" + }, + { + "label": "located_in", + "score": 0.0039, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "located_in", + "score": 0.0039, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "A Coruna." + }, + { + "label": "works_at", + "score": 0.0038, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.0037, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "located_in", + "score": 0.0037, + "head_text": "A Coruna.", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "headquartered_in", + "score": 0.0037, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Telefonica. La" + }, + { + "label": "headquartered_in", + "score": 0.0037, + "head_text": "A Coruna.", + "tail_text": "Telefonica. La" + }, + { + "label": "located_in", + "score": 0.0036, + "head_text": "Telefonica. La", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "located_in", + "score": 0.0036, + "head_text": "Telefonica. La", + "tail_text": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.0036, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Inditex, ha" + }, + { + "label": "headquartered_in", + "score": 0.0036, + "head_text": "A Coruna.", + "tail_text": "Inditex, ha" + }, + { + "label": "ceo_of", + "score": 0.0035, + "head_text": "Arteixo, A", + "tail_text": "Inditex factura" + }, + { + "label": "headquartered_in", + "score": 0.0034, + "head_text": "Inditex, ha", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "headquartered_in", + "score": 0.0034, + "head_text": "Inditex, ha", + "tail_text": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.0033, + "head_text": "Telefonica. La", + "tail_text": "Arteixo, A" + }, + { + "label": "works_at", + "score": 0.0032, + "head_text": "Inditex, ha", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "works_at", + "score": 0.0032, + "head_text": "Inditex, ha", + "tail_text": "A Coruna." + }, + { + "label": "works_at", + "score": 0.0029, + "head_text": "Arteixo, A", + "tail_text": "Madrid el" + }, + { + "label": "located_in", + "score": 0.0027, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Telefonica. La" + }, + { + "label": "located_in", + "score": 0.0027, + "head_text": "A Coruna.", + "tail_text": "Telefonica. La" + }, + { + "label": "ceo_of", + "score": 0.0027, + "head_text": "Arteixo, A", + "tail_text": "Madrid el" + }, + { + "label": "works_at", + "score": 0.0024, + "head_text": "Pablo Isla, expresidente", + "tail_text": "A Coruna." + }, + { + "label": "works_at", + "score": 0.0024, + "head_text": "A Coruna.", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "located_in", + "score": 0.0024, + "head_text": "Inditex, ha", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "located_in", + "score": 0.0024, + "head_text": "Inditex, ha", + "tail_text": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.0024, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.0023, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Inditex, ha" + }, + { + "label": "located_in", + "score": 0.0023, + "head_text": "A Coruna.", + "tail_text": "Inditex, ha" + }, + { + "label": "works_at", + "score": 0.0023, + "head_text": "Arteixo, A", + "tail_text": "Inditex factura" + }, + { + "label": "appointed_as", + "score": 0.0023, + "head_text": "Arteixo, A", + "tail_text": "Madrid el" + }, + { + "label": "works_at", + "score": 0.0022, + "head_text": "Arteixo, A", + "tail_text": "Inditex, ha" + }, + { + "label": "works_at", + "score": 0.002, + "head_text": "Madrid el", + "tail_text": "Arteixo, A" + }, + { + "label": "works_at", + "score": 0.0019, + "head_text": "Inditex, ha", + "tail_text": "Arteixo, A" + }, + { + "label": "works_at", + "score": 0.0018, + "head_text": "Inditex factura", + "tail_text": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.0017, + "head_text": "Arteixo, A", + "tail_text": "Inditex, ha" + }, + { + "label": "appointed_as", + "score": 0.0016, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.0016, + "head_text": "A Coruna.", + "tail_text": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.0014, + "head_text": "Pablo Isla, expresidente", + "tail_text": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.0014, + "head_text": "A Coruna.", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "appointed_as", + "score": 0.0012, + "head_text": "Arteixo, A", + "tail_text": "Inditex factura" + }, + { + "label": "appointed_as", + "score": 0.0009, + "head_text": "Madrid el", + "tail_text": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.0008, + "head_text": "Pablo Isla, expresidente", + "tail_text": "A Coruna." + }, + { + "label": "located_in", + "score": 0.0008, + "head_text": "A Coruna.", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "appointed_as", + "score": 0.0006, + "head_text": "Inditex, ha", + "tail_text": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.0004, + "head_text": "Inditex factura", + "tail_text": "Arteixo, A" + } + ], + "natural_long": [ + { + "label": "person works at organization", + "score": 0.0615, + "head_text": "Telefonica. La", + "tail_text": "Inditex, ha" + }, + { + "label": "person appointed as role at organization", + "score": 0.0566, + "head_text": "Telefonica. La", + "tail_text": "Inditex, ha" + }, + { + "label": "person works at organization", + "score": 0.0531, + "head_text": "Inditex, ha", + "tail_text": "Telefonica. La" + }, + { + "label": "person is ceo of organization", + "score": 0.0496, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Inditex, ha" + }, + { + "label": "person works at organization", + "score": 0.0483, + "head_text": "Telefonica. La", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "person works at organization", + "score": 0.0483, + "head_text": "Telefonica. La", + "tail_text": "A Coruna." + }, + { + "label": "person appointed as role at organization", + "score": 0.0477, + "head_text": "Telefonica. La", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "person appointed as role at organization", + "score": 0.0477, + "head_text": "Telefonica. La", + "tail_text": "A Coruna." + }, + { + "label": "person appointed as role at organization", + "score": 0.0463, + "head_text": "Inditex, ha", + "tail_text": "Telefonica. La" + }, + { + "label": "person works at organization", + "score": 0.0423, + "head_text": "Inditex factura", + "tail_text": "Telefonica. La" + }, + { + "label": "person works at organization", + "score": 0.0423, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Telefonica. La" + }, + { + "label": "person works at organization", + "score": 0.0423, + "head_text": "A Coruna.", + "tail_text": "Telefonica. La" + }, + { + "label": "person appointed as role at organization", + "score": 0.0397, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Telefonica. La" + }, + { + "label": "person appointed as role at organization", + "score": 0.0397, + "head_text": "A Coruna.", + "tail_text": "Telefonica. La" + }, + { + "label": "person works at organization", + "score": 0.0395, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Telefonica. La" + }, + { + "label": "person is ceo of organization", + "score": 0.0385, + "head_text": "Telefonica. La", + "tail_text": "Inditex, ha" + }, + { + "label": "person appointed as role at organization", + "score": 0.038, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Telefonica. La" + }, + { + "label": "person is ceo of organization", + "score": 0.0379, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "person is ceo of organization", + "score": 0.0379, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "A Coruna." + }, + { + "label": "person is ceo of organization", + "score": 0.0367, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Inditex, ha" + }, + { + "label": "person is ceo of organization", + "score": 0.0367, + "head_text": "A Coruna.", + "tail_text": "Inditex, ha" + }, + { + "label": "person appointed as role at organization", + "score": 0.0356, + "head_text": "Inditex factura", + "tail_text": "Telefonica. La" + }, + { + "label": "person works at organization", + "score": 0.0352, + "head_text": "Telefonica. La", + "tail_text": "Inditex factura" + }, + { + "label": "person appointed as role at organization", + "score": 0.0348, + "head_text": "Pablo Isla, expresidente", + "tail_text": "A Coruna." + }, + { + "label": "person appointed as role at organization", + "score": 0.0348, + "head_text": "A Coruna.", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "person is ceo of organization", + "score": 0.0342, + "head_text": "Inditex, ha", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "person is ceo of organization", + "score": 0.0342, + "head_text": "Inditex, ha", + "tail_text": "A Coruna." + }, + { + "label": "person works at organization", + "score": 0.034, + "head_text": "Inditex factura", + "tail_text": "Inditex, ha" + }, + { + "label": "person is ceo of organization", + "score": 0.0339, + "head_text": "Pablo Isla, expresidente", + "tail_text": "A Coruna." + }, + { + "label": "person is ceo of organization", + "score": 0.0339, + "head_text": "A Coruna.", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "person appointed as role at organization", + "score": 0.0328, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Inditex, ha" + }, + { + "label": "person appointed as role at organization", + "score": 0.0327, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Inditex, ha" + }, + { + "label": "person appointed as role at organization", + "score": 0.0327, + "head_text": "A Coruna.", + "tail_text": "Inditex, ha" + }, + { + "label": "person appointed as role at organization", + "score": 0.0323, + "head_text": "Inditex, ha", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "person appointed as role at organization", + "score": 0.0323, + "head_text": "Inditex, ha", + "tail_text": "A Coruna." + }, + { + "label": "organization is located in location", + "score": 0.0319, + "head_text": "Inditex factura", + "tail_text": "Inditex, ha" + }, + { + "label": "person appointed as role at organization", + "score": 0.0314, + "head_text": "Telefonica. La", + "tail_text": "Inditex factura" + }, + { + "label": "person is ceo of organization", + "score": 0.0313, + "head_text": "Telefonica. La", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "person is ceo of organization", + "score": 0.0313, + "head_text": "Telefonica. La", + "tail_text": "A Coruna." + }, + { + "label": "person appointed as role at organization", + "score": 0.0309, + "head_text": "Inditex factura", + "tail_text": "Inditex, ha" + }, + { + "label": "person appointed as role at organization", + "score": 0.0304, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "person appointed as role at organization", + "score": 0.0304, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "A Coruna." + }, + { + "label": "person works at organization", + "score": 0.0303, + "head_text": "Pablo Isla, expresidente", + "tail_text": "A Coruna." + }, + { + "label": "person works at organization", + "score": 0.0303, + "head_text": "A Coruna.", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "person is ceo of organization", + "score": 0.0303, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Inditex factura" + }, + { + "label": "person appointed as role at organization", + "score": 0.0302, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Inditex factura" + }, + { + "label": "person works at organization", + "score": 0.0298, + "head_text": "Inditex, ha", + "tail_text": "Inditex factura" + }, + { + "label": "person works at organization", + "score": 0.0294, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Inditex factura" + }, + { + "label": "person appointed as role at organization", + "score": 0.0294, + "head_text": "Inditex factura", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "person appointed as role at organization", + "score": 0.0294, + "head_text": "Inditex factura", + "tail_text": "A Coruna." + }, + { + "label": "person works at organization", + "score": 0.0293, + "head_text": "Inditex, ha", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "person works at organization", + "score": 0.0293, + "head_text": "Inditex, ha", + "tail_text": "A Coruna." + }, + { + "label": "person works at organization", + "score": 0.0291, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Inditex, ha" + }, + { + "label": "person works at organization", + "score": 0.0291, + "head_text": "A Coruna.", + "tail_text": "Inditex, ha" + }, + { + "label": "person works at organization", + "score": 0.0291, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Inditex, ha" + }, + { + "label": "person is ceo of organization", + "score": 0.0289, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Telefonica. La" + }, + { + "label": "person works at organization", + "score": 0.0288, + "head_text": "Inditex factura", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "person works at organization", + "score": 0.0288, + "head_text": "Inditex factura", + "tail_text": "A Coruna." + }, + { + "label": "organization headquartered in location", + "score": 0.0284, + "head_text": "Inditex factura", + "tail_text": "Inditex, ha" + }, + { + "label": "organization is located in location", + "score": 0.0279, + "head_text": "Inditex, ha", + "tail_text": "Inditex factura" + }, + { + "label": "person appointed as role at organization", + "score": 0.027, + "head_text": "Inditex, ha", + "tail_text": "Inditex factura" + }, + { + "label": "person is ceo of organization", + "score": 0.0268, + "head_text": "Inditex, ha", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "person works at organization", + "score": 0.026, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "person works at organization", + "score": 0.026, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "A Coruna." + }, + { + "label": "person is ceo of organization", + "score": 0.0258, + "head_text": "Inditex, ha", + "tail_text": "Telefonica. La" + }, + { + "label": "organization headquartered in location", + "score": 0.0251, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Inditex factura" + }, + { + "label": "person appointed as role at organization", + "score": 0.0244, + "head_text": "Madrid el", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "person appointed as role at organization", + "score": 0.0244, + "head_text": "Madrid el", + "tail_text": "A Coruna." + }, + { + "label": "person is ceo of organization", + "score": 0.0243, + "head_text": "Inditex factura", + "tail_text": "Inditex, ha" + }, + { + "label": "person is ceo of organization", + "score": 0.024, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "person is ceo of organization", + "score": 0.024, + "head_text": "A Coruna.", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "organization headquartered in location", + "score": 0.0232, + "head_text": "Inditex, ha", + "tail_text": "Inditex factura" + }, + { + "label": "organization headquartered in location", + "score": 0.0228, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Inditex, ha" + }, + { + "label": "organization is located in location", + "score": 0.0225, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Inditex factura" + }, + { + "label": "person appointed as role at organization", + "score": 0.0223, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Inditex factura" + }, + { + "label": "person appointed as role at organization", + "score": 0.0223, + "head_text": "A Coruna.", + "tail_text": "Inditex factura" + }, + { + "label": "person appointed as role at organization", + "score": 0.022, + "head_text": "Telefonica. La", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "person is ceo of organization", + "score": 0.0219, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Telefonica. La" + }, + { + "label": "person is ceo of organization", + "score": 0.0219, + "head_text": "A Coruna.", + "tail_text": "Telefonica. La" + }, + { + "label": "person works at organization", + "score": 0.0219, + "head_text": "Madrid el", + "tail_text": "Telefonica. La" + }, + { + "label": "person works at organization", + "score": 0.0216, + "head_text": "Telefonica. La", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "person works at organization", + "score": 0.0215, + "head_text": "Madrid el", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "person works at organization", + "score": 0.0215, + "head_text": "Madrid el", + "tail_text": "A Coruna." + }, + { + "label": "person works at organization", + "score": 0.0213, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Inditex factura" + }, + { + "label": "person works at organization", + "score": 0.0213, + "head_text": "A Coruna.", + "tail_text": "Inditex factura" + }, + { + "label": "person appointed as role at organization", + "score": 0.0212, + "head_text": "Madrid el", + "tail_text": "Telefonica. La" + }, + { + "label": "person is ceo of organization", + "score": 0.0211, + "head_text": "Inditex factura", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "person is ceo of organization", + "score": 0.0211, + "head_text": "Inditex factura", + "tail_text": "A Coruna." + }, + { + "label": "person is ceo of organization", + "score": 0.0205, + "head_text": "Inditex, ha", + "tail_text": "Inditex factura" + }, + { + "label": "person is ceo of organization", + "score": 0.02, + "head_text": "Telefonica. La", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "person is ceo of organization", + "score": 0.0199, + "head_text": "Inditex factura", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "person appointed as role at organization", + "score": 0.0195, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "person appointed as role at organization", + "score": 0.0195, + "head_text": "A Coruna.", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "organization is located in location", + "score": 0.0195, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Inditex, ha" + }, + { + "label": "person appointed as role at organization", + "score": 0.0194, + "head_text": "Inditex factura", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "person appointed as role at organization", + "score": 0.0194, + "head_text": "Inditex, ha", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "person works at organization", + "score": 0.0188, + "head_text": "Inditex factura", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "organization headquartered in location", + "score": 0.0183, + "head_text": "Madrid el", + "tail_text": "Inditex, ha" + }, + { + "label": "person is ceo of organization", + "score": 0.0178, + "head_text": "Madrid el", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "person is ceo of organization", + "score": 0.0178, + "head_text": "Madrid el", + "tail_text": "A Coruna." + }, + { + "label": "person works at organization", + "score": 0.0176, + "head_text": "Inditex, ha", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "person is ceo of organization", + "score": 0.017, + "head_text": "Telefonica. La", + "tail_text": "Inditex factura" + }, + { + "label": "organization headquartered in location", + "score": 0.0167, + "head_text": "Arteixo, A", + "tail_text": "Inditex, ha" + }, + { + "label": "person is ceo of organization", + "score": 0.0166, + "head_text": "Inditex factura", + "tail_text": "Telefonica. La" + }, + { + "label": "person works at organization", + "score": 0.0166, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "person works at organization", + "score": 0.0166, + "head_text": "A Coruna.", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "organization headquartered in location", + "score": 0.0163, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Arteixo, A" + }, + { + "label": "person appointed as role at organization", + "score": 0.0163, + "head_text": "Madrid el", + "tail_text": "Inditex, ha" + }, + { + "label": "person is ceo of organization", + "score": 0.016, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Inditex factura" + }, + { + "label": "person is ceo of organization", + "score": 0.016, + "head_text": "A Coruna.", + "tail_text": "Inditex factura" + }, + { + "label": "organization is located in location", + "score": 0.0159, + "head_text": "Arteixo, A", + "tail_text": "Inditex, ha" + }, + { + "label": "organization headquartered in location", + "score": 0.0158, + "head_text": "Arteixo, A", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "organization headquartered in location", + "score": 0.0158, + "head_text": "Arteixo, A", + "tail_text": "A Coruna." + }, + { + "label": "person works at organization", + "score": 0.0154, + "head_text": "Madrid el", + "tail_text": "Inditex, ha" + }, + { + "label": "organization is located in location", + "score": 0.0151, + "head_text": "Arteixo, A", + "tail_text": "Inditex factura" + }, + { + "label": "organization is located in location", + "score": 0.015, + "head_text": "Madrid el", + "tail_text": "Inditex, ha" + }, + { + "label": "organization headquartered in location", + "score": 0.0146, + "head_text": "Inditex factura", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "organization headquartered in location", + "score": 0.0139, + "head_text": "Inditex, ha", + "tail_text": "Arteixo, A" + }, + { + "label": "person is ceo of organization", + "score": 0.0138, + "head_text": "Madrid el", + "tail_text": "Inditex, ha" + }, + { + "label": "organization headquartered in location", + "score": 0.0136, + "head_text": "Arteixo, A", + "tail_text": "Inditex factura" + }, + { + "label": "organization headquartered in location", + "score": 0.0133, + "head_text": "Telefonica. La", + "tail_text": "Inditex, ha" + }, + { + "label": "organization headquartered in location", + "score": 0.013, + "head_text": "Arteixo, A", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "organization headquartered in location", + "score": 0.013, + "head_text": "Inditex factura", + "tail_text": "Arteixo, A" + }, + { + "label": "organization is located in location", + "score": 0.0126, + "head_text": "Arteixo, A", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "organization is located in location", + "score": 0.0126, + "head_text": "Arteixo, A", + "tail_text": "A Coruna." + }, + { + "label": "organization headquartered in location", + "score": 0.0124, + "head_text": "Arteixo, A", + "tail_text": "Telefonica. La" + }, + { + "label": "organization is located in location", + "score": 0.0121, + "head_text": "Inditex factura", + "tail_text": "Arteixo, A" + }, + { + "label": "organization is located in location", + "score": 0.0118, + "head_text": "Inditex, ha", + "tail_text": "Arteixo, A" + }, + { + "label": "organization is located in location", + "score": 0.0115, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Arteixo, A" + }, + { + "label": "organization headquartered in location", + "score": 0.0113, + "head_text": "Inditex, ha", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "organization headquartered in location", + "score": 0.0113, + "head_text": "Telefonica. La", + "tail_text": "Arteixo, A" + }, + { + "label": "organization is located in location", + "score": 0.0112, + "head_text": "Telefonica. La", + "tail_text": "Inditex, ha" + }, + { + "label": "organization headquartered in location", + "score": 0.011, + "head_text": "Telefonica. La", + "tail_text": "Inditex factura" + }, + { + "label": "person is ceo of organization", + "score": 0.0108, + "head_text": "Madrid el", + "tail_text": "Telefonica. La" + }, + { + "label": "organization headquartered in location", + "score": 0.0108, + "head_text": "Madrid el", + "tail_text": "Arteixo, A" + }, + { + "label": "organization is located in location", + "score": 0.0105, + "head_text": "Inditex factura", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "organization headquartered in location", + "score": 0.0104, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Arteixo, A" + }, + { + "label": "organization headquartered in location", + "score": 0.0104, + "head_text": "A Coruna.", + "tail_text": "Arteixo, A" + }, + { + "label": "organization is located in location", + "score": 0.0099, + "head_text": "Telefonica. La", + "tail_text": "Inditex factura" + }, + { + "label": "organization headquartered in location", + "score": 0.0098, + "head_text": "Madrid el", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "organization headquartered in location", + "score": 0.0098, + "head_text": "Madrid el", + "tail_text": "A Coruna." + }, + { + "label": "person works at organization", + "score": 0.0095, + "head_text": "Arteixo, A", + "tail_text": "Telefonica. La" + }, + { + "label": "organization headquartered in location", + "score": 0.0094, + "head_text": "Inditex factura", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "organization headquartered in location", + "score": 0.0094, + "head_text": "Inditex factura", + "tail_text": "A Coruna." + }, + { + "label": "organization is located in location", + "score": 0.0093, + "head_text": "Arteixo, A", + "tail_text": "Telefonica. La" + }, + { + "label": "organization is located in location", + "score": 0.0092, + "head_text": "Arteixo, A", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "organization is located in location", + "score": 0.009, + "head_text": "Inditex factura", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "organization is located in location", + "score": 0.009, + "head_text": "Inditex factura", + "tail_text": "A Coruna." + }, + { + "label": "organization headquartered in location", + "score": 0.0088, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Telefonica. La" + }, + { + "label": "organization is located in location", + "score": 0.0087, + "head_text": "Madrid el", + "tail_text": "Arteixo, A" + }, + { + "label": "organization headquartered in location", + "score": 0.0086, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "organization headquartered in location", + "score": 0.0086, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "A Coruna." + }, + { + "label": "organization headquartered in location", + "score": 0.0086, + "head_text": "Inditex factura", + "tail_text": "Telefonica. La" + }, + { + "label": "organization is located in location", + "score": 0.0085, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Inditex, ha" + }, + { + "label": "organization is located in location", + "score": 0.0085, + "head_text": "A Coruna.", + "tail_text": "Inditex, ha" + }, + { + "label": "organization headquartered in location", + "score": 0.0083, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Inditex, ha" + }, + { + "label": "organization headquartered in location", + "score": 0.0083, + "head_text": "A Coruna.", + "tail_text": "Inditex, ha" + }, + { + "label": "organization is located in location", + "score": 0.0082, + "head_text": "Inditex, ha", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "organization is located in location", + "score": 0.0079, + "head_text": "Inditex, ha", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "organization is located in location", + "score": 0.0079, + "head_text": "Inditex, ha", + "tail_text": "A Coruna." + }, + { + "label": "organization is located in location", + "score": 0.0079, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Inditex factura" + }, + { + "label": "organization is located in location", + "score": 0.0079, + "head_text": "A Coruna.", + "tail_text": "Inditex factura" + }, + { + "label": "organization headquartered in location", + "score": 0.0079, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Inditex factura" + }, + { + "label": "organization headquartered in location", + "score": 0.0079, + "head_text": "A Coruna.", + "tail_text": "Inditex factura" + }, + { + "label": "organization headquartered in location", + "score": 0.0077, + "head_text": "Inditex, ha", + "tail_text": "Telefonica. La" + }, + { + "label": "organization headquartered in location", + "score": 0.0076, + "head_text": "Inditex, ha", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "organization headquartered in location", + "score": 0.0076, + "head_text": "Inditex, ha", + "tail_text": "A Coruna." + }, + { + "label": "organization headquartered in location", + "score": 0.0075, + "head_text": "Arteixo, A", + "tail_text": "Madrid el" + }, + { + "label": "organization is located in location", + "score": 0.0075, + "head_text": "Madrid el", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "organization is located in location", + "score": 0.0075, + "head_text": "Madrid el", + "tail_text": "A Coruna." + }, + { + "label": "person appointed as role at organization", + "score": 0.0074, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Madrid el" + }, + { + "label": "person appointed as role at organization", + "score": 0.0074, + "head_text": "A Coruna.", + "tail_text": "Madrid el" + }, + { + "label": "organization headquartered in location", + "score": 0.0074, + "head_text": "Madrid el", + "tail_text": "Inditex factura" + }, + { + "label": "organization headquartered in location", + "score": 0.0073, + "head_text": "Inditex, ha", + "tail_text": "Madrid el" + }, + { + "label": "organization is located in location", + "score": 0.0073, + "head_text": "Telefonica. La", + "tail_text": "Arteixo, A" + }, + { + "label": "organization headquartered in location", + "score": 0.0071, + "head_text": "Telefonica. La", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "person is ceo of organization", + "score": 0.0071, + "head_text": "Madrid el", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "organization is located in location", + "score": 0.0071, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "organization is located in location", + "score": 0.0071, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "A Coruna." + }, + { + "label": "organization is located in location", + "score": 0.0071, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Arteixo, A" + }, + { + "label": "organization is located in location", + "score": 0.0071, + "head_text": "A Coruna.", + "tail_text": "Arteixo, A" + }, + { + "label": "organization is located in location", + "score": 0.007, + "head_text": "Inditex factura", + "tail_text": "Telefonica. La" + }, + { + "label": "organization headquartered in location", + "score": 0.0069, + "head_text": "Madrid el", + "tail_text": "Telefonica. La" + }, + { + "label": "person appointed as role at organization", + "score": 0.0069, + "head_text": "Arteixo, A", + "tail_text": "Telefonica. La" + }, + { + "label": "organization is located in location", + "score": 0.0069, + "head_text": "Arteixo, A", + "tail_text": "Madrid el" + }, + { + "label": "person appointed as role at organization", + "score": 0.0067, + "head_text": "Telefonica. La", + "tail_text": "Madrid el" + }, + { + "label": "organization is located in location", + "score": 0.0065, + "head_text": "Madrid el", + "tail_text": "Inditex factura" + }, + { + "label": "organization is located in location", + "score": 0.0064, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Telefonica. La" + }, + { + "label": "organization headquartered in location", + "score": 0.0063, + "head_text": "Madrid el", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "person appointed as role at organization", + "score": 0.0063, + "head_text": "Madrid el", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "organization is located in location", + "score": 0.0063, + "head_text": "Inditex, ha", + "tail_text": "Telefonica. La" + }, + { + "label": "person works at organization", + "score": 0.0063, + "head_text": "Telefonica. La", + "tail_text": "Madrid el" + }, + { + "label": "person works at organization", + "score": 0.0061, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Madrid el" + }, + { + "label": "person works at organization", + "score": 0.0061, + "head_text": "A Coruna.", + "tail_text": "Madrid el" + }, + { + "label": "person appointed as role at organization", + "score": 0.006, + "head_text": "Inditex, ha", + "tail_text": "Madrid el" + }, + { + "label": "organization is located in location", + "score": 0.0058, + "head_text": "Inditex, ha", + "tail_text": "Madrid el" + }, + { + "label": "organization headquartered in location", + "score": 0.0056, + "head_text": "Telefonica. La", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "organization headquartered in location", + "score": 0.0056, + "head_text": "Telefonica. La", + "tail_text": "A Coruna." + }, + { + "label": "person works at organization", + "score": 0.0056, + "head_text": "Madrid el", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "person is ceo of organization", + "score": 0.0055, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Madrid el" + }, + { + "label": "person is ceo of organization", + "score": 0.0055, + "head_text": "A Coruna.", + "tail_text": "Madrid el" + }, + { + "label": "person works at organization", + "score": 0.0055, + "head_text": "Inditex, ha", + "tail_text": "Madrid el" + }, + { + "label": "organization headquartered in location", + "score": 0.0055, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "organization headquartered in location", + "score": 0.0055, + "head_text": "A Coruna.", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "person works at organization", + "score": 0.0052, + "head_text": "Arteixo, A", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "person works at organization", + "score": 0.0052, + "head_text": "Arteixo, A", + "tail_text": "A Coruna." + }, + { + "label": "person is ceo of organization", + "score": 0.005, + "head_text": "Inditex, ha", + "tail_text": "Madrid el" + }, + { + "label": "person works at organization", + "score": 0.0049, + "head_text": "Madrid el", + "tail_text": "Inditex factura" + }, + { + "label": "organization headquartered in location", + "score": 0.0049, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Madrid el" + }, + { + "label": "organization is located in location", + "score": 0.0048, + "head_text": "Madrid el", + "tail_text": "Telefonica. La" + }, + { + "label": "organization is located in location", + "score": 0.0046, + "head_text": "Telefonica. La", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "organization is located in location", + "score": 0.0046, + "head_text": "Telefonica. La", + "tail_text": "A Coruna." + }, + { + "label": "person appointed as role at organization", + "score": 0.0045, + "head_text": "Madrid el", + "tail_text": "Inditex factura" + }, + { + "label": "organization is located in location", + "score": 0.0044, + "head_text": "Telefonica. La", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "person is ceo of organization", + "score": 0.0043, + "head_text": "Arteixo, A", + "tail_text": "Telefonica. La" + }, + { + "label": "organization headquartered in location", + "score": 0.0043, + "head_text": "Inditex factura", + "tail_text": "Madrid el" + }, + { + "label": "person is ceo of organization", + "score": 0.0042, + "head_text": "Telefonica. La", + "tail_text": "Madrid el" + }, + { + "label": "organization is located in location", + "score": 0.0042, + "head_text": "Madrid el", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "organization headquartered in location", + "score": 0.0042, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Madrid el" + }, + { + "label": "organization headquartered in location", + "score": 0.0042, + "head_text": "A Coruna.", + "tail_text": "Madrid el" + }, + { + "label": "person appointed as role at organization", + "score": 0.0041, + "head_text": "Arteixo, A", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "person appointed as role at organization", + "score": 0.0041, + "head_text": "Arteixo, A", + "tail_text": "A Coruna." + }, + { + "label": "person is ceo of organization", + "score": 0.0041, + "head_text": "Arteixo, A", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "person is ceo of organization", + "score": 0.0041, + "head_text": "Arteixo, A", + "tail_text": "A Coruna." + }, + { + "label": "organization headquartered in location", + "score": 0.0041, + "head_text": "Telefonica. La", + "tail_text": "Madrid el" + }, + { + "label": "person is ceo of organization", + "score": 0.004, + "head_text": "Arteixo, A", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "person is ceo of organization", + "score": 0.0039, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Madrid el" + }, + { + "label": "organization is located in location", + "score": 0.0038, + "head_text": "Pablo Isla, expresidente", + "tail_text": "A Coruna." + }, + { + "label": "organization is located in location", + "score": 0.0038, + "head_text": "A Coruna.", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "organization is located in location", + "score": 0.0037, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "organization is located in location", + "score": 0.0037, + "head_text": "A Coruna.", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "organization headquartered in location", + "score": 0.0037, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Telefonica. La" + }, + { + "label": "organization headquartered in location", + "score": 0.0037, + "head_text": "A Coruna.", + "tail_text": "Telefonica. La" + }, + { + "label": "organization headquartered in location", + "score": 0.0036, + "head_text": "Pablo Isla, expresidente", + "tail_text": "A Coruna." + }, + { + "label": "organization headquartered in location", + "score": 0.0036, + "head_text": "A Coruna.", + "tail_text": "Pablo Isla, expresidente" + }, + { + "label": "person works at organization", + "score": 0.0036, + "head_text": "Arteixo, A", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "organization is located in location", + "score": 0.0036, + "head_text": "Inditex factura", + "tail_text": "Madrid el" + }, + { + "label": "organization is located in location", + "score": 0.0035, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Madrid el" + }, + { + "label": "person appointed as role at organization", + "score": 0.0035, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Madrid el" + }, + { + "label": "person appointed as role at organization", + "score": 0.0032, + "head_text": "Arteixo, A", + "tail_text": "Jose Maria Alvarez-Pallete en" + }, + { + "label": "person works at organization", + "score": 0.003, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Madrid el" + }, + { + "label": "person is ceo of organization", + "score": 0.003, + "head_text": "Madrid el", + "tail_text": "Inditex factura" + }, + { + "label": "organization is located in location", + "score": 0.0029, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Madrid el" + }, + { + "label": "organization is located in location", + "score": 0.0029, + "head_text": "A Coruna.", + "tail_text": "Madrid el" + }, + { + "label": "organization is located in location", + "score": 0.0028, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Telefonica. La" + }, + { + "label": "organization is located in location", + "score": 0.0028, + "head_text": "A Coruna.", + "tail_text": "Telefonica. La" + }, + { + "label": "organization is located in location", + "score": 0.0028, + "head_text": "Telefonica. La", + "tail_text": "Madrid el" + }, + { + "label": "person works at organization", + "score": 0.0024, + "head_text": "Inditex factura", + "tail_text": "Madrid el" + }, + { + "label": "person appointed as role at organization", + "score": 0.0024, + "head_text": "Inditex factura", + "tail_text": "Madrid el" + }, + { + "label": "person works at organization", + "score": 0.0023, + "head_text": "Arteixo, A", + "tail_text": "Inditex factura" + }, + { + "label": "person works at organization", + "score": 0.0022, + "head_text": "Telefonica. La", + "tail_text": "Arteixo, A" + }, + { + "label": "person is ceo of organization", + "score": 0.0022, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Arteixo, A" + }, + { + "label": "person works at organization", + "score": 0.002, + "head_text": "Arteixo, A", + "tail_text": "Inditex, ha" + }, + { + "label": "person works at organization", + "score": 0.0017, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Arteixo, A" + }, + { + "label": "person is ceo of organization", + "score": 0.0017, + "head_text": "Arteixo, A", + "tail_text": "Inditex, ha" + }, + { + "label": "person is ceo of organization", + "score": 0.0017, + "head_text": "Inditex factura", + "tail_text": "Madrid el" + }, + { + "label": "person appointed as role at organization", + "score": 0.0016, + "head_text": "Arteixo, A", + "tail_text": "Inditex factura" + }, + { + "label": "person is ceo of organization", + "score": 0.0016, + "head_text": "Telefonica. La", + "tail_text": "Arteixo, A" + }, + { + "label": "person appointed as role at organization", + "score": 0.0015, + "head_text": "Telefonica. La", + "tail_text": "Arteixo, A" + }, + { + "label": "person appointed as role at organization", + "score": 0.0015, + "head_text": "Arteixo, A", + "tail_text": "Inditex, ha" + }, + { + "label": "person works at organization", + "score": 0.0014, + "head_text": "Arteixo, A", + "tail_text": "Madrid el" + }, + { + "label": "person works at organization", + "score": 0.0014, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Arteixo, A" + }, + { + "label": "person works at organization", + "score": 0.0014, + "head_text": "A Coruna.", + "tail_text": "Arteixo, A" + }, + { + "label": "person is ceo of organization", + "score": 0.0014, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Arteixo, A" + }, + { + "label": "person is ceo of organization", + "score": 0.0014, + "head_text": "A Coruna.", + "tail_text": "Arteixo, A" + }, + { + "label": "person appointed as role at organization", + "score": 0.0013, + "head_text": "Jose Maria Alvarez-Pallete en", + "tail_text": "Arteixo, A" + }, + { + "label": "person is ceo of organization", + "score": 0.0013, + "head_text": "Arteixo, A", + "tail_text": "Inditex factura" + }, + { + "label": "person appointed as role at organization", + "score": 0.0012, + "head_text": "Arteixo, A", + "tail_text": "Madrid el" + }, + { + "label": "person works at organization", + "score": 0.0012, + "head_text": "Inditex factura", + "tail_text": "Arteixo, A" + }, + { + "label": "person appointed as role at organization", + "score": 0.0011, + "head_text": "Pablo Isla, expresidente", + "tail_text": "Arteixo, A" + }, + { + "label": "person appointed as role at organization", + "score": 0.0011, + "head_text": "A Coruna.", + "tail_text": "Arteixo, A" + }, + { + "label": "person works at organization", + "score": 0.0011, + "head_text": "Inditex, ha", + "tail_text": "Arteixo, A" + }, + { + "label": "person is ceo of organization", + "score": 0.0011, + "head_text": "Arteixo, A", + "tail_text": "Madrid el" + }, + { + "label": "person works at organization", + "score": 0.001, + "head_text": "Madrid el", + "tail_text": "Arteixo, A" + }, + { + "label": "person is ceo of organization", + "score": 0.001, + "head_text": "Inditex, ha", + "tail_text": "Arteixo, A" + }, + { + "label": "person is ceo of organization", + "score": 0.0009, + "head_text": "Madrid el", + "tail_text": "Arteixo, A" + }, + { + "label": "person is ceo of organization", + "score": 0.0008, + "head_text": "Inditex factura", + "tail_text": "Arteixo, A" + }, + { + "label": "person appointed as role at organization", + "score": 0.0008, + "head_text": "Inditex factura", + "tail_text": "Arteixo, A" + }, + { + "label": "person appointed as role at organization", + "score": 0.0008, + "head_text": "Madrid el", + "tail_text": "Arteixo, A" + }, + { + "label": "person appointed as role at organization", + "score": 0.0007, + "head_text": "Inditex, ha", + "tail_text": "Arteixo, A" + } + ] + } + }, + "en_corporate": { + "entities": [ + [ + "Pablo Isla", + "person", + 0.989 + ], + [ + "Inditex", + "organization", + 0.992 + ], + [ + "Telefonica", + "organization", + 0.993 + ], + [ + "Jose Maria Alvarez-Pallete", + "person", + 0.976 + ], + [ + "Telefonica", + "organization", + 0.991 + ], + [ + "Madrid", + "location", + 0.966 + ], + [ + "Inditex", + "organization", + 0.995 + ], + [ + "Arteixo", + "location", + 0.969 + ], + [ + "A Coruna", + "location", + 0.92 + ] + ], + "ner": [ + [ + 0, + 2, + "person" + ], + [ + 6, + 7, + "organization" + ], + [ + 14, + 15, + "organization" + ], + [ + 20, + 23, + "person" + ], + [ + 26, + 27, + "organization" + ], + [ + 28, + 29, + "location" + ], + [ + 31, + 32, + "organization" + ], + [ + 36, + 37, + "location" + ], + [ + 37, + 39, + "location" + ] + ], + "styles": { + "snake_short": [ + { + "label": "appointed_as", + "score": 0.2328, + "head_text": "Madrid last", + "tail_text": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.2328, + "head_text": "Madrid last", + "tail_text": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.2221, + "head_text": "Madrid last", + "tail_text": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.2096, + "head_text": "Telefonica, in", + "tail_text": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.2096, + "head_text": "Telefonica, in", + "tail_text": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.2042, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Inditex, has" + }, + { + "label": "headquartered_in", + "score": 0.1994, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.1959, + "head_text": "Arteixo, A", + "tail_text": "Madrid last" + }, + { + "label": "appointed_as", + "score": 0.1911, + "head_text": "Telefonica, in", + "tail_text": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.1825, + "head_text": "Telefonica. The", + "tail_text": "Inditex, has" + }, + { + "label": "headquartered_in", + "score": 0.1767, + "head_text": "Inditex has", + "tail_text": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.1757, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.1757, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.1738, + "head_text": "Inditex, has", + "tail_text": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.1715, + "head_text": "Arteixo, A", + "tail_text": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.1705, + "head_text": "Pablo Isla, the", + "tail_text": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.1705, + "head_text": "A Coruna.", + "tail_text": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.1678, + "head_text": "Telefonica. The", + "tail_text": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.1678, + "head_text": "Telefonica. The", + "tail_text": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.1642, + "head_text": "Telefonica, in", + "tail_text": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.162, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.1617, + "head_text": "Inditex has", + "tail_text": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.1614, + "head_text": "Arteixo, A", + "tail_text": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.1599, + "head_text": "Madrid last", + "tail_text": "Inditex, has" + }, + { + "label": "located_in", + "score": 0.1555, + "head_text": "Madrid last", + "tail_text": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.155, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.153, + "head_text": "Pablo Isla, the", + "tail_text": "Telefonica. The" + }, + { + "label": "appointed_as", + "score": 0.153, + "head_text": "A Coruna.", + "tail_text": "Telefonica. The" + }, + { + "label": "appointed_as", + "score": 0.1488, + "head_text": "Inditex, has", + "tail_text": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.1487, + "head_text": "Inditex, has", + "tail_text": "Telefonica. The" + }, + { + "label": "headquartered_in", + "score": 0.1464, + "head_text": "Arteixo, A", + "tail_text": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.1451, + "head_text": "Telefonica. The", + "tail_text": "Inditex has" + }, + { + "label": "located_in", + "score": 0.1436, + "head_text": "Telefonica, in", + "tail_text": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.142, + "head_text": "Telefonica. The", + "tail_text": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.1415, + "head_text": "Inditex, has", + "tail_text": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.1413, + "head_text": "Inditex has", + "tail_text": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.1413, + "head_text": "Inditex has", + "tail_text": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.1403, + "head_text": "Arteixo, A", + "tail_text": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.1399, + "head_text": "Inditex has", + "tail_text": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.1398, + "head_text": "Arteixo, A", + "tail_text": "Inditex, has" + }, + { + "label": "located_in", + "score": 0.1371, + "head_text": "Arteixo, A", + "tail_text": "Madrid last" + }, + { + "label": "headquartered_in", + "score": 0.1364, + "head_text": "Pablo Isla, the", + "tail_text": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.1364, + "head_text": "A Coruna.", + "tail_text": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.1321, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.1304, + "head_text": "Arteixo, A", + "tail_text": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.1292, + "head_text": "Inditex, has", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.1292, + "head_text": "Pablo Isla, the", + "tail_text": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.1292, + "head_text": "A Coruna.", + "tail_text": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.1284, + "head_text": "Pablo Isla, the", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.1284, + "head_text": "A Coruna.", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "located_in", + "score": 0.1254, + "head_text": "Telefonica. The", + "tail_text": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.1249, + "head_text": "Madrid last", + "tail_text": "Telefonica. The" + }, + { + "label": "appointed_as", + "score": 0.1213, + "head_text": "Pablo Isla, the", + "tail_text": "Madrid last" + }, + { + "label": "appointed_as", + "score": 0.1213, + "head_text": "A Coruna.", + "tail_text": "Madrid last" + }, + { + "label": "appointed_as", + "score": 0.1206, + "head_text": "Pablo Isla, the", + "tail_text": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.1206, + "head_text": "A Coruna.", + "tail_text": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.1196, + "head_text": "Telefonica, in", + "tail_text": "Telefonica. The" + }, + { + "label": "appointed_as", + "score": 0.1167, + "head_text": "Inditex, has", + "tail_text": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.1167, + "head_text": "Inditex, has", + "tail_text": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.1145, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.1111, + "head_text": "Inditex has", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.1096, + "head_text": "Inditex has", + "tail_text": "Inditex, has" + }, + { + "label": "headquartered_in", + "score": 0.1089, + "head_text": "Arteixo, A", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.1042, + "head_text": "Telefonica. The", + "tail_text": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.1036, + "head_text": "Pablo Isla, the", + "tail_text": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.1036, + "head_text": "A Coruna.", + "tail_text": "Arteixo, A" + }, + { + "label": "ceo_of", + "score": 0.0979, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Inditex has" + }, + { + "label": "located_in", + "score": 0.0974, + "head_text": "Arteixo, A", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "headquartered_in", + "score": 0.0951, + "head_text": "Arteixo, A", + "tail_text": "Telefonica. The" + }, + { + "label": "headquartered_in", + "score": 0.0931, + "head_text": "Arteixo, A", + "tail_text": "Pablo Isla, the" + }, + { + "label": "headquartered_in", + "score": 0.0931, + "head_text": "Arteixo, A", + "tail_text": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.0918, + "head_text": "Inditex, has", + "tail_text": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.0911, + "head_text": "Telefonica, in", + "tail_text": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0899, + "head_text": "Telefonica. The", + "tail_text": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.0896, + "head_text": "Inditex has", + "tail_text": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.0887, + "head_text": "Pablo Isla, the", + "tail_text": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.0887, + "head_text": "A Coruna.", + "tail_text": "Pablo Isla, the" + }, + { + "label": "located_in", + "score": 0.0867, + "head_text": "Arteixo, A", + "tail_text": "Pablo Isla, the" + }, + { + "label": "located_in", + "score": 0.0867, + "head_text": "Arteixo, A", + "tail_text": "A Coruna." + }, + { + "label": "located_in", + "score": 0.0866, + "head_text": "Arteixo, A", + "tail_text": "Telefonica. The" + }, + { + "label": "appointed_as", + "score": 0.0853, + "head_text": "Telefonica. The", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.0806, + "head_text": "Telefonica, in", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.0801, + "head_text": "Inditex, has", + "tail_text": "Madrid last" + }, + { + "label": "appointed_as", + "score": 0.073, + "head_text": "Madrid last", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "ceo_of", + "score": 0.0718, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0708, + "head_text": "Telefonica, in", + "tail_text": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0696, + "head_text": "Pablo Isla, the", + "tail_text": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0696, + "head_text": "A Coruna.", + "tail_text": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.0682, + "head_text": "Madrid last", + "tail_text": "Telefonica, in" + }, + { + "label": "ceo_of", + "score": 0.0649, + "head_text": "Telefonica, in", + "tail_text": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.0644, + "head_text": "Telefonica. The", + "tail_text": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.0607, + "head_text": "Inditex, has", + "tail_text": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.0602, + "head_text": "Madrid last", + "tail_text": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0577, + "head_text": "Pablo Isla, the", + "tail_text": "Telefonica, in" + }, + { + "label": "ceo_of", + "score": 0.0577, + "head_text": "A Coruna.", + "tail_text": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.0571, + "head_text": "Telefonica, in", + "tail_text": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0556, + "head_text": "Inditex, has", + "tail_text": "Telefonica, in" + }, + { + "label": "ceo_of", + "score": 0.055, + "head_text": "Telefonica, in", + "tail_text": "Pablo Isla, the" + }, + { + "label": "ceo_of", + "score": 0.055, + "head_text": "Telefonica, in", + "tail_text": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0542, + "head_text": "Telefonica. The", + "tail_text": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.0532, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.0524, + "head_text": "Inditex has", + "tail_text": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.0515, + "head_text": "Madrid last", + "tail_text": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.0482, + "head_text": "Telefonica, in", + "tail_text": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0479, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Pablo Isla, the" + }, + { + "label": "ceo_of", + "score": 0.0479, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0473, + "head_text": "Inditex has", + "tail_text": "Telefonica. The" + }, + { + "label": "ceo_of", + "score": 0.0467, + "head_text": "Inditex has", + "tail_text": "Pablo Isla, the" + }, + { + "label": "ceo_of", + "score": 0.0467, + "head_text": "Inditex has", + "tail_text": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0455, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Telefonica, in" + }, + { + "label": "ceo_of", + "score": 0.0427, + "head_text": "Inditex has", + "tail_text": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.0425, + "head_text": "Madrid last", + "tail_text": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0419, + "head_text": "Inditex has", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "located_in", + "score": 0.0412, + "head_text": "Inditex has", + "tail_text": "Telefonica, in" + }, + { + "label": "headquartered_in", + "score": 0.0401, + "head_text": "Inditex has", + "tail_text": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.04, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Telefonica. The" + }, + { + "label": "headquartered_in", + "score": 0.0396, + "head_text": "Madrid last", + "tail_text": "Inditex, has" + }, + { + "label": "located_in", + "score": 0.0394, + "head_text": "Inditex, has", + "tail_text": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.0387, + "head_text": "Arteixo, A", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "ceo_of", + "score": 0.0382, + "head_text": "Inditex, has", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "ceo_of", + "score": 0.0378, + "head_text": "Inditex, has", + "tail_text": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.0374, + "head_text": "Inditex has", + "tail_text": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0372, + "head_text": "Telefonica. The", + "tail_text": "Telefonica, in" + }, + { + "label": "ceo_of", + "score": 0.0363, + "head_text": "Telefonica. The", + "tail_text": "Pablo Isla, the" + }, + { + "label": "ceo_of", + "score": 0.0363, + "head_text": "Telefonica. The", + "tail_text": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.0362, + "head_text": "Telefonica, in", + "tail_text": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.0351, + "head_text": "Pablo Isla, the", + "tail_text": "Telefonica. The" + }, + { + "label": "ceo_of", + "score": 0.0351, + "head_text": "A Coruna.", + "tail_text": "Telefonica. The" + }, + { + "label": "ceo_of", + "score": 0.035, + "head_text": "Pablo Isla, the", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "ceo_of", + "score": 0.035, + "head_text": "A Coruna.", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "ceo_of", + "score": 0.0349, + "head_text": "Pablo Isla, the", + "tail_text": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0349, + "head_text": "A Coruna.", + "tail_text": "Inditex, has" + }, + { + "label": "headquartered_in", + "score": 0.0349, + "head_text": "Inditex has", + "tail_text": "Telefonica, in" + }, + { + "label": "headquartered_in", + "score": 0.0347, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0341, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.0339, + "head_text": "Arteixo, A", + "tail_text": "Telefonica. The" + }, + { + "label": "ceo_of", + "score": 0.0335, + "head_text": "Madrid last", + "tail_text": "Pablo Isla, the" + }, + { + "label": "ceo_of", + "score": 0.0335, + "head_text": "Madrid last", + "tail_text": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0335, + "head_text": "Telefonica, in", + "tail_text": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.0327, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.0326, + "head_text": "Inditex, has", + "tail_text": "Madrid last" + }, + { + "label": "headquartered_in", + "score": 0.031, + "head_text": "Inditex, has", + "tail_text": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.0303, + "head_text": "Inditex has", + "tail_text": "Madrid last" + }, + { + "label": "located_in", + "score": 0.0288, + "head_text": "Inditex has", + "tail_text": "Madrid last" + }, + { + "label": "headquartered_in", + "score": 0.0282, + "head_text": "Inditex has", + "tail_text": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.0275, + "head_text": "Arteixo, A", + "tail_text": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.0275, + "head_text": "Arteixo, A", + "tail_text": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0274, + "head_text": "Inditex, has", + "tail_text": "Pablo Isla, the" + }, + { + "label": "ceo_of", + "score": 0.0274, + "head_text": "Inditex, has", + "tail_text": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.026, + "head_text": "Madrid last", + "tail_text": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.025, + "head_text": "Telefonica, in", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "ceo_of", + "score": 0.0243, + "head_text": "Telefonica. The", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "located_in", + "score": 0.0241, + "head_text": "Telefonica. The", + "tail_text": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.024, + "head_text": "Madrid last", + "tail_text": "Telefonica, in" + }, + { + "label": "ceo_of", + "score": 0.0234, + "head_text": "Telefonica. The", + "tail_text": "Arteixo, A" + }, + { + "label": "ceo_of", + "score": 0.0232, + "head_text": "Pablo Isla, the", + "tail_text": "Arteixo, A" + }, + { + "label": "ceo_of", + "score": 0.0232, + "head_text": "A Coruna.", + "tail_text": "Arteixo, A" + }, + { + "label": "ceo_of", + "score": 0.0226, + "head_text": "Arteixo, A", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "located_in", + "score": 0.0224, + "head_text": "Madrid last", + "tail_text": "Inditex, has" + }, + { + "label": "headquartered_in", + "score": 0.0209, + "head_text": "Telefonica. The", + "tail_text": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0205, + "head_text": "Pablo Isla, the", + "tail_text": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.0205, + "head_text": "A Coruna.", + "tail_text": "Madrid last" + }, + { + "label": "headquartered_in", + "score": 0.02, + "head_text": "Telefonica, in", + "tail_text": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.0197, + "head_text": "Pablo Isla, the", + "tail_text": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0197, + "head_text": "A Coruna.", + "tail_text": "Pablo Isla, the" + }, + { + "label": "located_in", + "score": 0.0189, + "head_text": "Telefonica, in", + "tail_text": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0183, + "head_text": "Madrid last", + "tail_text": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.0181, + "head_text": "Inditex, has", + "tail_text": "Madrid last" + }, + { + "label": "headquartered_in", + "score": 0.0181, + "head_text": "Telefonica, in", + "tail_text": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.017, + "head_text": "Arteixo, A", + "tail_text": "Telefonica. The" + }, + { + "label": "appointed_as", + "score": 0.0169, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.0169, + "head_text": "Arteixo, A", + "tail_text": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.0168, + "head_text": "Inditex has", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "headquartered_in", + "score": 0.0165, + "head_text": "Telefonica. The", + "tail_text": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.0165, + "head_text": "Arteixo, A", + "tail_text": "Pablo Isla, the" + }, + { + "label": "ceo_of", + "score": 0.0165, + "head_text": "Arteixo, A", + "tail_text": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.0162, + "head_text": "Pablo Isla, the", + "tail_text": "Madrid last" + }, + { + "label": "headquartered_in", + "score": 0.0162, + "head_text": "A Coruna.", + "tail_text": "Madrid last" + }, + { + "label": "headquartered_in", + "score": 0.0161, + "head_text": "Inditex has", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "headquartered_in", + "score": 0.016, + "head_text": "Madrid last", + "tail_text": "Pablo Isla, the" + }, + { + "label": "headquartered_in", + "score": 0.016, + "head_text": "Madrid last", + "tail_text": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.0153, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.0153, + "head_text": "Inditex, has", + "tail_text": "Madrid last" + }, + { + "label": "located_in", + "score": 0.0152, + "head_text": "Madrid last", + "tail_text": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.0149, + "head_text": "Inditex, has", + "tail_text": "Telefonica, in" + }, + { + "label": "headquartered_in", + "score": 0.0147, + "head_text": "Inditex, has", + "tail_text": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.0146, + "head_text": "Arteixo, A", + "tail_text": "Madrid last" + }, + { + "label": "works_at", + "score": 0.0144, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Inditex, has" + }, + { + "label": "headquartered_in", + "score": 0.0143, + "head_text": "Madrid last", + "tail_text": "Telefonica. The" + }, + { + "label": "appointed_as", + "score": 0.014, + "head_text": "Arteixo, A", + "tail_text": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0139, + "head_text": "Telefonica, in", + "tail_text": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.0134, + "head_text": "Inditex has", + "tail_text": "Telefonica. The" + }, + { + "label": "ceo_of", + "score": 0.0133, + "head_text": "Inditex, has", + "tail_text": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.0129, + "head_text": "Telefonica, in", + "tail_text": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.0128, + "head_text": "Telefonica. The", + "tail_text": "Madrid last" + }, + { + "label": "works_at", + "score": 0.0127, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0127, + "head_text": "Madrid last", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "works_at", + "score": 0.0126, + "head_text": "Madrid last", + "tail_text": "Pablo Isla, the" + }, + { + "label": "works_at", + "score": 0.0126, + "head_text": "Madrid last", + "tail_text": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0124, + "head_text": "Madrid last", + "tail_text": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.0118, + "head_text": "Pablo Isla, the", + "tail_text": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.0118, + "head_text": "A Coruna.", + "tail_text": "Arteixo, A" + }, + { + "label": "works_at", + "score": 0.0118, + "head_text": "Pablo Isla, the", + "tail_text": "Madrid last" + }, + { + "label": "works_at", + "score": 0.0118, + "head_text": "A Coruna.", + "tail_text": "Madrid last" + }, + { + "label": "appointed_as", + "score": 0.0117, + "head_text": "Telefonica. The", + "tail_text": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.0113, + "head_text": "Inditex has", + "tail_text": "Telefonica. The" + }, + { + "label": "headquartered_in", + "score": 0.0112, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0111, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Madrid last" + }, + { + "label": "works_at", + "score": 0.011, + "head_text": "Inditex, has", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "headquartered_in", + "score": 0.0109, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Telefonica, in" + }, + { + "label": "ceo_of", + "score": 0.0107, + "head_text": "Madrid last", + "tail_text": "Telefonica, in" + }, + { + "label": "ceo_of", + "score": 0.0106, + "head_text": "Arteixo, A", + "tail_text": "Telefonica, in" + }, + { + "label": "works_at", + "score": 0.0103, + "head_text": "Madrid last", + "tail_text": "Inditex, has" + }, + { + "label": "works_at", + "score": 0.0101, + "head_text": "Pablo Isla, the", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "works_at", + "score": 0.0101, + "head_text": "A Coruna.", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "headquartered_in", + "score": 0.01, + "head_text": "Madrid last", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "located_in", + "score": 0.0098, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Telefonica, in" + }, + { + "label": "works_at", + "score": 0.0098, + "head_text": "Inditex has", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "works_at", + "score": 0.0097, + "head_text": "Inditex, has", + "tail_text": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.0097, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0096, + "head_text": "Arteixo, A", + "tail_text": "Inditex, has" + }, + { + "label": "works_at", + "score": 0.0095, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Pablo Isla, the" + }, + { + "label": "works_at", + "score": 0.0095, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "A Coruna." + }, + { + "label": "located_in", + "score": 0.0094, + "head_text": "Telefonica. The", + "tail_text": "Madrid last" + }, + { + "label": "located_in", + "score": 0.0094, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Madrid last" + }, + { + "label": "works_at", + "score": 0.0093, + "head_text": "Telefonica, in", + "tail_text": "Inditex, has" + }, + { + "label": "works_at", + "score": 0.0093, + "head_text": "Inditex, has", + "tail_text": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.009, + "head_text": "Madrid last", + "tail_text": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.0088, + "head_text": "Madrid last", + "tail_text": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.0086, + "head_text": "Madrid last", + "tail_text": "Pablo Isla, the" + }, + { + "label": "located_in", + "score": 0.0086, + "head_text": "Madrid last", + "tail_text": "A Coruna." + }, + { + "label": "located_in", + "score": 0.0086, + "head_text": "Pablo Isla, the", + "tail_text": "Inditex has" + }, + { + "label": "located_in", + "score": 0.0086, + "head_text": "A Coruna.", + "tail_text": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.0081, + "head_text": "Madrid last", + "tail_text": "Arteixo, A" + }, + { + "label": "works_at", + "score": 0.0081, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.008, + "head_text": "Inditex has", + "tail_text": "Pablo Isla, the" + }, + { + "label": "located_in", + "score": 0.008, + "head_text": "Inditex has", + "tail_text": "A Coruna." + }, + { + "label": "works_at", + "score": 0.0079, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.0075, + "head_text": "Pablo Isla, the", + "tail_text": "Madrid last" + }, + { + "label": "located_in", + "score": 0.0075, + "head_text": "A Coruna.", + "tail_text": "Madrid last" + }, + { + "label": "works_at", + "score": 0.0073, + "head_text": "Pablo Isla, the", + "tail_text": "Telefonica, in" + }, + { + "label": "works_at", + "score": 0.0073, + "head_text": "A Coruna.", + "tail_text": "Telefonica, in" + }, + { + "label": "works_at", + "score": 0.0073, + "head_text": "Inditex, has", + "tail_text": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.0072, + "head_text": "Inditex, has", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "headquartered_in", + "score": 0.0072, + "head_text": "Pablo Isla, the", + "tail_text": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.0072, + "head_text": "A Coruna.", + "tail_text": "Inditex has" + }, + { + "label": "works_at", + "score": 0.0072, + "head_text": "Arteixo, A", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "located_in", + "score": 0.0072, + "head_text": "Madrid last", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "located_in", + "score": 0.0072, + "head_text": "Telefonica. The", + "tail_text": "Telefonica, in" + }, + { + "label": "works_at", + "score": 0.0071, + "head_text": "Telefonica. The", + "tail_text": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.0071, + "head_text": "Telefonica. The", + "tail_text": "Telefonica, in" + }, + { + "label": "works_at", + "score": 0.0071, + "head_text": "Telefonica. The", + "tail_text": "Inditex, has" + }, + { + "label": "works_at", + "score": 0.0071, + "head_text": "Inditex has", + "tail_text": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.007, + "head_text": "Inditex, has", + "tail_text": "Arteixo, A" + }, + { + "label": "ceo_of", + "score": 0.007, + "head_text": "Telefonica, in", + "tail_text": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.007, + "head_text": "Inditex has", + "tail_text": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.007, + "head_text": "Telefonica, in", + "tail_text": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.0069, + "head_text": "Inditex, has", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "headquartered_in", + "score": 0.0066, + "head_text": "Telefonica, in", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "works_at", + "score": 0.0066, + "head_text": "Inditex has", + "tail_text": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.0066, + "head_text": "Telefonica, in", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "works_at", + "score": 0.0065, + "head_text": "Inditex, has", + "tail_text": "Telefonica. The" + }, + { + "label": "ceo_of", + "score": 0.0064, + "head_text": "Inditex has", + "tail_text": "Madrid last" + }, + { + "label": "works_at", + "score": 0.0064, + "head_text": "Madrid last", + "tail_text": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.0063, + "head_text": "Inditex has", + "tail_text": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.0062, + "head_text": "Telefonica, in", + "tail_text": "Telefonica. The" + }, + { + "label": "headquartered_in", + "score": 0.006, + "head_text": "Inditex has", + "tail_text": "Pablo Isla, the" + }, + { + "label": "headquartered_in", + "score": 0.006, + "head_text": "Inditex has", + "tail_text": "A Coruna." + }, + { + "label": "works_at", + "score": 0.006, + "head_text": "Telefonica, in", + "tail_text": "Pablo Isla, the" + }, + { + "label": "works_at", + "score": 0.006, + "head_text": "Telefonica, in", + "tail_text": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.0058, + "head_text": "Telefonica, in", + "tail_text": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.0058, + "head_text": "Telefonica. The", + "tail_text": "Telefonica, in" + }, + { + "label": "works_at", + "score": 0.0058, + "head_text": "Telefonica. The", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "works_at", + "score": 0.0057, + "head_text": "Telefonica. The", + "tail_text": "Madrid last" + }, + { + "label": "works_at", + "score": 0.0057, + "head_text": "Telefonica, in", + "tail_text": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0057, + "head_text": "Arteixo, A", + "tail_text": "Madrid last" + }, + { + "label": "works_at", + "score": 0.0056, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Madrid last" + }, + { + "label": "works_at", + "score": 0.0055, + "head_text": "Telefonica, in", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "works_at", + "score": 0.0054, + "head_text": "Telefonica, in", + "tail_text": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.0053, + "head_text": "Telefonica. The", + "tail_text": "Inditex, has" + }, + { + "label": "works_at", + "score": 0.0051, + "head_text": "Pablo Isla, the", + "tail_text": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.0051, + "head_text": "A Coruna.", + "tail_text": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.0051, + "head_text": "Pablo Isla, the", + "tail_text": "Inditex has" + }, + { + "label": "works_at", + "score": 0.0051, + "head_text": "A Coruna.", + "tail_text": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.0051, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.005, + "head_text": "Pablo Isla, the", + "tail_text": "Inditex, has" + }, + { + "label": "works_at", + "score": 0.005, + "head_text": "A Coruna.", + "tail_text": "Inditex, has" + }, + { + "label": "works_at", + "score": 0.005, + "head_text": "Madrid last", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.005, + "head_text": "Arteixo, A", + "tail_text": "Inditex has" + }, + { + "label": "located_in", + "score": 0.0049, + "head_text": "Telefonica, in", + "tail_text": "Pablo Isla, the" + }, + { + "label": "located_in", + "score": 0.0049, + "head_text": "Telefonica, in", + "tail_text": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.0049, + "head_text": "Telefonica. The", + "tail_text": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0048, + "head_text": "Arteixo, A", + "tail_text": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.0047, + "head_text": "Telefonica, in", + "tail_text": "Pablo Isla, the" + }, + { + "label": "headquartered_in", + "score": 0.0047, + "head_text": "Telefonica, in", + "tail_text": "A Coruna." + }, + { + "label": "works_at", + "score": 0.0047, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Arteixo, A" + }, + { + "label": "works_at", + "score": 0.0047, + "head_text": "Arteixo, A", + "tail_text": "Madrid last" + }, + { + "label": "located_in", + "score": 0.0045, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.0045, + "head_text": "Inditex has", + "tail_text": "Pablo Isla, the" + }, + { + "label": "works_at", + "score": 0.0045, + "head_text": "Inditex has", + "tail_text": "A Coruna." + }, + { + "label": "works_at", + "score": 0.0044, + "head_text": "Telefonica. The", + "tail_text": "Pablo Isla, the" + }, + { + "label": "works_at", + "score": 0.0044, + "head_text": "Telefonica. The", + "tail_text": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.0043, + "head_text": "Pablo Isla, the", + "tail_text": "Telefonica, in" + }, + { + "label": "headquartered_in", + "score": 0.0043, + "head_text": "A Coruna.", + "tail_text": "Telefonica, in" + }, + { + "label": "works_at", + "score": 0.004, + "head_text": "Inditex, has", + "tail_text": "Pablo Isla, the" + }, + { + "label": "works_at", + "score": 0.004, + "head_text": "Inditex, has", + "tail_text": "A Coruna." + }, + { + "label": "works_at", + "score": 0.004, + "head_text": "Madrid last", + "tail_text": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.004, + "head_text": "Pablo Isla, the", + "tail_text": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.004, + "head_text": "A Coruna.", + "tail_text": "Telefonica, in" + }, + { + "label": "headquartered_in", + "score": 0.0039, + "head_text": "Telefonica. The", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "located_in", + "score": 0.0038, + "head_text": "Inditex, has", + "tail_text": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.0037, + "head_text": "Madrid last", + "tail_text": "Inditex has" + }, + { + "label": "works_at", + "score": 0.0037, + "head_text": "Inditex has", + "tail_text": "Madrid last" + }, + { + "label": "located_in", + "score": 0.0037, + "head_text": "Telefonica. The", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "headquartered_in", + "score": 0.0036, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Pablo Isla, the" + }, + { + "label": "headquartered_in", + "score": 0.0036, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.0035, + "head_text": "Inditex has", + "tail_text": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.0034, + "head_text": "Inditex, has", + "tail_text": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.0034, + "head_text": "Arteixo, A", + "tail_text": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.0033, + "head_text": "Telefonica, in", + "tail_text": "Madrid last" + }, + { + "label": "located_in", + "score": 0.0032, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Pablo Isla, the" + }, + { + "label": "located_in", + "score": 0.0032, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.0032, + "head_text": "Pablo Isla, the", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "headquartered_in", + "score": 0.0032, + "head_text": "A Coruna.", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "works_at", + "score": 0.003, + "head_text": "Pablo Isla, the", + "tail_text": "A Coruna." + }, + { + "label": "works_at", + "score": 0.003, + "head_text": "A Coruna.", + "tail_text": "Pablo Isla, the" + }, + { + "label": "works_at", + "score": 0.003, + "head_text": "Arteixo, A", + "tail_text": "Pablo Isla, the" + }, + { + "label": "works_at", + "score": 0.003, + "head_text": "Arteixo, A", + "tail_text": "A Coruna." + }, + { + "label": "located_in", + "score": 0.0028, + "head_text": "Pablo Isla, the", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "located_in", + "score": 0.0028, + "head_text": "A Coruna.", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "works_at", + "score": 0.0027, + "head_text": "Arteixo, A", + "tail_text": "Telefonica, in" + }, + { + "label": "works_at", + "score": 0.0025, + "head_text": "Pablo Isla, the", + "tail_text": "Arteixo, A" + }, + { + "label": "works_at", + "score": 0.0025, + "head_text": "A Coruna.", + "tail_text": "Arteixo, A" + }, + { + "label": "works_at", + "score": 0.0024, + "head_text": "Madrid last", + "tail_text": "Arteixo, A" + }, + { + "label": "works_at", + "score": 0.0022, + "head_text": "Arteixo, A", + "tail_text": "Inditex, has" + }, + { + "label": "works_at", + "score": 0.0021, + "head_text": "Telefonica. The", + "tail_text": "Arteixo, A" + }, + { + "label": "works_at", + "score": 0.0018, + "head_text": "Inditex, has", + "tail_text": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.0018, + "head_text": "Pablo Isla, the", + "tail_text": "Inditex, has" + }, + { + "label": "located_in", + "score": 0.0018, + "head_text": "A Coruna.", + "tail_text": "Inditex, has" + }, + { + "label": "located_in", + "score": 0.0018, + "head_text": "Inditex, has", + "tail_text": "Pablo Isla, the" + }, + { + "label": "located_in", + "score": 0.0018, + "head_text": "Inditex, has", + "tail_text": "A Coruna." + }, + { + "label": "located_in", + "score": 0.0016, + "head_text": "Telefonica. The", + "tail_text": "Pablo Isla, the" + }, + { + "label": "located_in", + "score": 0.0016, + "head_text": "Telefonica. The", + "tail_text": "A Coruna." + }, + { + "label": "works_at", + "score": 0.0016, + "head_text": "Telefonica, in", + "tail_text": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.0014, + "head_text": "Pablo Isla, the", + "tail_text": "Inditex, has" + }, + { + "label": "headquartered_in", + "score": 0.0014, + "head_text": "A Coruna.", + "tail_text": "Inditex, has" + }, + { + "label": "headquartered_in", + "score": 0.0014, + "head_text": "Telefonica. The", + "tail_text": "Pablo Isla, the" + }, + { + "label": "headquartered_in", + "score": 0.0014, + "head_text": "Telefonica. The", + "tail_text": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.0013, + "head_text": "Inditex, has", + "tail_text": "Pablo Isla, the" + }, + { + "label": "headquartered_in", + "score": 0.0013, + "head_text": "Inditex, has", + "tail_text": "A Coruna." + }, + { + "label": "located_in", + "score": 0.0013, + "head_text": "Pablo Isla, the", + "tail_text": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.0013, + "head_text": "A Coruna.", + "tail_text": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.0012, + "head_text": "Arteixo, A", + "tail_text": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.0012, + "head_text": "Pablo Isla, the", + "tail_text": "Telefonica. The" + }, + { + "label": "headquartered_in", + "score": 0.0012, + "head_text": "A Coruna.", + "tail_text": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.0012, + "head_text": "Inditex has", + "tail_text": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.0006, + "head_text": "Pablo Isla, the", + "tail_text": "A Coruna." + }, + { + "label": "located_in", + "score": 0.0006, + "head_text": "A Coruna.", + "tail_text": "Pablo Isla, the" + }, + { + "label": "headquartered_in", + "score": 0.0004, + "head_text": "Pablo Isla, the", + "tail_text": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.0004, + "head_text": "A Coruna.", + "tail_text": "Pablo Isla, the" + } + ], + "natural_long": [ + { + "label": "organization headquartered in location", + "score": 0.0802, + "head_text": "Inditex, has", + "tail_text": "Inditex has" + }, + { + "label": "organization headquartered in location", + "score": 0.0753, + "head_text": "Inditex has", + "tail_text": "Inditex, has" + }, + { + "label": "organization is located in location", + "score": 0.0607, + "head_text": "Inditex, has", + "tail_text": "Inditex has" + }, + { + "label": "organization headquartered in location", + "score": 0.0592, + "head_text": "Telefonica, in", + "tail_text": "Inditex has" + }, + { + "label": "organization is located in location", + "score": 0.0573, + "head_text": "Inditex has", + "tail_text": "Inditex, has" + }, + { + "label": "organization headquartered in location", + "score": 0.0444, + "head_text": "Inditex has", + "tail_text": "Arteixo, A" + }, + { + "label": "person appointed as role at organization", + "score": 0.0442, + "head_text": "Inditex has", + "tail_text": "Inditex, has" + }, + { + "label": "organization is located in location", + "score": 0.0424, + "head_text": "Telefonica, in", + "tail_text": "Inditex has" + }, + { + "label": "person appointed as role at organization", + "score": 0.0403, + "head_text": "Pablo Isla, the", + "tail_text": "Inditex, has" + }, + { + "label": "person appointed as role at organization", + "score": 0.0403, + "head_text": "A Coruna.", + "tail_text": "Inditex, has" + }, + { + "label": "person appointed as role at organization", + "score": 0.0387, + "head_text": "Inditex, has", + "tail_text": "Pablo Isla, the" + }, + { + "label": "person appointed as role at organization", + "score": 0.0387, + "head_text": "Inditex, has", + "tail_text": "A Coruna." + }, + { + "label": "person appointed as role at organization", + "score": 0.0379, + "head_text": "Telefonica. The", + "tail_text": "Inditex, has" + }, + { + "label": "person appointed as role at organization", + "score": 0.0376, + "head_text": "Telefonica, in", + "tail_text": "Inditex, has" + }, + { + "label": "person appointed as role at organization", + "score": 0.0367, + "head_text": "Inditex, has", + "tail_text": "Inditex has" + }, + { + "label": "organization headquartered in location", + "score": 0.0362, + "head_text": "Telefonica. The", + "tail_text": "Inditex has" + }, + { + "label": "organization headquartered in location", + "score": 0.0353, + "head_text": "Arteixo, A", + "tail_text": "Inditex has" + }, + { + "label": "person appointed as role at organization", + "score": 0.0342, + "head_text": "Pablo Isla, the", + "tail_text": "A Coruna." + }, + { + "label": "person appointed as role at organization", + "score": 0.0342, + "head_text": "A Coruna.", + "tail_text": "Pablo Isla, the" + }, + { + "label": "organization headquartered in location", + "score": 0.0342, + "head_text": "Inditex, has", + "tail_text": "Arteixo, A" + }, + { + "label": "person appointed as role at organization", + "score": 0.0332, + "head_text": "Inditex has", + "tail_text": "Pablo Isla, the" + }, + { + "label": "person appointed as role at organization", + "score": 0.0332, + "head_text": "Inditex has", + "tail_text": "A Coruna." + }, + { + "label": "organization headquartered in location", + "score": 0.0328, + "head_text": "Inditex has", + "tail_text": "Telefonica, in" + }, + { + "label": "person works at organization", + "score": 0.0315, + "head_text": "Inditex has", + "tail_text": "Inditex, has" + }, + { + "label": "person is ceo of organization", + "score": 0.0311, + "head_text": "Inditex has", + "tail_text": "Inditex, has" + }, + { + "label": "person appointed as role at organization", + "score": 0.0297, + "head_text": "Telefonica, in", + "tail_text": "Pablo Isla, the" + }, + { + "label": "person appointed as role at organization", + "score": 0.0297, + "head_text": "Telefonica, in", + "tail_text": "A Coruna." + }, + { + "label": "organization headquartered in location", + "score": 0.0284, + "head_text": "Madrid last", + "tail_text": "Arteixo, A" + }, + { + "label": "organization headquartered in location", + "score": 0.0282, + "head_text": "Telefonica, in", + "tail_text": "Inditex, has" + }, + { + "label": "person appointed as role at organization", + "score": 0.0279, + "head_text": "Telefonica. The", + "tail_text": "Pablo Isla, the" + }, + { + "label": "person appointed as role at organization", + "score": 0.0279, + "head_text": "Telefonica. The", + "tail_text": "A Coruna." + }, + { + "label": "person is ceo of organization", + "score": 0.0279, + "head_text": "Inditex, has", + "tail_text": "Inditex has" + }, + { + "label": "organization is located in location", + "score": 0.0276, + "head_text": "Telefonica. The", + "tail_text": "Inditex has" + }, + { + "label": "organization headquartered in location", + "score": 0.0275, + "head_text": "Telefonica, in", + "tail_text": "Arteixo, A" + }, + { + "label": "person is ceo of organization", + "score": 0.0264, + "head_text": "Telefonica. The", + "tail_text": "Inditex, has" + }, + { + "label": "person is ceo of organization", + "score": 0.0264, + "head_text": "Telefonica, in", + "tail_text": "Inditex, has" + }, + { + "label": "organization headquartered in location", + "score": 0.0264, + "head_text": "Arteixo, A", + "tail_text": "Inditex, has" + }, + { + "label": "person works at organization", + "score": 0.0258, + "head_text": "Inditex, has", + "tail_text": "Inditex has" + }, + { + "label": "person works at organization", + "score": 0.0257, + "head_text": "Telefonica, in", + "tail_text": "Inditex, has" + }, + { + "label": "person is ceo of organization", + "score": 0.025, + "head_text": "Pablo Isla, the", + "tail_text": "Inditex, has" + }, + { + "label": "person is ceo of organization", + "score": 0.025, + "head_text": "A Coruna.", + "tail_text": "Inditex, has" + }, + { + "label": "person works at organization", + "score": 0.025, + "head_text": "Pablo Isla, the", + "tail_text": "Inditex, has" + }, + { + "label": "person works at organization", + "score": 0.025, + "head_text": "A Coruna.", + "tail_text": "Inditex, has" + }, + { + "label": "organization headquartered in location", + "score": 0.025, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Arteixo, A" + }, + { + "label": "person works at organization", + "score": 0.0249, + "head_text": "Inditex, has", + "tail_text": "Pablo Isla, the" + }, + { + "label": "person works at organization", + "score": 0.0249, + "head_text": "Inditex, has", + "tail_text": "A Coruna." + }, + { + "label": "person works at organization", + "score": 0.0248, + "head_text": "Telefonica. The", + "tail_text": "Inditex, has" + }, + { + "label": "person appointed as role at organization", + "score": 0.0238, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Inditex, has" + }, + { + "label": "person appointed as role at organization", + "score": 0.0238, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Pablo Isla, the" + }, + { + "label": "person appointed as role at organization", + "score": 0.0238, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "A Coruna." + }, + { + "label": "person is ceo of organization", + "score": 0.0235, + "head_text": "Inditex, has", + "tail_text": "Pablo Isla, the" + }, + { + "label": "person is ceo of organization", + "score": 0.0235, + "head_text": "Inditex, has", + "tail_text": "A Coruna." + }, + { + "label": "person appointed as role at organization", + "score": 0.0234, + "head_text": "Madrid last", + "tail_text": "Pablo Isla, the" + }, + { + "label": "person appointed as role at organization", + "score": 0.0234, + "head_text": "Madrid last", + "tail_text": "A Coruna." + }, + { + "label": "organization headquartered in location", + "score": 0.0234, + "head_text": "Telefonica. The", + "tail_text": "Arteixo, A" + }, + { + "label": "person works at organization", + "score": 0.0228, + "head_text": "Pablo Isla, the", + "tail_text": "A Coruna." + }, + { + "label": "person works at organization", + "score": 0.0228, + "head_text": "A Coruna.", + "tail_text": "Pablo Isla, the" + }, + { + "label": "person appointed as role at organization", + "score": 0.0228, + "head_text": "Pablo Isla, the", + "tail_text": "Inditex has" + }, + { + "label": "person appointed as role at organization", + "score": 0.0228, + "head_text": "A Coruna.", + "tail_text": "Inditex has" + }, + { + "label": "person appointed as role at organization", + "score": 0.0223, + "head_text": "Telefonica. The", + "tail_text": "Inditex has" + }, + { + "label": "person appointed as role at organization", + "score": 0.022, + "head_text": "Inditex, has", + "tail_text": "Telefonica, in" + }, + { + "label": "person appointed as role at organization", + "score": 0.022, + "head_text": "Inditex, has", + "tail_text": "Telefonica. The" + }, + { + "label": "person works at organization", + "score": 0.0214, + "head_text": "Inditex has", + "tail_text": "Pablo Isla, the" + }, + { + "label": "person works at organization", + "score": 0.0214, + "head_text": "Inditex has", + "tail_text": "A Coruna." + }, + { + "label": "organization is located in location", + "score": 0.0214, + "head_text": "Inditex has", + "tail_text": "Telefonica, in" + }, + { + "label": "organization headquartered in location", + "score": 0.0214, + "head_text": "Arteixo, A", + "tail_text": "Madrid last" + }, + { + "label": "person is ceo of organization", + "score": 0.0214, + "head_text": "Pablo Isla, the", + "tail_text": "A Coruna." + }, + { + "label": "person is ceo of organization", + "score": 0.0214, + "head_text": "A Coruna.", + "tail_text": "Pablo Isla, the" + }, + { + "label": "organization is located in location", + "score": 0.0212, + "head_text": "Telefonica, in", + "tail_text": "Inditex, has" + }, + { + "label": "person is ceo of organization", + "score": 0.0207, + "head_text": "Inditex has", + "tail_text": "Pablo Isla, the" + }, + { + "label": "person is ceo of organization", + "score": 0.0207, + "head_text": "Inditex has", + "tail_text": "A Coruna." + }, + { + "label": "person is ceo of organization", + "score": 0.0204, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Inditex, has" + }, + { + "label": "person works at organization", + "score": 0.0196, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Inditex, has" + }, + { + "label": "organization is located in location", + "score": 0.0195, + "head_text": "Inditex has", + "tail_text": "Arteixo, A" + }, + { + "label": "organization headquartered in location", + "score": 0.0193, + "head_text": "Madrid last", + "tail_text": "Inditex has" + }, + { + "label": "person is ceo of organization", + "score": 0.0192, + "head_text": "Telefonica. The", + "tail_text": "Inditex has" + }, + { + "label": "person works at organization", + "score": 0.019, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Pablo Isla, the" + }, + { + "label": "person works at organization", + "score": 0.019, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "A Coruna." + }, + { + "label": "person works at organization", + "score": 0.0186, + "head_text": "Telefonica, in", + "tail_text": "Pablo Isla, the" + }, + { + "label": "person works at organization", + "score": 0.0186, + "head_text": "Telefonica, in", + "tail_text": "A Coruna." + }, + { + "label": "person is ceo of organization", + "score": 0.0185, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Pablo Isla, the" + }, + { + "label": "person is ceo of organization", + "score": 0.0185, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "A Coruna." + }, + { + "label": "person is ceo of organization", + "score": 0.0182, + "head_text": "Telefonica, in", + "tail_text": "Pablo Isla, the" + }, + { + "label": "person is ceo of organization", + "score": 0.0182, + "head_text": "Telefonica, in", + "tail_text": "A Coruna." + }, + { + "label": "organization headquartered in location", + "score": 0.0177, + "head_text": "Arteixo, A", + "tail_text": "Telefonica, in" + }, + { + "label": "organization headquartered in location", + "score": 0.0175, + "head_text": "Pablo Isla, the", + "tail_text": "Arteixo, A" + }, + { + "label": "organization headquartered in location", + "score": 0.0175, + "head_text": "A Coruna.", + "tail_text": "Arteixo, A" + }, + { + "label": "organization headquartered in location", + "score": 0.0174, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Inditex has" + }, + { + "label": "person works at organization", + "score": 0.0173, + "head_text": "Madrid last", + "tail_text": "Pablo Isla, the" + }, + { + "label": "person works at organization", + "score": 0.0173, + "head_text": "Madrid last", + "tail_text": "A Coruna." + }, + { + "label": "person works at organization", + "score": 0.0172, + "head_text": "Telefonica. The", + "tail_text": "Pablo Isla, the" + }, + { + "label": "person works at organization", + "score": 0.0172, + "head_text": "Telefonica. The", + "tail_text": "A Coruna." + }, + { + "label": "person is ceo of organization", + "score": 0.0171, + "head_text": "Telefonica. The", + "tail_text": "Pablo Isla, the" + }, + { + "label": "person is ceo of organization", + "score": 0.0171, + "head_text": "Telefonica. The", + "tail_text": "A Coruna." + }, + { + "label": "organization is located in location", + "score": 0.0164, + "head_text": "Arteixo, A", + "tail_text": "Inditex has" + }, + { + "label": "organization headquartered in location", + "score": 0.0164, + "head_text": "Inditex, has", + "tail_text": "Telefonica, in" + }, + { + "label": "person appointed as role at organization", + "score": 0.0163, + "head_text": "Inditex has", + "tail_text": "Telefonica. The" + }, + { + "label": "person appointed as role at organization", + "score": 0.0163, + "head_text": "Pablo Isla, the", + "tail_text": "Telefonica. The" + }, + { + "label": "person appointed as role at organization", + "score": 0.0163, + "head_text": "A Coruna.", + "tail_text": "Telefonica. The" + }, + { + "label": "person is ceo of organization", + "score": 0.0159, + "head_text": "Pablo Isla, the", + "tail_text": "Inditex has" + }, + { + "label": "person is ceo of organization", + "score": 0.0159, + "head_text": "A Coruna.", + "tail_text": "Inditex has" + }, + { + "label": "organization headquartered in location", + "score": 0.0158, + "head_text": "Arteixo, A", + "tail_text": "Pablo Isla, the" + }, + { + "label": "organization headquartered in location", + "score": 0.0158, + "head_text": "Arteixo, A", + "tail_text": "A Coruna." + }, + { + "label": "person appointed as role at organization", + "score": 0.0156, + "head_text": "Pablo Isla, the", + "tail_text": "Telefonica, in" + }, + { + "label": "person appointed as role at organization", + "score": 0.0156, + "head_text": "A Coruna.", + "tail_text": "Telefonica, in" + }, + { + "label": "person works at organization", + "score": 0.0154, + "head_text": "Telefonica. The", + "tail_text": "Inditex has" + }, + { + "label": "person appointed as role at organization", + "score": 0.015, + "head_text": "Madrid last", + "tail_text": "Inditex, has" + }, + { + "label": "person works at organization", + "score": 0.0149, + "head_text": "Inditex, has", + "tail_text": "Telefonica, in" + }, + { + "label": "organization is located in location", + "score": 0.0149, + "head_text": "Inditex, has", + "tail_text": "Arteixo, A" + }, + { + "label": "person is ceo of organization", + "score": 0.0148, + "head_text": "Inditex, has", + "tail_text": "Telefonica, in" + }, + { + "label": "organization headquartered in location", + "score": 0.0145, + "head_text": "Madrid last", + "tail_text": "Inditex, has" + }, + { + "label": "person works at organization", + "score": 0.0144, + "head_text": "Inditex, has", + "tail_text": "Telefonica. The" + }, + { + "label": "person is ceo of organization", + "score": 0.0143, + "head_text": "Inditex, has", + "tail_text": "Telefonica. The" + }, + { + "label": "person appointed as role at organization", + "score": 0.014, + "head_text": "Telefonica, in", + "tail_text": "Inditex has" + }, + { + "label": "organization headquartered in location", + "score": 0.0138, + "head_text": "Inditex has", + "tail_text": "Telefonica. The" + }, + { + "label": "person works at organization", + "score": 0.0135, + "head_text": "Pablo Isla, the", + "tail_text": "Inditex has" + }, + { + "label": "person works at organization", + "score": 0.0135, + "head_text": "A Coruna.", + "tail_text": "Inditex has" + }, + { + "label": "organization headquartered in location", + "score": 0.0132, + "head_text": "Pablo Isla, the", + "tail_text": "Inditex has" + }, + { + "label": "organization headquartered in location", + "score": 0.0132, + "head_text": "A Coruna.", + "tail_text": "Inditex has" + }, + { + "label": "organization headquartered in location", + "score": 0.0129, + "head_text": "Inditex has", + "tail_text": "Pablo Isla, the" + }, + { + "label": "organization headquartered in location", + "score": 0.0129, + "head_text": "Inditex has", + "tail_text": "A Coruna." + }, + { + "label": "organization headquartered in location", + "score": 0.0128, + "head_text": "Arteixo, A", + "tail_text": "Telefonica. The" + }, + { + "label": "organization is located in location", + "score": 0.0124, + "head_text": "Telefonica, in", + "tail_text": "Arteixo, A" + }, + { + "label": "organization is located in location", + "score": 0.0124, + "head_text": "Arteixo, A", + "tail_text": "Inditex, has" + }, + { + "label": "person is ceo of organization", + "score": 0.0124, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Inditex has" + }, + { + "label": "organization headquartered in location", + "score": 0.0123, + "head_text": "Telefonica. The", + "tail_text": "Inditex, has" + }, + { + "label": "person is ceo of organization", + "score": 0.0123, + "head_text": "Madrid last", + "tail_text": "Pablo Isla, the" + }, + { + "label": "person is ceo of organization", + "score": 0.0123, + "head_text": "Madrid last", + "tail_text": "A Coruna." + }, + { + "label": "person appointed as role at organization", + "score": 0.0123, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Inditex has" + }, + { + "label": "organization is located in location", + "score": 0.0121, + "head_text": "Madrid last", + "tail_text": "Arteixo, A" + }, + { + "label": "person appointed as role at organization", + "score": 0.0121, + "head_text": "Inditex has", + "tail_text": "Telefonica, in" + }, + { + "label": "person is ceo of organization", + "score": 0.0121, + "head_text": "Telefonica, in", + "tail_text": "Inditex has" + }, + { + "label": "person works at organization", + "score": 0.0117, + "head_text": "Madrid last", + "tail_text": "Inditex, has" + }, + { + "label": "organization is located in location", + "score": 0.0116, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Arteixo, A" + }, + { + "label": "person is ceo of organization", + "score": 0.0116, + "head_text": "Inditex has", + "tail_text": "Telefonica. The" + }, + { + "label": "organization is located in location", + "score": 0.0116, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Inditex has" + }, + { + "label": "person works at organization", + "score": 0.0115, + "head_text": "Inditex has", + "tail_text": "Telefonica. The" + }, + { + "label": "organization is located in location", + "score": 0.0112, + "head_text": "Inditex, has", + "tail_text": "Telefonica, in" + }, + { + "label": "organization is located in location", + "score": 0.0111, + "head_text": "Telefonica. The", + "tail_text": "Arteixo, A" + }, + { + "label": "person appointed as role at organization", + "score": 0.0107, + "head_text": "Telefonica, in", + "tail_text": "Telefonica. The" + }, + { + "label": "person works at organization", + "score": 0.0105, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Inditex has" + }, + { + "label": "organization is located in location", + "score": 0.0104, + "head_text": "Arteixo, A", + "tail_text": "Madrid last" + }, + { + "label": "person works at organization", + "score": 0.0104, + "head_text": "Telefonica, in", + "tail_text": "Inditex has" + }, + { + "label": "organization headquartered in location", + "score": 0.0102, + "head_text": "Inditex has", + "tail_text": "Madrid last" + }, + { + "label": "organization is located in location", + "score": 0.0102, + "head_text": "Madrid last", + "tail_text": "Inditex has" + }, + { + "label": "organization is located in location", + "score": 0.01, + "head_text": "Telefonica. The", + "tail_text": "Inditex, has" + }, + { + "label": "organization headquartered in location", + "score": 0.0098, + "head_text": "Arteixo, A", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "person is ceo of organization", + "score": 0.0097, + "head_text": "Pablo Isla, the", + "tail_text": "Telefonica. The" + }, + { + "label": "person is ceo of organization", + "score": 0.0097, + "head_text": "A Coruna.", + "tail_text": "Telefonica. The" + }, + { + "label": "person is ceo of organization", + "score": 0.0097, + "head_text": "Pablo Isla, the", + "tail_text": "Telefonica, in" + }, + { + "label": "person is ceo of organization", + "score": 0.0097, + "head_text": "A Coruna.", + "tail_text": "Telefonica, in" + }, + { + "label": "organization is located in location", + "score": 0.0096, + "head_text": "Pablo Isla, the", + "tail_text": "Inditex has" + }, + { + "label": "organization is located in location", + "score": 0.0096, + "head_text": "A Coruna.", + "tail_text": "Inditex has" + }, + { + "label": "person works at organization", + "score": 0.0095, + "head_text": "Pablo Isla, the", + "tail_text": "Telefonica. The" + }, + { + "label": "person works at organization", + "score": 0.0095, + "head_text": "A Coruna.", + "tail_text": "Telefonica. The" + }, + { + "label": "organization is located in location", + "score": 0.0095, + "head_text": "Inditex has", + "tail_text": "Pablo Isla, the" + }, + { + "label": "organization is located in location", + "score": 0.0095, + "head_text": "Inditex has", + "tail_text": "A Coruna." + }, + { + "label": "organization is located in location", + "score": 0.0095, + "head_text": "Inditex has", + "tail_text": "Telefonica. The" + }, + { + "label": "person appointed as role at organization", + "score": 0.0093, + "head_text": "Pablo Isla, the", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "person appointed as role at organization", + "score": 0.0093, + "head_text": "A Coruna.", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "person works at organization", + "score": 0.0092, + "head_text": "Inditex has", + "tail_text": "Telefonica, in" + }, + { + "label": "person works at organization", + "score": 0.0092, + "head_text": "Pablo Isla, the", + "tail_text": "Telefonica, in" + }, + { + "label": "person works at organization", + "score": 0.0092, + "head_text": "A Coruna.", + "tail_text": "Telefonica, in" + }, + { + "label": "organization headquartered in location", + "score": 0.0091, + "head_text": "Inditex, has", + "tail_text": "Madrid last" + }, + { + "label": "person appointed as role at organization", + "score": 0.009, + "head_text": "Telefonica. The", + "tail_text": "Telefonica, in" + }, + { + "label": "person appointed as role at organization", + "score": 0.0089, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Telefonica. The" + }, + { + "label": "person is ceo of organization", + "score": 0.0089, + "head_text": "Inditex has", + "tail_text": "Telefonica, in" + }, + { + "label": "organization headquartered in location", + "score": 0.0088, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Inditex, has" + }, + { + "label": "organization is located in location", + "score": 0.0085, + "head_text": "Madrid last", + "tail_text": "Inditex, has" + }, + { + "label": "person appointed as role at organization", + "score": 0.0084, + "head_text": "Inditex, has", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "person is ceo of organization", + "score": 0.0083, + "head_text": "Madrid last", + "tail_text": "Inditex, has" + }, + { + "label": "organization is located in location", + "score": 0.0082, + "head_text": "Arteixo, A", + "tail_text": "Telefonica, in" + }, + { + "label": "person appointed as role at organization", + "score": 0.008, + "head_text": "Pablo Isla, the", + "tail_text": "Madrid last" + }, + { + "label": "person appointed as role at organization", + "score": 0.008, + "head_text": "A Coruna.", + "tail_text": "Madrid last" + }, + { + "label": "person works at organization", + "score": 0.0079, + "head_text": "Pablo Isla, the", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "person works at organization", + "score": 0.0079, + "head_text": "A Coruna.", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "person is ceo of organization", + "score": 0.0077, + "head_text": "Telefonica, in", + "tail_text": "Telefonica. The" + }, + { + "label": "person works at organization", + "score": 0.0075, + "head_text": "Telefonica, in", + "tail_text": "Telefonica. The" + }, + { + "label": "person works at organization", + "score": 0.0075, + "head_text": "Inditex, has", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "organization is located in location", + "score": 0.0075, + "head_text": "Arteixo, A", + "tail_text": "Pablo Isla, the" + }, + { + "label": "organization is located in location", + "score": 0.0075, + "head_text": "Arteixo, A", + "tail_text": "A Coruna." + }, + { + "label": "organization is located in location", + "score": 0.0075, + "head_text": "Pablo Isla, the", + "tail_text": "Arteixo, A" + }, + { + "label": "organization is located in location", + "score": 0.0075, + "head_text": "A Coruna.", + "tail_text": "Arteixo, A" + }, + { + "label": "person is ceo of organization", + "score": 0.0075, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Telefonica. The" + }, + { + "label": "person works at organization", + "score": 0.0072, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Telefonica. The" + }, + { + "label": "person is ceo of organization", + "score": 0.0072, + "head_text": "Pablo Isla, the", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "person is ceo of organization", + "score": 0.0072, + "head_text": "A Coruna.", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "person appointed as role at organization", + "score": 0.0069, + "head_text": "Inditex, has", + "tail_text": "Madrid last" + }, + { + "label": "person is ceo of organization", + "score": 0.0069, + "head_text": "Telefonica. The", + "tail_text": "Telefonica, in" + }, + { + "label": "person is ceo of organization", + "score": 0.0068, + "head_text": "Inditex, has", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "person works at organization", + "score": 0.0064, + "head_text": "Telefonica. The", + "tail_text": "Telefonica, in" + }, + { + "label": "organization headquartered in location", + "score": 0.0063, + "head_text": "Telefonica. The", + "tail_text": "Telefonica, in" + }, + { + "label": "organization is located in location", + "score": 0.0062, + "head_text": "Arteixo, A", + "tail_text": "Telefonica. The" + }, + { + "label": "organization is located in location", + "score": 0.0062, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Inditex, has" + }, + { + "label": "organization headquartered in location", + "score": 0.0062, + "head_text": "Madrid last", + "tail_text": "Pablo Isla, the" + }, + { + "label": "organization headquartered in location", + "score": 0.0062, + "head_text": "Madrid last", + "tail_text": "A Coruna." + }, + { + "label": "organization headquartered in location", + "score": 0.0061, + "head_text": "Telefonica, in", + "tail_text": "Pablo Isla, the" + }, + { + "label": "organization headquartered in location", + "score": 0.0061, + "head_text": "Telefonica, in", + "tail_text": "A Coruna." + }, + { + "label": "organization headquartered in location", + "score": 0.0059, + "head_text": "Inditex, has", + "tail_text": "Telefonica. The" + }, + { + "label": "person works at organization", + "score": 0.0057, + "head_text": "Pablo Isla, the", + "tail_text": "Madrid last" + }, + { + "label": "person works at organization", + "score": 0.0057, + "head_text": "A Coruna.", + "tail_text": "Madrid last" + }, + { + "label": "person appointed as role at organization", + "score": 0.0057, + "head_text": "Inditex has", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "person works at organization", + "score": 0.0055, + "head_text": "Inditex, has", + "tail_text": "Madrid last" + }, + { + "label": "organization headquartered in location", + "score": 0.0055, + "head_text": "Telefonica, in", + "tail_text": "Telefonica. The" + }, + { + "label": "organization is located in location", + "score": 0.0055, + "head_text": "Inditex has", + "tail_text": "Madrid last" + }, + { + "label": "person appointed as role at organization", + "score": 0.0053, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Telefonica, in" + }, + { + "label": "person appointed as role at organization", + "score": 0.0053, + "head_text": "Madrid last", + "tail_text": "Telefonica. The" + }, + { + "label": "organization is located in location", + "score": 0.0053, + "head_text": "Inditex, has", + "tail_text": "Madrid last" + }, + { + "label": "person works at organization", + "score": 0.0053, + "head_text": "Inditex has", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "organization headquartered in location", + "score": 0.0052, + "head_text": "Pablo Isla, the", + "tail_text": "Inditex, has" + }, + { + "label": "organization headquartered in location", + "score": 0.0052, + "head_text": "A Coruna.", + "tail_text": "Inditex, has" + }, + { + "label": "organization headquartered in location", + "score": 0.0051, + "head_text": "Inditex, has", + "tail_text": "Pablo Isla, the" + }, + { + "label": "organization headquartered in location", + "score": 0.0051, + "head_text": "Inditex, has", + "tail_text": "A Coruna." + }, + { + "label": "person is ceo of organization", + "score": 0.0049, + "head_text": "Inditex has", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "person is ceo of organization", + "score": 0.0048, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Telefonica, in" + }, + { + "label": "organization headquartered in location", + "score": 0.0046, + "head_text": "Madrid last", + "tail_text": "Telefonica, in" + }, + { + "label": "organization is located in location", + "score": 0.0046, + "head_text": "Arteixo, A", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "organization headquartered in location", + "score": 0.0045, + "head_text": "Inditex has", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "organization is located in location", + "score": 0.0045, + "head_text": "Telefonica, in", + "tail_text": "Pablo Isla, the" + }, + { + "label": "organization is located in location", + "score": 0.0045, + "head_text": "Telefonica, in", + "tail_text": "A Coruna." + }, + { + "label": "person works at organization", + "score": 0.0044, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Telefonica, in" + }, + { + "label": "organization is located in location", + "score": 0.0043, + "head_text": "Pablo Isla, the", + "tail_text": "Inditex, has" + }, + { + "label": "organization is located in location", + "score": 0.0043, + "head_text": "A Coruna.", + "tail_text": "Inditex, has" + }, + { + "label": "organization is located in location", + "score": 0.0043, + "head_text": "Telefonica. The", + "tail_text": "Telefonica, in" + }, + { + "label": "organization is located in location", + "score": 0.0043, + "head_text": "Inditex, has", + "tail_text": "Telefonica. The" + }, + { + "label": "person is ceo of organization", + "score": 0.0042, + "head_text": "Pablo Isla, the", + "tail_text": "Madrid last" + }, + { + "label": "person is ceo of organization", + "score": 0.0042, + "head_text": "A Coruna.", + "tail_text": "Madrid last" + }, + { + "label": "organization is located in location", + "score": 0.0042, + "head_text": "Inditex, has", + "tail_text": "Pablo Isla, the" + }, + { + "label": "organization is located in location", + "score": 0.0042, + "head_text": "Inditex, has", + "tail_text": "A Coruna." + }, + { + "label": "person appointed as role at organization", + "score": 0.0042, + "head_text": "Arteixo, A", + "tail_text": "Pablo Isla, the" + }, + { + "label": "person appointed as role at organization", + "score": 0.0042, + "head_text": "Arteixo, A", + "tail_text": "A Coruna." + }, + { + "label": "person works at organization", + "score": 0.0041, + "head_text": "Madrid last", + "tail_text": "Telefonica. The" + }, + { + "label": "organization headquartered in location", + "score": 0.004, + "head_text": "Telefonica. The", + "tail_text": "Madrid last" + }, + { + "label": "organization headquartered in location", + "score": 0.0039, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Telefonica, in" + }, + { + "label": "organization headquartered in location", + "score": 0.0039, + "head_text": "Madrid last", + "tail_text": "Telefonica. The" + }, + { + "label": "person is ceo of organization", + "score": 0.0039, + "head_text": "Inditex, has", + "tail_text": "Madrid last" + }, + { + "label": "person appointed as role at organization", + "score": 0.0039, + "head_text": "Telefonica. The", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "person appointed as role at organization", + "score": 0.0038, + "head_text": "Madrid last", + "tail_text": "Inditex has" + }, + { + "label": "organization is located in location", + "score": 0.0038, + "head_text": "Madrid last", + "tail_text": "Pablo Isla, the" + }, + { + "label": "organization is located in location", + "score": 0.0038, + "head_text": "Madrid last", + "tail_text": "A Coruna." + }, + { + "label": "organization is located in location", + "score": 0.0038, + "head_text": "Telefonica, in", + "tail_text": "Telefonica. The" + }, + { + "label": "organization headquartered in location", + "score": 0.0037, + "head_text": "Telefonica, in", + "tail_text": "Madrid last" + }, + { + "label": "person works at organization", + "score": 0.0037, + "head_text": "Arteixo, A", + "tail_text": "Pablo Isla, the" + }, + { + "label": "person works at organization", + "score": 0.0037, + "head_text": "Arteixo, A", + "tail_text": "A Coruna." + }, + { + "label": "organization headquartered in location", + "score": 0.0037, + "head_text": "Pablo Isla, the", + "tail_text": "Madrid last" + }, + { + "label": "organization headquartered in location", + "score": 0.0037, + "head_text": "A Coruna.", + "tail_text": "Madrid last" + }, + { + "label": "organization headquartered in location", + "score": 0.0036, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Pablo Isla, the" + }, + { + "label": "organization headquartered in location", + "score": 0.0036, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "A Coruna." + }, + { + "label": "organization headquartered in location", + "score": 0.0035, + "head_text": "Pablo Isla, the", + "tail_text": "Telefonica, in" + }, + { + "label": "organization headquartered in location", + "score": 0.0035, + "head_text": "A Coruna.", + "tail_text": "Telefonica, in" + }, + { + "label": "person works at organization", + "score": 0.0033, + "head_text": "Telefonica. The", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "person works at organization", + "score": 0.0032, + "head_text": "Madrid last", + "tail_text": "Inditex has" + }, + { + "label": "person is ceo of organization", + "score": 0.0032, + "head_text": "Telefonica. The", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "organization headquartered in location", + "score": 0.0032, + "head_text": "Telefonica. The", + "tail_text": "Pablo Isla, the" + }, + { + "label": "organization headquartered in location", + "score": 0.0032, + "head_text": "Telefonica. The", + "tail_text": "A Coruna." + }, + { + "label": "person is ceo of organization", + "score": 0.0031, + "head_text": "Madrid last", + "tail_text": "Telefonica. The" + }, + { + "label": "person appointed as role at organization", + "score": 0.0031, + "head_text": "Telefonica. The", + "tail_text": "Madrid last" + }, + { + "label": "organization headquartered in location", + "score": 0.0031, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Telefonica. The" + }, + { + "label": "organization headquartered in location", + "score": 0.0029, + "head_text": "Inditex, has", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "person works at organization", + "score": 0.0028, + "head_text": "Arteixo, A", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "person appointed as role at organization", + "score": 0.0028, + "head_text": "Telefonica, in", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "person is ceo of organization", + "score": 0.0027, + "head_text": "Arteixo, A", + "tail_text": "Pablo Isla, the" + }, + { + "label": "person is ceo of organization", + "score": 0.0027, + "head_text": "Arteixo, A", + "tail_text": "A Coruna." + }, + { + "label": "organization headquartered in location", + "score": 0.0027, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Madrid last" + }, + { + "label": "person appointed as role at organization", + "score": 0.0026, + "head_text": "Arteixo, A", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "organization is located in location", + "score": 0.0026, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Pablo Isla, the" + }, + { + "label": "organization is located in location", + "score": 0.0026, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "A Coruna." + }, + { + "label": "organization is located in location", + "score": 0.0026, + "head_text": "Inditex has", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "person appointed as role at organization", + "score": 0.0025, + "head_text": "Arteixo, A", + "tail_text": "Telefonica. The" + }, + { + "label": "organization is located in location", + "score": 0.0025, + "head_text": "Telefonica. The", + "tail_text": "Pablo Isla, the" + }, + { + "label": "organization is located in location", + "score": 0.0025, + "head_text": "Telefonica. The", + "tail_text": "A Coruna." + }, + { + "label": "person appointed as role at organization", + "score": 0.0025, + "head_text": "Arteixo, A", + "tail_text": "Inditex, has" + }, + { + "label": "organization is located in location", + "score": 0.0025, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Telefonica, in" + }, + { + "label": "person works at organization", + "score": 0.0025, + "head_text": "Telefonica, in", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "person works at organization", + "score": 0.0024, + "head_text": "Telefonica. The", + "tail_text": "Madrid last" + }, + { + "label": "person is ceo of organization", + "score": 0.0024, + "head_text": "Telefonica, in", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "organization is located in location", + "score": 0.0024, + "head_text": "Madrid last", + "tail_text": "Telefonica, in" + }, + { + "label": "person is ceo of organization", + "score": 0.0024, + "head_text": "Madrid last", + "tail_text": "Inditex has" + }, + { + "label": "organization is located in location", + "score": 0.0024, + "head_text": "Telefonica. The", + "tail_text": "Madrid last" + }, + { + "label": "organization is located in location", + "score": 0.0024, + "head_text": "Pablo Isla, the", + "tail_text": "Telefonica, in" + }, + { + "label": "organization is located in location", + "score": 0.0024, + "head_text": "A Coruna.", + "tail_text": "Telefonica, in" + }, + { + "label": "person appointed as role at organization", + "score": 0.0023, + "head_text": "Madrid last", + "tail_text": "Telefonica, in" + }, + { + "label": "person appointed as role at organization", + "score": 0.0023, + "head_text": "Inditex has", + "tail_text": "Madrid last" + }, + { + "label": "person works at organization", + "score": 0.0023, + "head_text": "Arteixo, A", + "tail_text": "Inditex, has" + }, + { + "label": "person works at organization", + "score": 0.0022, + "head_text": "Arteixo, A", + "tail_text": "Telefonica. The" + }, + { + "label": "person is ceo of organization", + "score": 0.0022, + "head_text": "Arteixo, A", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "organization is located in location", + "score": 0.0022, + "head_text": "Madrid last", + "tail_text": "Telefonica. The" + }, + { + "label": "organization is located in location", + "score": 0.0022, + "head_text": "Pablo Isla, the", + "tail_text": "Madrid last" + }, + { + "label": "organization is located in location", + "score": 0.0022, + "head_text": "A Coruna.", + "tail_text": "Madrid last" + }, + { + "label": "organization headquartered in location", + "score": 0.0021, + "head_text": "Pablo Isla, the", + "tail_text": "A Coruna." + }, + { + "label": "organization headquartered in location", + "score": 0.0021, + "head_text": "A Coruna.", + "tail_text": "Pablo Isla, the" + }, + { + "label": "person is ceo of organization", + "score": 0.0021, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Arteixo, A" + }, + { + "label": "organization is located in location", + "score": 0.0021, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Telefonica. The" + }, + { + "label": "person appointed as role at organization", + "score": 0.0021, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Madrid last" + }, + { + "label": "organization is located in location", + "score": 0.0021, + "head_text": "Telefonica, in", + "tail_text": "Madrid last" + }, + { + "label": "person appointed as role at organization", + "score": 0.0021, + "head_text": "Madrid last", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "person works at organization", + "score": 0.002, + "head_text": "Inditex has", + "tail_text": "Madrid last" + }, + { + "label": "person works at organization", + "score": 0.002, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Arteixo, A" + }, + { + "label": "person is ceo of organization", + "score": 0.0019, + "head_text": "Telefonica. The", + "tail_text": "Madrid last" + }, + { + "label": "person works at organization", + "score": 0.0019, + "head_text": "Arteixo, A", + "tail_text": "Madrid last" + }, + { + "label": "person works at organization", + "score": 0.0019, + "head_text": "Madrid last", + "tail_text": "Telefonica, in" + }, + { + "label": "person appointed as role at organization", + "score": 0.0019, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Arteixo, A" + }, + { + "label": "person works at organization", + "score": 0.0019, + "head_text": "Madrid last", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "person appointed as role at organization", + "score": 0.0018, + "head_text": "Arteixo, A", + "tail_text": "Madrid last" + }, + { + "label": "person works at organization", + "score": 0.0018, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Madrid last" + }, + { + "label": "organization is located in location", + "score": 0.0018, + "head_text": "Pablo Isla, the", + "tail_text": "A Coruna." + }, + { + "label": "organization is located in location", + "score": 0.0018, + "head_text": "A Coruna.", + "tail_text": "Pablo Isla, the" + }, + { + "label": "person appointed as role at organization", + "score": 0.0017, + "head_text": "Pablo Isla, the", + "tail_text": "Arteixo, A" + }, + { + "label": "person appointed as role at organization", + "score": 0.0017, + "head_text": "A Coruna.", + "tail_text": "Arteixo, A" + }, + { + "label": "person appointed as role at organization", + "score": 0.0017, + "head_text": "Inditex, has", + "tail_text": "Arteixo, A" + }, + { + "label": "organization is located in location", + "score": 0.0017, + "head_text": "Inditex, has", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "person is ceo of organization", + "score": 0.0017, + "head_text": "Arteixo, A", + "tail_text": "Telefonica. The" + }, + { + "label": "organization headquartered in location", + "score": 0.0017, + "head_text": "Telefonica, in", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "organization headquartered in location", + "score": 0.0017, + "head_text": "Pablo Isla, the", + "tail_text": "Telefonica. The" + }, + { + "label": "organization headquartered in location", + "score": 0.0017, + "head_text": "A Coruna.", + "tail_text": "Telefonica. The" + }, + { + "label": "person works at organization", + "score": 0.0016, + "head_text": "Inditex, has", + "tail_text": "Arteixo, A" + }, + { + "label": "organization headquartered in location", + "score": 0.0016, + "head_text": "Madrid last", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "person is ceo of organization", + "score": 0.0016, + "head_text": "Arteixo, A", + "tail_text": "Inditex, has" + }, + { + "label": "person appointed as role at organization", + "score": 0.0016, + "head_text": "Arteixo, A", + "tail_text": "Telefonica, in" + }, + { + "label": "person works at organization", + "score": 0.0015, + "head_text": "Pablo Isla, the", + "tail_text": "Arteixo, A" + }, + { + "label": "person works at organization", + "score": 0.0015, + "head_text": "A Coruna.", + "tail_text": "Arteixo, A" + }, + { + "label": "organization is located in location", + "score": 0.0015, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Madrid last" + }, + { + "label": "organization headquartered in location", + "score": 0.0015, + "head_text": "Pablo Isla, the", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "organization headquartered in location", + "score": 0.0015, + "head_text": "A Coruna.", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "organization headquartered in location", + "score": 0.0015, + "head_text": "Telefonica. The", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "person is ceo of organization", + "score": 0.0015, + "head_text": "Madrid last", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "person works at organization", + "score": 0.0015, + "head_text": "Arteixo, A", + "tail_text": "Telefonica, in" + }, + { + "label": "person is ceo of organization", + "score": 0.0015, + "head_text": "Jose Maria Alvarez-Pallete, the", + "tail_text": "Madrid last" + }, + { + "label": "person appointed as role at organization", + "score": 0.0014, + "head_text": "Telefonica, in", + "tail_text": "Madrid last" + }, + { + "label": "person is ceo of organization", + "score": 0.0014, + "head_text": "Pablo Isla, the", + "tail_text": "Arteixo, A" + }, + { + "label": "person is ceo of organization", + "score": 0.0014, + "head_text": "A Coruna.", + "tail_text": "Arteixo, A" + }, + { + "label": "person is ceo of organization", + "score": 0.0014, + "head_text": "Inditex, has", + "tail_text": "Arteixo, A" + }, + { + "label": "person appointed as role at organization", + "score": 0.0014, + "head_text": "Telefonica. The", + "tail_text": "Arteixo, A" + }, + { + "label": "person is ceo of organization", + "score": 0.0014, + "head_text": "Madrid last", + "tail_text": "Telefonica, in" + }, + { + "label": "person is ceo of organization", + "score": 0.0013, + "head_text": "Inditex has", + "tail_text": "Madrid last" + }, + { + "label": "person is ceo of organization", + "score": 0.0013, + "head_text": "Telefonica. The", + "tail_text": "Arteixo, A" + }, + { + "label": "person works at organization", + "score": 0.0013, + "head_text": "Inditex has", + "tail_text": "Arteixo, A" + }, + { + "label": "person works at organization", + "score": 0.0013, + "head_text": "Arteixo, A", + "tail_text": "Inditex has" + }, + { + "label": "person works at organization", + "score": 0.0013, + "head_text": "Madrid last", + "tail_text": "Arteixo, A" + }, + { + "label": "person appointed as role at organization", + "score": 0.0012, + "head_text": "Arteixo, A", + "tail_text": "Inditex has" + }, + { + "label": "person works at organization", + "score": 0.0012, + "head_text": "Telefonica. The", + "tail_text": "Arteixo, A" + }, + { + "label": "person appointed as role at organization", + "score": 0.0012, + "head_text": "Inditex has", + "tail_text": "Arteixo, A" + }, + { + "label": "person appointed as role at organization", + "score": 0.0012, + "head_text": "Madrid last", + "tail_text": "Arteixo, A" + }, + { + "label": "person works at organization", + "score": 0.0012, + "head_text": "Telefonica, in", + "tail_text": "Madrid last" + }, + { + "label": "organization is located in location", + "score": 0.0012, + "head_text": "Pablo Isla, the", + "tail_text": "Telefonica. The" + }, + { + "label": "organization is located in location", + "score": 0.0012, + "head_text": "A Coruna.", + "tail_text": "Telefonica. The" + }, + { + "label": "person is ceo of organization", + "score": 0.0012, + "head_text": "Arteixo, A", + "tail_text": "Madrid last" + }, + { + "label": "person is ceo of organization", + "score": 0.0011, + "head_text": "Arteixo, A", + "tail_text": "Telefonica, in" + }, + { + "label": "person appointed as role at organization", + "score": 0.0011, + "head_text": "Telefonica, in", + "tail_text": "Arteixo, A" + }, + { + "label": "person is ceo of organization", + "score": 0.001, + "head_text": "Inditex has", + "tail_text": "Arteixo, A" + }, + { + "label": "person works at organization", + "score": 0.001, + "head_text": "Telefonica, in", + "tail_text": "Arteixo, A" + }, + { + "label": "person is ceo of organization", + "score": 0.001, + "head_text": "Madrid last", + "tail_text": "Arteixo, A" + }, + { + "label": "organization is located in location", + "score": 0.001, + "head_text": "Telefonica, in", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "person is ceo of organization", + "score": 0.001, + "head_text": "Telefonica, in", + "tail_text": "Arteixo, A" + }, + { + "label": "organization is located in location", + "score": 0.0009, + "head_text": "Pablo Isla, the", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "organization is located in location", + "score": 0.0009, + "head_text": "A Coruna.", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "organization is located in location", + "score": 0.0009, + "head_text": "Telefonica. The", + "tail_text": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "person is ceo of organization", + "score": 0.0009, + "head_text": "Telefonica, in", + "tail_text": "Madrid last" + }, + { + "label": "person is ceo of organization", + "score": 0.0009, + "head_text": "Arteixo, A", + "tail_text": "Inditex has" + }, + { + "label": "organization is located in location", + "score": 0.0008, + "head_text": "Madrid last", + "tail_text": "Jose Maria Alvarez-Pallete, the" + } + ] + } + }, + "en_osint": { + "entities": [], + "note": "too few entities" + }, + "es_journalism": { + "entities": [ + [ + "Iberdrola", + "organizacion", + 0.992 + ], + [ + "Endesa", + "organizacion", + 0.987 + ], + [ + "Galicia", + "lugar", + 0.861 + ], + [ + "Iberdrola", + "organizacion", + 0.987 + ], + [ + "Ignacio Galan", + "persona", + 0.981 + ], + [ + "Endesa", + "organizacion", + 0.983 + ], + [ + "Marina Serrano", + "persona", + 0.975 + ], + [ + "Bilbao", + "lugar", + 0.974 + ] + ], + "ner": [ + [ + 0, + 1, + "organizacion" + ], + [ + 2, + 3, + "organizacion" + ], + [ + 12, + 13, + "lugar" + ], + [ + 16, + 17, + "organizacion" + ], + [ + 17, + 19, + "persona" + ], + [ + 25, + 26, + "organizacion" + ], + [ + 26, + 28, + "persona" + ], + [ + 29, + 30, + "lugar" + ] + ], + "styles": { + "snake_short": [ + { + "label": "ceo_of", + "score": 0.1954, + "head_text": "Marina Serrano, en", + "tail_text": "Galicia. El" + }, + { + "label": "located_in", + "score": 0.1891, + "head_text": "Bilbao. El", + "tail_text": "Galicia. El" + }, + { + "label": "ceo_of", + "score": 0.1869, + "head_text": "Marina Serrano, en", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "ceo_of", + "score": 0.1719, + "head_text": "Galicia. El", + "tail_text": "Marina Serrano, en" + }, + { + "label": "ceo_of", + "score": 0.1714, + "head_text": "Marina Serrano, en", + "tail_text": "Iberdrola y" + }, + { + "label": "ceo_of", + "score": 0.1707, + "head_text": "Endesa, Marina", + "tail_text": "Marina Serrano, en" + }, + { + "label": "ceo_of", + "score": 0.1688, + "head_text": "Marina Serrano, en", + "tail_text": "Endesa, Marina" + }, + { + "label": "ceo_of", + "score": 0.1686, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Marina Serrano, en" + }, + { + "label": "located_in", + "score": 0.1644, + "head_text": "Iberdrola y", + "tail_text": "Galicia. El" + }, + { + "label": "headquartered_in", + "score": 0.1635, + "head_text": "Bilbao. El", + "tail_text": "Galicia. El" + }, + { + "label": "ceo_of", + "score": 0.1629, + "head_text": "Marina Serrano, en", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "ceo_of", + "score": 0.1538, + "head_text": "Ignacio Galan, se", + "tail_text": "Marina Serrano, en" + }, + { + "label": "ceo_of", + "score": 0.1522, + "head_text": "Iberdrola y", + "tail_text": "Marina Serrano, en" + }, + { + "label": "located_in", + "score": 0.1486, + "head_text": "Galicia. El", + "tail_text": "Iberdrola y" + }, + { + "label": "headquartered_in", + "score": 0.1386, + "head_text": "Iberdrola y", + "tail_text": "Galicia. El" + }, + { + "label": "located_in", + "score": 0.1349, + "head_text": "Endesa firmaron", + "tail_text": "Galicia. El" + }, + { + "label": "located_in", + "score": 0.1234, + "head_text": "Ignacio Galan, se", + "tail_text": "Galicia. El" + }, + { + "label": "located_in", + "score": 0.1234, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Galicia. El" + }, + { + "label": "headquartered_in", + "score": 0.1213, + "head_text": "Galicia. El", + "tail_text": "Iberdrola y" + }, + { + "label": "located_in", + "score": 0.1187, + "head_text": "Galicia. El", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "headquartered_in", + "score": 0.1139, + "head_text": "Ignacio Galan, se", + "tail_text": "Galicia. El" + }, + { + "label": "headquartered_in", + "score": 0.1096, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Galicia. El" + }, + { + "label": "ceo_of", + "score": 0.1069, + "head_text": "Bilbao. El", + "tail_text": "Marina Serrano, en" + }, + { + "label": "headquartered_in", + "score": 0.1064, + "head_text": "Endesa firmaron", + "tail_text": "Galicia. El" + }, + { + "label": "headquartered_in", + "score": 0.1008, + "head_text": "Galicia. El", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "ceo_of", + "score": 0.1006, + "head_text": "Endesa, Marina", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "ceo_of", + "score": 0.0937, + "head_text": "Ignacio Galan, se", + "tail_text": "Endesa, Marina" + }, + { + "label": "located_in", + "score": 0.0919, + "head_text": "Galicia. El", + "tail_text": "Bilbao. El" + }, + { + "label": "located_in", + "score": 0.0854, + "head_text": "Endesa, Marina", + "tail_text": "Galicia. El" + }, + { + "label": "ceo_of", + "score": 0.0845, + "head_text": "Marina Serrano, en", + "tail_text": "Bilbao. El" + }, + { + "label": "headquartered_in", + "score": 0.0799, + "head_text": "Galicia. El", + "tail_text": "Bilbao. El" + }, + { + "label": "located_in", + "score": 0.0798, + "head_text": "Marina Serrano, en", + "tail_text": "Galicia. El" + }, + { + "label": "located_in", + "score": 0.0781, + "head_text": "Galicia. El", + "tail_text": "Endesa firmaron" + }, + { + "label": "headquartered_in", + "score": 0.0764, + "head_text": "Endesa, Marina", + "tail_text": "Galicia. El" + }, + { + "label": "ceo_of", + "score": 0.0764, + "head_text": "Endesa, Marina", + "tail_text": "Galicia. El" + }, + { + "label": "ceo_of", + "score": 0.073, + "head_text": "Marina Serrano, en", + "tail_text": "Endesa firmaron" + }, + { + "label": "ceo_of", + "score": 0.0729, + "head_text": "Endesa firmaron", + "tail_text": "Marina Serrano, en" + }, + { + "label": "headquartered_in", + "score": 0.0724, + "head_text": "Marina Serrano, en", + "tail_text": "Galicia. El" + }, + { + "label": "ceo_of", + "score": 0.0714, + "head_text": "Bilbao. El", + "tail_text": "Endesa, Marina" + }, + { + "label": "ceo_of", + "score": 0.0712, + "head_text": "Endesa, Marina", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "ceo_of", + "score": 0.0692, + "head_text": "Galicia. El", + "tail_text": "Endesa, Marina" + }, + { + "label": "located_in", + "score": 0.0662, + "head_text": "Galicia. El", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "ceo_of", + "score": 0.0656, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Endesa, Marina" + }, + { + "label": "headquartered_in", + "score": 0.0622, + "head_text": "Galicia. El", + "tail_text": "Endesa firmaron" + }, + { + "label": "headquartered_in", + "score": 0.0597, + "head_text": "Galicia. El", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "ceo_of", + "score": 0.0591, + "head_text": "Ignacio Galan, se", + "tail_text": "Galicia. El" + }, + { + "label": "ceo_of", + "score": 0.0577, + "head_text": "Bilbao. El", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "ceo_of", + "score": 0.0553, + "head_text": "Galicia. El", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "ceo_of", + "score": 0.0517, + "head_text": "Endesa, Marina", + "tail_text": "Iberdrola y" + }, + { + "label": "ceo_of", + "score": 0.0489, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "ceo_of", + "score": 0.0478, + "head_text": "Iberdrola y", + "tail_text": "Endesa, Marina" + }, + { + "label": "ceo_of", + "score": 0.0463, + "head_text": "Endesa, Marina", + "tail_text": "Bilbao. El" + }, + { + "label": "located_in", + "score": 0.0448, + "head_text": "Bilbao. El", + "tail_text": "Iberdrola y" + }, + { + "label": "ceo_of", + "score": 0.0441, + "head_text": "Ignacio Galan, se", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "headquartered_in", + "score": 0.042, + "head_text": "Bilbao. El", + "tail_text": "Iberdrola y" + }, + { + "label": "ceo_of", + "score": 0.0392, + "head_text": "Iberdrola y", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "located_in", + "score": 0.0392, + "head_text": "Bilbao. El", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "located_in", + "score": 0.0387, + "head_text": "Galicia. El", + "tail_text": "Endesa, Marina" + }, + { + "label": "headquartered_in", + "score": 0.038, + "head_text": "Bilbao. El", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "ceo_of", + "score": 0.0369, + "head_text": "Ignacio Galan, se", + "tail_text": "Bilbao. El" + }, + { + "label": "ceo_of", + "score": 0.0364, + "head_text": "Ignacio Galan, se", + "tail_text": "Iberdrola y" + }, + { + "label": "headquartered_in", + "score": 0.0338, + "head_text": "Galicia. El", + "tail_text": "Endesa, Marina" + }, + { + "label": "appointed_as", + "score": 0.0284, + "head_text": "Bilbao. El", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "works_at", + "score": 0.0275, + "head_text": "Marina Serrano, en", + "tail_text": "Galicia. El" + }, + { + "label": "ceo_of", + "score": 0.0266, + "head_text": "Bilbao. El", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "appointed_as", + "score": 0.0254, + "head_text": "Galicia. El", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "works_at", + "score": 0.0246, + "head_text": "Galicia. El", + "tail_text": "Marina Serrano, en" + }, + { + "label": "located_in", + "score": 0.0241, + "head_text": "Galicia. El", + "tail_text": "Marina Serrano, en" + }, + { + "label": "appointed_as", + "score": 0.024, + "head_text": "Ignacio Galan, se", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "appointed_as", + "score": 0.024, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "appointed_as", + "score": 0.0233, + "head_text": "Ignacio Galan, se", + "tail_text": "Iberdrola y" + }, + { + "label": "ceo_of", + "score": 0.0228, + "head_text": "Bilbao. El", + "tail_text": "Galicia. El" + }, + { + "label": "appointed_as", + "score": 0.0226, + "head_text": "Iberdrola y", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "appointed_as", + "score": 0.0226, + "head_text": "Marina Serrano, en", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "appointed_as", + "score": 0.0219, + "head_text": "Ignacio Galan, se", + "tail_text": "Galicia. El" + }, + { + "label": "headquartered_in", + "score": 0.0219, + "head_text": "Galicia. El", + "tail_text": "Marina Serrano, en" + }, + { + "label": "located_in", + "score": 0.0214, + "head_text": "Iberdrola y", + "tail_text": "Bilbao. El" + }, + { + "label": "works_at", + "score": 0.0213, + "head_text": "Bilbao. El", + "tail_text": "Marina Serrano, en" + }, + { + "label": "appointed_as", + "score": 0.0213, + "head_text": "Endesa, Marina", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "works_at", + "score": 0.0212, + "head_text": "Marina Serrano, en", + "tail_text": "Bilbao. El" + }, + { + "label": "headquartered_in", + "score": 0.0209, + "head_text": "Iberdrola y", + "tail_text": "Bilbao. El" + }, + { + "label": "appointed_as", + "score": 0.0208, + "head_text": "Bilbao. El", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "located_in", + "score": 0.0196, + "head_text": "Bilbao. El", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "headquartered_in", + "score": 0.0195, + "head_text": "Bilbao. El", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "appointed_as", + "score": 0.0193, + "head_text": "Marina Serrano, en", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "appointed_as", + "score": 0.0187, + "head_text": "Ignacio Galan, se", + "tail_text": "Endesa, Marina" + }, + { + "label": "ceo_of", + "score": 0.0187, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Bilbao. El" + }, + { + "label": "appointed_as", + "score": 0.0177, + "head_text": "Bilbao. El", + "tail_text": "Iberdrola y" + }, + { + "label": "headquartered_in", + "score": 0.0173, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Bilbao. El" + }, + { + "label": "works_at", + "score": 0.0168, + "head_text": "Bilbao. El", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "located_in", + "score": 0.0168, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Bilbao. El" + }, + { + "label": "appointed_as", + "score": 0.0167, + "head_text": "Marina Serrano, en", + "tail_text": "Iberdrola y" + }, + { + "label": "appointed_as", + "score": 0.0164, + "head_text": "Bilbao. El", + "tail_text": "Endesa, Marina" + }, + { + "label": "appointed_as", + "score": 0.0162, + "head_text": "Bilbao. El", + "tail_text": "Galicia. El" + }, + { + "label": "appointed_as", + "score": 0.0162, + "head_text": "Ignacio Galan, se", + "tail_text": "Bilbao. El" + }, + { + "label": "works_at", + "score": 0.016, + "head_text": "Marina Serrano, en", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "ceo_of", + "score": 0.016, + "head_text": "Endesa firmaron", + "tail_text": "Endesa, Marina" + }, + { + "label": "appointed_as", + "score": 0.016, + "head_text": "Ignacio Galan, se", + "tail_text": "Marina Serrano, en" + }, + { + "label": "appointed_as", + "score": 0.016, + "head_text": "Marina Serrano, en", + "tail_text": "Galicia. El" + }, + { + "label": "works_at", + "score": 0.0159, + "head_text": "Bilbao. El", + "tail_text": "Galicia. El" + }, + { + "label": "appointed_as", + "score": 0.0158, + "head_text": "Marina Serrano, en", + "tail_text": "Endesa firmaron" + }, + { + "label": "ceo_of", + "score": 0.0157, + "head_text": "Bilbao. El", + "tail_text": "Iberdrola y" + }, + { + "label": "ceo_of", + "score": 0.0154, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Galicia. El" + }, + { + "label": "appointed_as", + "score": 0.0154, + "head_text": "Galicia. El", + "tail_text": "Marina Serrano, en" + }, + { + "label": "ceo_of", + "score": 0.0152, + "head_text": "Galicia. El", + "tail_text": "Bilbao. El" + }, + { + "label": "located_in", + "score": 0.0148, + "head_text": "Bilbao. El", + "tail_text": "Endesa firmaron" + }, + { + "label": "located_in", + "score": 0.0148, + "head_text": "Bilbao. El", + "tail_text": "Endesa, Marina" + }, + { + "label": "works_at", + "score": 0.0147, + "head_text": "Galicia. El", + "tail_text": "Bilbao. El" + }, + { + "label": "works_at", + "score": 0.0145, + "head_text": "Ignacio Galan, se", + "tail_text": "Marina Serrano, en" + }, + { + "label": "headquartered_in", + "score": 0.0143, + "head_text": "Bilbao. El", + "tail_text": "Endesa, Marina" + }, + { + "label": "ceo_of", + "score": 0.014, + "head_text": "Galicia. El", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "appointed_as", + "score": 0.014, + "head_text": "Endesa, Marina", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "headquartered_in", + "score": 0.0139, + "head_text": "Ignacio Galan, se", + "tail_text": "Bilbao. El" + }, + { + "label": "appointed_as", + "score": 0.0138, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Marina Serrano, en" + }, + { + "label": "located_in", + "score": 0.0137, + "head_text": "Iberdrola y", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "appointed_as", + "score": 0.0136, + "head_text": "Galicia. El", + "tail_text": "Bilbao. El" + }, + { + "label": "ceo_of", + "score": 0.0135, + "head_text": "Endesa, Marina", + "tail_text": "Endesa firmaron" + }, + { + "label": "located_in", + "score": 0.0133, + "head_text": "Ignacio Galan, se", + "tail_text": "Bilbao. El" + }, + { + "label": "appointed_as", + "score": 0.0132, + "head_text": "Marina Serrano, en", + "tail_text": "Endesa, Marina" + }, + { + "label": "appointed_as", + "score": 0.0132, + "head_text": "Bilbao. El", + "tail_text": "Marina Serrano, en" + }, + { + "label": "works_at", + "score": 0.0131, + "head_text": "Marina Serrano, en", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "headquartered_in", + "score": 0.013, + "head_text": "Bilbao. El", + "tail_text": "Endesa firmaron" + }, + { + "label": "appointed_as", + "score": 0.0128, + "head_text": "Iberdrola y", + "tail_text": "Marina Serrano, en" + }, + { + "label": "works_at", + "score": 0.0127, + "head_text": "Endesa, Marina", + "tail_text": "Marina Serrano, en" + }, + { + "label": "works_at", + "score": 0.0126, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Marina Serrano, en" + }, + { + "label": "headquartered_in", + "score": 0.0124, + "head_text": "Iberdrola y", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "located_in", + "score": 0.0124, + "head_text": "Ignacio Galan, se", + "tail_text": "Iberdrola y" + }, + { + "label": "appointed_as", + "score": 0.0123, + "head_text": "Ignacio Galan, se", + "tail_text": "Endesa firmaron" + }, + { + "label": "headquartered_in", + "score": 0.0122, + "head_text": "Ignacio Galan, se", + "tail_text": "Iberdrola y" + }, + { + "label": "appointed_as", + "score": 0.0122, + "head_text": "Galicia. El", + "tail_text": "Endesa, Marina" + }, + { + "label": "works_at", + "score": 0.0122, + "head_text": "Ignacio Galan, se", + "tail_text": "Bilbao. El" + }, + { + "label": "located_in", + "score": 0.0119, + "head_text": "Endesa firmaron", + "tail_text": "Bilbao. El" + }, + { + "label": "located_in", + "score": 0.0119, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Iberdrola y" + }, + { + "label": "appointed_as", + "score": 0.0117, + "head_text": "Endesa firmaron", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "works_at", + "score": 0.0117, + "head_text": "Bilbao. El", + "tail_text": "Endesa, Marina" + }, + { + "label": "appointed_as", + "score": 0.0116, + "head_text": "Endesa, Marina", + "tail_text": "Marina Serrano, en" + }, + { + "label": "appointed_as", + "score": 0.0116, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Endesa, Marina" + }, + { + "label": "appointed_as", + "score": 0.0116, + "head_text": "Galicia. El", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "ceo_of", + "score": 0.0116, + "head_text": "Iberdrola y", + "tail_text": "Bilbao. El" + }, + { + "label": "appointed_as", + "score": 0.0115, + "head_text": "Marina Serrano, en", + "tail_text": "Bilbao. El" + }, + { + "label": "works_at", + "score": 0.0115, + "head_text": "Iberdrola y", + "tail_text": "Marina Serrano, en" + }, + { + "label": "appointed_as", + "score": 0.0113, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Bilbao. El" + }, + { + "label": "works_at", + "score": 0.0113, + "head_text": "Marina Serrano, en", + "tail_text": "Endesa, Marina" + }, + { + "label": "appointed_as", + "score": 0.011, + "head_text": "Endesa, Marina", + "tail_text": "Galicia. El" + }, + { + "label": "headquartered_in", + "score": 0.011, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Iberdrola y" + }, + { + "label": "appointed_as", + "score": 0.0109, + "head_text": "Endesa firmaron", + "tail_text": "Marina Serrano, en" + }, + { + "label": "ceo_of", + "score": 0.0109, + "head_text": "Endesa firmaron", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "works_at", + "score": 0.0106, + "head_text": "Marina Serrano, en", + "tail_text": "Iberdrola y" + }, + { + "label": "located_in", + "score": 0.0105, + "head_text": "Ignacio Galan, se", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "headquartered_in", + "score": 0.0105, + "head_text": "Ignacio Galan, se", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "headquartered_in", + "score": 0.0104, + "head_text": "Endesa firmaron", + "tail_text": "Bilbao. El" + }, + { + "label": "located_in", + "score": 0.0103, + "head_text": "Iberdrola y", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "located_in", + "score": 0.0103, + "head_text": "Endesa firmaron", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "appointed_as", + "score": 0.0102, + "head_text": "Endesa, Marina", + "tail_text": "Iberdrola y" + }, + { + "label": "headquartered_in", + "score": 0.0101, + "head_text": "Iberdrola y", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "works_at", + "score": 0.0101, + "head_text": "Galicia. El", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "appointed_as", + "score": 0.01, + "head_text": "Endesa, Marina", + "tail_text": "Bilbao. El" + }, + { + "label": "headquartered_in", + "score": 0.0099, + "head_text": "Endesa, Marina", + "tail_text": "Bilbao. El" + }, + { + "label": "located_in", + "score": 0.0098, + "head_text": "Marina Serrano, en", + "tail_text": "Bilbao. El" + }, + { + "label": "headquartered_in", + "score": 0.0098, + "head_text": "Marina Serrano, en", + "tail_text": "Bilbao. El" + }, + { + "label": "works_at", + "score": 0.0098, + "head_text": "Endesa, Marina", + "tail_text": "Bilbao. El" + }, + { + "label": "appointed_as", + "score": 0.0098, + "head_text": "Iberdrola y", + "tail_text": "Bilbao. El" + }, + { + "label": "located_in", + "score": 0.0098, + "head_text": "Marina Serrano, en", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "appointed_as", + "score": 0.0097, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Iberdrola y" + }, + { + "label": "headquartered_in", + "score": 0.0097, + "head_text": "Marina Serrano, en", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "located_in", + "score": 0.0097, + "head_text": "Endesa, Marina", + "tail_text": "Bilbao. El" + }, + { + "label": "located_in", + "score": 0.0097, + "head_text": "Marina Serrano, en", + "tail_text": "Iberdrola y" + }, + { + "label": "headquartered_in", + "score": 0.0095, + "head_text": "Marina Serrano, en", + "tail_text": "Iberdrola y" + }, + { + "label": "appointed_as", + "score": 0.0095, + "head_text": "Iberdrola y", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "appointed_as", + "score": 0.0095, + "head_text": "Iberdrola y", + "tail_text": "Endesa, Marina" + }, + { + "label": "works_at", + "score": 0.0093, + "head_text": "Ignacio Galan, se", + "tail_text": "Galicia. El" + }, + { + "label": "appointed_as", + "score": 0.0093, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Galicia. El" + }, + { + "label": "located_in", + "score": 0.0092, + "head_text": "Endesa firmaron", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "ceo_of", + "score": 0.0091, + "head_text": "Ignacio Galan, se", + "tail_text": "Endesa firmaron" + }, + { + "label": "located_in", + "score": 0.0091, + "head_text": "Bilbao. El", + "tail_text": "Marina Serrano, en" + }, + { + "label": "headquartered_in", + "score": 0.009, + "head_text": "Endesa firmaron", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "headquartered_in", + "score": 0.0089, + "head_text": "Marina Serrano, en", + "tail_text": "Endesa firmaron" + }, + { + "label": "appointed_as", + "score": 0.0089, + "head_text": "Galicia. El", + "tail_text": "Iberdrola y" + }, + { + "label": "headquartered_in", + "score": 0.0088, + "head_text": "Endesa firmaron", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "ceo_of", + "score": 0.0088, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Iberdrola y" + }, + { + "label": "ceo_of", + "score": 0.0087, + "head_text": "Iberdrola y", + "tail_text": "Galicia. El" + }, + { + "label": "located_in", + "score": 0.0087, + "head_text": "Marina Serrano, en", + "tail_text": "Endesa firmaron" + }, + { + "label": "headquartered_in", + "score": 0.0087, + "head_text": "Bilbao. El", + "tail_text": "Marina Serrano, en" + }, + { + "label": "located_in", + "score": 0.0087, + "head_text": "Endesa firmaron", + "tail_text": "Iberdrola y" + }, + { + "label": "works_at", + "score": 0.0084, + "head_text": "Bilbao. El", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "ceo_of", + "score": 0.0082, + "head_text": "Iberdrola y", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "appointed_as", + "score": 0.0081, + "head_text": "Iberdrola y", + "tail_text": "Galicia. El" + }, + { + "label": "headquartered_in", + "score": 0.008, + "head_text": "Ignacio Galan, se", + "tail_text": "Endesa firmaron" + }, + { + "label": "located_in", + "score": 0.0079, + "head_text": "Endesa, Marina", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "located_in", + "score": 0.0078, + "head_text": "Ignacio Galan, se", + "tail_text": "Endesa firmaron" + }, + { + "label": "headquartered_in", + "score": 0.0078, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "located_in", + "score": 0.0077, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "headquartered_in", + "score": 0.0076, + "head_text": "Endesa, Marina", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "located_in", + "score": 0.0074, + "head_text": "Endesa, Marina", + "tail_text": "Iberdrola y" + }, + { + "label": "works_at", + "score": 0.0073, + "head_text": "Endesa firmaron", + "tail_text": "Marina Serrano, en" + }, + { + "label": "headquartered_in", + "score": 0.0072, + "head_text": "Endesa firmaron", + "tail_text": "Iberdrola y" + }, + { + "label": "ceo_of", + "score": 0.0071, + "head_text": "Galicia. El", + "tail_text": "Iberdrola y" + }, + { + "label": "headquartered_in", + "score": 0.007, + "head_text": "Endesa, Marina", + "tail_text": "Iberdrola y" + }, + { + "label": "located_in", + "score": 0.0069, + "head_text": "Endesa firmaron", + "tail_text": "Endesa, Marina" + }, + { + "label": "works_at", + "score": 0.0068, + "head_text": "Endesa, Marina", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "located_in", + "score": 0.0068, + "head_text": "Endesa firmaron", + "tail_text": "Marina Serrano, en" + }, + { + "label": "works_at", + "score": 0.0068, + "head_text": "Endesa, Marina", + "tail_text": "Galicia. El" + }, + { + "label": "headquartered_in", + "score": 0.0068, + "head_text": "Marina Serrano, en", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "located_in", + "score": 0.0067, + "head_text": "Marina Serrano, en", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "appointed_as", + "score": 0.0066, + "head_text": "Endesa firmaron", + "tail_text": "Endesa, Marina" + }, + { + "label": "headquartered_in", + "score": 0.0065, + "head_text": "Endesa firmaron", + "tail_text": "Marina Serrano, en" + }, + { + "label": "works_at", + "score": 0.0065, + "head_text": "Bilbao. El", + "tail_text": "Iberdrola y" + }, + { + "label": "located_in", + "score": 0.0065, + "head_text": "Iberdrola y", + "tail_text": "Endesa firmaron" + }, + { + "label": "appointed_as", + "score": 0.0064, + "head_text": "Endesa, Marina", + "tail_text": "Endesa firmaron" + }, + { + "label": "headquartered_in", + "score": 0.0064, + "head_text": "Endesa firmaron", + "tail_text": "Endesa, Marina" + }, + { + "label": "works_at", + "score": 0.0062, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Bilbao. El" + }, + { + "label": "located_in", + "score": 0.0062, + "head_text": "Iberdrola y", + "tail_text": "Endesa, Marina" + }, + { + "label": "works_at", + "score": 0.0061, + "head_text": "Galicia. El", + "tail_text": "Endesa, Marina" + }, + { + "label": "located_in", + "score": 0.0061, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Endesa firmaron" + }, + { + "label": "headquartered_in", + "score": 0.0059, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Endesa firmaron" + }, + { + "label": "headquartered_in", + "score": 0.0058, + "head_text": "Iberdrola y", + "tail_text": "Endesa, Marina" + }, + { + "label": "headquartered_in", + "score": 0.0058, + "head_text": "Iberdrola y", + "tail_text": "Endesa firmaron" + }, + { + "label": "works_at", + "score": 0.0057, + "head_text": "Marina Serrano, en", + "tail_text": "Endesa firmaron" + }, + { + "label": "headquartered_in", + "score": 0.0057, + "head_text": "Endesa, Marina", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "works_at", + "score": 0.0057, + "head_text": "Ignacio Galan, se", + "tail_text": "Endesa, Marina" + }, + { + "label": "headquartered_in", + "score": 0.0056, + "head_text": "Ignacio Galan, se", + "tail_text": "Endesa, Marina" + }, + { + "label": "located_in", + "score": 0.0056, + "head_text": "Endesa, Marina", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "located_in", + "score": 0.0056, + "head_text": "Ignacio Galan, se", + "tail_text": "Endesa, Marina" + }, + { + "label": "located_in", + "score": 0.0055, + "head_text": "Marina Serrano, en", + "tail_text": "Endesa, Marina" + }, + { + "label": "located_in", + "score": 0.0055, + "head_text": "Iberdrola y", + "tail_text": "Marina Serrano, en" + }, + { + "label": "headquartered_in", + "score": 0.0054, + "head_text": "Marina Serrano, en", + "tail_text": "Endesa, Marina" + }, + { + "label": "headquartered_in", + "score": 0.0054, + "head_text": "Iberdrola y", + "tail_text": "Marina Serrano, en" + }, + { + "label": "works_at", + "score": 0.0054, + "head_text": "Iberdrola y", + "tail_text": "Bilbao. El" + }, + { + "label": "located_in", + "score": 0.0054, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Endesa, Marina" + }, + { + "label": "appointed_as", + "score": 0.0053, + "head_text": "Bilbao. El", + "tail_text": "Endesa firmaron" + }, + { + "label": "headquartered_in", + "score": 0.0052, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Endesa, Marina" + }, + { + "label": "headquartered_in", + "score": 0.0051, + "head_text": "Endesa, Marina", + "tail_text": "Endesa firmaron" + }, + { + "label": "located_in", + "score": 0.0051, + "head_text": "Endesa, Marina", + "tail_text": "Endesa firmaron" + }, + { + "label": "appointed_as", + "score": 0.0048, + "head_text": "Galicia. El", + "tail_text": "Endesa firmaron" + }, + { + "label": "headquartered_in", + "score": 0.0047, + "head_text": "Ignacio Galan, se", + "tail_text": "Marina Serrano, en" + }, + { + "label": "located_in", + "score": 0.0047, + "head_text": "Ignacio Galan, se", + "tail_text": "Marina Serrano, en" + }, + { + "label": "headquartered_in", + "score": 0.0046, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Marina Serrano, en" + }, + { + "label": "located_in", + "score": 0.0046, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Marina Serrano, en" + }, + { + "label": "located_in", + "score": 0.0041, + "head_text": "Endesa, Marina", + "tail_text": "Marina Serrano, en" + }, + { + "label": "headquartered_in", + "score": 0.004, + "head_text": "Endesa, Marina", + "tail_text": "Marina Serrano, en" + }, + { + "label": "appointed_as", + "score": 0.004, + "head_text": "Endesa firmaron", + "tail_text": "Galicia. El" + }, + { + "label": "works_at", + "score": 0.0037, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "appointed_as", + "score": 0.0035, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Endesa firmaron" + }, + { + "label": "appointed_as", + "score": 0.0031, + "head_text": "Endesa firmaron", + "tail_text": "Bilbao. El" + }, + { + "label": "works_at", + "score": 0.0031, + "head_text": "Iberdrola y", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "works_at", + "score": 0.003, + "head_text": "Ignacio Galan, se", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "appointed_as", + "score": 0.003, + "head_text": "Endesa firmaron", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "works_at", + "score": 0.0027, + "head_text": "Endesa, Marina", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "works_at", + "score": 0.0026, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Endesa, Marina" + }, + { + "label": "works_at", + "score": 0.0024, + "head_text": "Ignacio Galan, se", + "tail_text": "Iberdrola y" + }, + { + "label": "ceo_of", + "score": 0.0024, + "head_text": "Bilbao. El", + "tail_text": "Endesa firmaron" + }, + { + "label": "works_at", + "score": 0.0023, + "head_text": "Galicia. El", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "works_at", + "score": 0.0022, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Galicia. El" + }, + { + "label": "appointed_as", + "score": 0.0022, + "head_text": "Iberdrola y", + "tail_text": "Endesa firmaron" + }, + { + "label": "ceo_of", + "score": 0.002, + "head_text": "Endesa firmaron", + "tail_text": "Bilbao. El" + }, + { + "label": "ceo_of", + "score": 0.002, + "head_text": "Endesa firmaron", + "tail_text": "Galicia. El" + }, + { + "label": "works_at", + "score": 0.0019, + "head_text": "Iberdrola y", + "tail_text": "Endesa, Marina" + }, + { + "label": "works_at", + "score": 0.0018, + "head_text": "Iberdrola y", + "tail_text": "Galicia. El" + }, + { + "label": "appointed_as", + "score": 0.0017, + "head_text": "Endesa firmaron", + "tail_text": "Iberdrola y" + }, + { + "label": "ceo_of", + "score": 0.0017, + "head_text": "Galicia. El", + "tail_text": "Endesa firmaron" + }, + { + "label": "works_at", + "score": 0.0017, + "head_text": "Endesa, Marina", + "tail_text": "Iberdrola y" + }, + { + "label": "works_at", + "score": 0.0015, + "head_text": "Galicia. El", + "tail_text": "Iberdrola y" + }, + { + "label": "ceo_of", + "score": 0.0014, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Endesa firmaron" + }, + { + "label": "works_at", + "score": 0.0013, + "head_text": "Endesa firmaron", + "tail_text": "Bilbao. El" + }, + { + "label": "works_at", + "score": 0.0012, + "head_text": "Bilbao. El", + "tail_text": "Endesa firmaron" + }, + { + "label": "works_at", + "score": 0.0011, + "head_text": "Endesa firmaron", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "ceo_of", + "score": 0.0011, + "head_text": "Endesa firmaron", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "works_at", + "score": 0.0009, + "head_text": "Endesa firmaron", + "tail_text": "Endesa, Marina" + }, + { + "label": "works_at", + "score": 0.0007, + "head_text": "Ignacio Galan, se", + "tail_text": "Endesa firmaron" + }, + { + "label": "works_at", + "score": 0.0006, + "head_text": "Endesa firmaron", + "tail_text": "Galicia. El" + }, + { + "label": "works_at", + "score": 0.0006, + "head_text": "Endesa, Marina", + "tail_text": "Endesa firmaron" + }, + { + "label": "ceo_of", + "score": 0.0006, + "head_text": "Iberdrola y", + "tail_text": "Endesa firmaron" + }, + { + "label": "works_at", + "score": 0.0005, + "head_text": "Galicia. El", + "tail_text": "Endesa firmaron" + }, + { + "label": "works_at", + "score": 0.0005, + "head_text": "Iberdrola y", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "works_at", + "score": 0.0005, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Iberdrola y" + }, + { + "label": "ceo_of", + "score": 0.0004, + "head_text": "Endesa firmaron", + "tail_text": "Iberdrola y" + }, + { + "label": "works_at", + "score": 0.0001, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Endesa firmaron" + }, + { + "label": "works_at", + "score": 0.0001, + "head_text": "Endesa firmaron", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "works_at", + "score": 0.0, + "head_text": "Iberdrola y", + "tail_text": "Endesa firmaron" + }, + { + "label": "works_at", + "score": 0.0, + "head_text": "Endesa firmaron", + "tail_text": "Iberdrola y" + } + ], + "natural_long": [ + { + "label": "organization headquartered in location", + "score": 0.1378, + "head_text": "Iberdrola y", + "tail_text": "Galicia. El" + }, + { + "label": "organization headquartered in location", + "score": 0.1257, + "head_text": "Galicia. El", + "tail_text": "Iberdrola y" + }, + { + "label": "organization is located in location", + "score": 0.1135, + "head_text": "Iberdrola y", + "tail_text": "Galicia. El" + }, + { + "label": "organization is located in location", + "score": 0.101, + "head_text": "Galicia. El", + "tail_text": "Iberdrola y" + }, + { + "label": "organization headquartered in location", + "score": 0.0953, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Galicia. El" + }, + { + "label": "organization headquartered in location", + "score": 0.0915, + "head_text": "Endesa firmaron", + "tail_text": "Galicia. El" + }, + { + "label": "organization headquartered in location", + "score": 0.0904, + "head_text": "Galicia. El", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "organization is located in location", + "score": 0.077, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Galicia. El" + }, + { + "label": "organization headquartered in location", + "score": 0.0752, + "head_text": "Ignacio Galan, se", + "tail_text": "Galicia. El" + }, + { + "label": "organization is located in location", + "score": 0.0718, + "head_text": "Galicia. El", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "organization is located in location", + "score": 0.0709, + "head_text": "Endesa firmaron", + "tail_text": "Galicia. El" + }, + { + "label": "organization is located in location", + "score": 0.0617, + "head_text": "Ignacio Galan, se", + "tail_text": "Galicia. El" + }, + { + "label": "organization headquartered in location", + "score": 0.0579, + "head_text": "Endesa, Marina", + "tail_text": "Galicia. El" + }, + { + "label": "organization headquartered in location", + "score": 0.0548, + "head_text": "Bilbao. El", + "tail_text": "Galicia. El" + }, + { + "label": "organization headquartered in location", + "score": 0.0483, + "head_text": "Galicia. El", + "tail_text": "Endesa firmaron" + }, + { + "label": "organization is located in location", + "score": 0.0449, + "head_text": "Endesa, Marina", + "tail_text": "Galicia. El" + }, + { + "label": "organization is located in location", + "score": 0.0418, + "head_text": "Bilbao. El", + "tail_text": "Galicia. El" + }, + { + "label": "organization headquartered in location", + "score": 0.0384, + "head_text": "Iberdrola y", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "organization headquartered in location", + "score": 0.0379, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Iberdrola y" + }, + { + "label": "organization headquartered in location", + "score": 0.0377, + "head_text": "Marina Serrano, en", + "tail_text": "Galicia. El" + }, + { + "label": "organization is located in location", + "score": 0.0356, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Iberdrola y" + }, + { + "label": "organization is located in location", + "score": 0.0353, + "head_text": "Iberdrola y", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "organization is located in location", + "score": 0.035, + "head_text": "Galicia. El", + "tail_text": "Endesa firmaron" + }, + { + "label": "organization headquartered in location", + "score": 0.0327, + "head_text": "Galicia. El", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "organization headquartered in location", + "score": 0.0303, + "head_text": "Galicia. El", + "tail_text": "Endesa, Marina" + }, + { + "label": "organization is located in location", + "score": 0.0295, + "head_text": "Marina Serrano, en", + "tail_text": "Galicia. El" + }, + { + "label": "organization is located in location", + "score": 0.0258, + "head_text": "Galicia. El", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "organization headquartered in location", + "score": 0.0248, + "head_text": "Ignacio Galan, se", + "tail_text": "Iberdrola y" + }, + { + "label": "person is ceo of organization", + "score": 0.0234, + "head_text": "Marina Serrano, en", + "tail_text": "Endesa, Marina" + }, + { + "label": "organization is located in location", + "score": 0.0233, + "head_text": "Galicia. El", + "tail_text": "Endesa, Marina" + }, + { + "label": "person is ceo of organization", + "score": 0.023, + "head_text": "Marina Serrano, en", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "organization is located in location", + "score": 0.0224, + "head_text": "Ignacio Galan, se", + "tail_text": "Iberdrola y" + }, + { + "label": "organization headquartered in location", + "score": 0.0216, + "head_text": "Bilbao. El", + "tail_text": "Iberdrola y" + }, + { + "label": "organization headquartered in location", + "score": 0.02, + "head_text": "Galicia. El", + "tail_text": "Bilbao. El" + }, + { + "label": "person works at organization", + "score": 0.0199, + "head_text": "Marina Serrano, en", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "organization headquartered in location", + "score": 0.0192, + "head_text": "Bilbao. El", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "organization headquartered in location", + "score": 0.0188, + "head_text": "Ignacio Galan, se", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "person works at organization", + "score": 0.0181, + "head_text": "Marina Serrano, en", + "tail_text": "Endesa, Marina" + }, + { + "label": "organization headquartered in location", + "score": 0.0181, + "head_text": "Endesa, Marina", + "tail_text": "Iberdrola y" + }, + { + "label": "person is ceo of organization", + "score": 0.0177, + "head_text": "Marina Serrano, en", + "tail_text": "Iberdrola y" + }, + { + "label": "organization is located in location", + "score": 0.0176, + "head_text": "Ignacio Galan, se", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "person works at organization", + "score": 0.0175, + "head_text": "Ignacio Galan, se", + "tail_text": "Endesa, Marina" + }, + { + "label": "person is ceo of organization", + "score": 0.0174, + "head_text": "Ignacio Galan, se", + "tail_text": "Endesa, Marina" + }, + { + "label": "person is ceo of organization", + "score": 0.0172, + "head_text": "Marina Serrano, en", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "organization headquartered in location", + "score": 0.0171, + "head_text": "Endesa, Marina", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "person is ceo of organization", + "score": 0.017, + "head_text": "Endesa, Marina", + "tail_text": "Marina Serrano, en" + }, + { + "label": "person is ceo of organization", + "score": 0.0169, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Marina Serrano, en" + }, + { + "label": "person is ceo of organization", + "score": 0.0161, + "head_text": "Marina Serrano, en", + "tail_text": "Galicia. El" + }, + { + "label": "organization is located in location", + "score": 0.0156, + "head_text": "Endesa, Marina", + "tail_text": "Iberdrola y" + }, + { + "label": "organization is located in location", + "score": 0.0156, + "head_text": "Endesa, Marina", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "person works at organization", + "score": 0.0151, + "head_text": "Marina Serrano, en", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "organization headquartered in location", + "score": 0.015, + "head_text": "Endesa firmaron", + "tail_text": "Iberdrola y" + }, + { + "label": "organization headquartered in location", + "score": 0.0149, + "head_text": "Endesa firmaron", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "person appointed as role at organization", + "score": 0.0148, + "head_text": "Ignacio Galan, se", + "tail_text": "Endesa, Marina" + }, + { + "label": "person works at organization", + "score": 0.0148, + "head_text": "Marina Serrano, en", + "tail_text": "Iberdrola y" + }, + { + "label": "organization headquartered in location", + "score": 0.0146, + "head_text": "Iberdrola y", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "person is ceo of organization", + "score": 0.0146, + "head_text": "Ignacio Galan, se", + "tail_text": "Marina Serrano, en" + }, + { + "label": "organization is located in location", + "score": 0.0146, + "head_text": "Bilbao. El", + "tail_text": "Iberdrola y" + }, + { + "label": "person appointed as role at organization", + "score": 0.0144, + "head_text": "Marina Serrano, en", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "organization is located in location", + "score": 0.0144, + "head_text": "Galicia. El", + "tail_text": "Bilbao. El" + }, + { + "label": "person is ceo of organization", + "score": 0.0141, + "head_text": "Galicia. El", + "tail_text": "Marina Serrano, en" + }, + { + "label": "person appointed as role at organization", + "score": 0.014, + "head_text": "Ignacio Galan, se", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "person works at organization", + "score": 0.0137, + "head_text": "Endesa, Marina", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "person is ceo of organization", + "score": 0.0137, + "head_text": "Endesa, Marina", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "person works at organization", + "score": 0.0135, + "head_text": "Marina Serrano, en", + "tail_text": "Galicia. El" + }, + { + "label": "person works at organization", + "score": 0.0132, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Marina Serrano, en" + }, + { + "label": "person is ceo of organization", + "score": 0.0132, + "head_text": "Iberdrola y", + "tail_text": "Marina Serrano, en" + }, + { + "label": "person works at organization", + "score": 0.0131, + "head_text": "Endesa, Marina", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "organization is located in location", + "score": 0.013, + "head_text": "Bilbao. El", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "organization is located in location", + "score": 0.0127, + "head_text": "Iberdrola y", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "person appointed as role at organization", + "score": 0.0125, + "head_text": "Endesa, Marina", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "person appointed as role at organization", + "score": 0.0124, + "head_text": "Ignacio Galan, se", + "tail_text": "Iberdrola y" + }, + { + "label": "person is ceo of organization", + "score": 0.0124, + "head_text": "Endesa, Marina", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "person appointed as role at organization", + "score": 0.0124, + "head_text": "Endesa, Marina", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "person works at organization", + "score": 0.0122, + "head_text": "Galicia. El", + "tail_text": "Marina Serrano, en" + }, + { + "label": "person works at organization", + "score": 0.0122, + "head_text": "Endesa, Marina", + "tail_text": "Marina Serrano, en" + }, + { + "label": "person appointed as role at organization", + "score": 0.0119, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "person works at organization", + "score": 0.0118, + "head_text": "Ignacio Galan, se", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "person works at organization", + "score": 0.0118, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Endesa, Marina" + }, + { + "label": "person works at organization", + "score": 0.0118, + "head_text": "Ignacio Galan, se", + "tail_text": "Marina Serrano, en" + }, + { + "label": "organization headquartered in location", + "score": 0.0117, + "head_text": "Iberdrola y", + "tail_text": "Endesa, Marina" + }, + { + "label": "person appointed as role at organization", + "score": 0.0116, + "head_text": "Marina Serrano, en", + "tail_text": "Iberdrola y" + }, + { + "label": "person is ceo of organization", + "score": 0.0114, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Endesa, Marina" + }, + { + "label": "organization headquartered in location", + "score": 0.0113, + "head_text": "Marina Serrano, en", + "tail_text": "Iberdrola y" + }, + { + "label": "organization headquartered in location", + "score": 0.0113, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "organization headquartered in location", + "score": 0.0112, + "head_text": "Marina Serrano, en", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "person appointed as role at organization", + "score": 0.011, + "head_text": "Marina Serrano, en", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "organization headquartered in location", + "score": 0.0109, + "head_text": "Iberdrola y", + "tail_text": "Endesa firmaron" + }, + { + "label": "person appointed as role at organization", + "score": 0.0108, + "head_text": "Marina Serrano, en", + "tail_text": "Endesa, Marina" + }, + { + "label": "organization is located in location", + "score": 0.0107, + "head_text": "Endesa firmaron", + "tail_text": "Iberdrola y" + }, + { + "label": "person works at organization", + "score": 0.0107, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "person appointed as role at organization", + "score": 0.0106, + "head_text": "Ignacio Galan, se", + "tail_text": "Galicia. El" + }, + { + "label": "person works at organization", + "score": 0.0106, + "head_text": "Iberdrola y", + "tail_text": "Marina Serrano, en" + }, + { + "label": "organization is located in location", + "score": 0.0104, + "head_text": "Endesa firmaron", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "organization headquartered in location", + "score": 0.0104, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Endesa, Marina" + }, + { + "label": "person appointed as role at organization", + "score": 0.0104, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Endesa, Marina" + }, + { + "label": "organization is located in location", + "score": 0.0103, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "person appointed as role at organization", + "score": 0.0103, + "head_text": "Iberdrola y", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "person works at organization", + "score": 0.0101, + "head_text": "Galicia. El", + "tail_text": "Endesa, Marina" + }, + { + "label": "organization is located in location", + "score": 0.01, + "head_text": "Iberdrola y", + "tail_text": "Endesa, Marina" + }, + { + "label": "person appointed as role at organization", + "score": 0.0099, + "head_text": "Galicia. El", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "person appointed as role at organization", + "score": 0.0099, + "head_text": "Marina Serrano, en", + "tail_text": "Galicia. El" + }, + { + "label": "person works at organization", + "score": 0.0096, + "head_text": "Galicia. El", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "organization headquartered in location", + "score": 0.0096, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Endesa firmaron" + }, + { + "label": "organization headquartered in location", + "score": 0.0095, + "head_text": "Ignacio Galan, se", + "tail_text": "Endesa firmaron" + }, + { + "label": "organization is located in location", + "score": 0.0095, + "head_text": "Marina Serrano, en", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "organization is located in location", + "score": 0.0094, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Endesa, Marina" + }, + { + "label": "organization headquartered in location", + "score": 0.0093, + "head_text": "Galicia. El", + "tail_text": "Marina Serrano, en" + }, + { + "label": "person works at organization", + "score": 0.0093, + "head_text": "Ignacio Galan, se", + "tail_text": "Galicia. El" + }, + { + "label": "person is ceo of organization", + "score": 0.0093, + "head_text": "Ignacio Galan, se", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "organization is located in location", + "score": 0.0092, + "head_text": "Marina Serrano, en", + "tail_text": "Iberdrola y" + }, + { + "label": "organization headquartered in location", + "score": 0.0091, + "head_text": "Ignacio Galan, se", + "tail_text": "Endesa, Marina" + }, + { + "label": "person works at organization", + "score": 0.009, + "head_text": "Ignacio Galan, se", + "tail_text": "Iberdrola y" + }, + { + "label": "person is ceo of organization", + "score": 0.0088, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "person works at organization", + "score": 0.0087, + "head_text": "Iberdrola y", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "person is ceo of organization", + "score": 0.0087, + "head_text": "Galicia. El", + "tail_text": "Endesa, Marina" + }, + { + "label": "person appointed as role at organization", + "score": 0.0086, + "head_text": "Galicia. El", + "tail_text": "Endesa, Marina" + }, + { + "label": "person appointed as role at organization", + "score": 0.0086, + "head_text": "Endesa, Marina", + "tail_text": "Iberdrola y" + }, + { + "label": "organization is located in location", + "score": 0.0083, + "head_text": "Ignacio Galan, se", + "tail_text": "Endesa, Marina" + }, + { + "label": "person works at organization", + "score": 0.0082, + "head_text": "Iberdrola y", + "tail_text": "Endesa, Marina" + }, + { + "label": "person appointed as role at organization", + "score": 0.0081, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Marina Serrano, en" + }, + { + "label": "organization headquartered in location", + "score": 0.0081, + "head_text": "Bilbao. El", + "tail_text": "Endesa, Marina" + }, + { + "label": "organization headquartered in location", + "score": 0.008, + "head_text": "Endesa firmaron", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "person works at organization", + "score": 0.0078, + "head_text": "Endesa, Marina", + "tail_text": "Iberdrola y" + }, + { + "label": "organization is located in location", + "score": 0.0078, + "head_text": "Iberdrola y", + "tail_text": "Endesa firmaron" + }, + { + "label": "person appointed as role at organization", + "score": 0.0077, + "head_text": "Iberdrola y", + "tail_text": "Endesa, Marina" + }, + { + "label": "organization headquartered in location", + "score": 0.0076, + "head_text": "Endesa firmaron", + "tail_text": "Endesa, Marina" + }, + { + "label": "person is ceo of organization", + "score": 0.0076, + "head_text": "Ignacio Galan, se", + "tail_text": "Galicia. El" + }, + { + "label": "person appointed as role at organization", + "score": 0.0075, + "head_text": "Ignacio Galan, se", + "tail_text": "Marina Serrano, en" + }, + { + "label": "person is ceo of organization", + "score": 0.0074, + "head_text": "Iberdrola y", + "tail_text": "Endesa, Marina" + }, + { + "label": "person is ceo of organization", + "score": 0.0074, + "head_text": "Endesa, Marina", + "tail_text": "Iberdrola y" + }, + { + "label": "person appointed as role at organization", + "score": 0.0073, + "head_text": "Endesa, Marina", + "tail_text": "Galicia. El" + }, + { + "label": "person appointed as role at organization", + "score": 0.0073, + "head_text": "Galicia. El", + "tail_text": "Marina Serrano, en" + }, + { + "label": "organization is located in location", + "score": 0.0072, + "head_text": "Ignacio Galan, se", + "tail_text": "Endesa firmaron" + }, + { + "label": "person works at organization", + "score": 0.0072, + "head_text": "Endesa, Marina", + "tail_text": "Galicia. El" + }, + { + "label": "organization headquartered in location", + "score": 0.0072, + "head_text": "Endesa, Marina", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "person is ceo of organization", + "score": 0.0071, + "head_text": "Galicia. El", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "organization is located in location", + "score": 0.007, + "head_text": "Galicia. El", + "tail_text": "Marina Serrano, en" + }, + { + "label": "organization headquartered in location", + "score": 0.007, + "head_text": "Iberdrola y", + "tail_text": "Bilbao. El" + }, + { + "label": "person is ceo of organization", + "score": 0.0069, + "head_text": "Ignacio Galan, se", + "tail_text": "Iberdrola y" + }, + { + "label": "person appointed as role at organization", + "score": 0.0069, + "head_text": "Endesa, Marina", + "tail_text": "Marina Serrano, en" + }, + { + "label": "person is ceo of organization", + "score": 0.0068, + "head_text": "Endesa, Marina", + "tail_text": "Galicia. El" + }, + { + "label": "organization is located in location", + "score": 0.0068, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Endesa firmaron" + }, + { + "label": "person appointed as role at organization", + "score": 0.0067, + "head_text": "Iberdrola y", + "tail_text": "Marina Serrano, en" + }, + { + "label": "organization headquartered in location", + "score": 0.0067, + "head_text": "Bilbao. El", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "person is ceo of organization", + "score": 0.0066, + "head_text": "Iberdrola y", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "organization is located in location", + "score": 0.0063, + "head_text": "Endesa, Marina", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "person appointed as role at organization", + "score": 0.0063, + "head_text": "Bilbao. El", + "tail_text": "Endesa, Marina" + }, + { + "label": "person works at organization", + "score": 0.0063, + "head_text": "Bilbao. El", + "tail_text": "Endesa, Marina" + }, + { + "label": "person appointed as role at organization", + "score": 0.0063, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Iberdrola y" + }, + { + "label": "organization headquartered in location", + "score": 0.0061, + "head_text": "Endesa, Marina", + "tail_text": "Endesa firmaron" + }, + { + "label": "person appointed as role at organization", + "score": 0.006, + "head_text": "Iberdrola y", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "organization is located in location", + "score": 0.0059, + "head_text": "Endesa firmaron", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "organization headquartered in location", + "score": 0.0059, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Bilbao. El" + }, + { + "label": "person appointed as role at organization", + "score": 0.0059, + "head_text": "Galicia. El", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "organization headquartered in location", + "score": 0.0058, + "head_text": "Marina Serrano, en", + "tail_text": "Endesa firmaron" + }, + { + "label": "organization is located in location", + "score": 0.0056, + "head_text": "Bilbao. El", + "tail_text": "Endesa, Marina" + }, + { + "label": "organization headquartered in location", + "score": 0.0055, + "head_text": "Marina Serrano, en", + "tail_text": "Endesa, Marina" + }, + { + "label": "person appointed as role at organization", + "score": 0.0054, + "head_text": "Bilbao. El", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "organization is located in location", + "score": 0.0054, + "head_text": "Endesa firmaron", + "tail_text": "Endesa, Marina" + }, + { + "label": "person appointed as role at organization", + "score": 0.0053, + "head_text": "Bilbao. El", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "person is ceo of organization", + "score": 0.0053, + "head_text": "Bilbao. El", + "tail_text": "Endesa, Marina" + }, + { + "label": "organization headquartered in location", + "score": 0.0052, + "head_text": "Marina Serrano, en", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "person appointed as role at organization", + "score": 0.005, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Galicia. El" + }, + { + "label": "organization is located in location", + "score": 0.0048, + "head_text": "Bilbao. El", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "organization is located in location", + "score": 0.0047, + "head_text": "Marina Serrano, en", + "tail_text": "Endesa, Marina" + }, + { + "label": "person works at organization", + "score": 0.0046, + "head_text": "Bilbao. El", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "organization is located in location", + "score": 0.0046, + "head_text": "Iberdrola y", + "tail_text": "Bilbao. El" + }, + { + "label": "person works at organization", + "score": 0.0046, + "head_text": "Galicia. El", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "person appointed as role at organization", + "score": 0.0045, + "head_text": "Bilbao. El", + "tail_text": "Galicia. El" + }, + { + "label": "organization is located in location", + "score": 0.0045, + "head_text": "Marina Serrano, en", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "person appointed as role at organization", + "score": 0.0043, + "head_text": "Iberdrola y", + "tail_text": "Galicia. El" + }, + { + "label": "person works at organization", + "score": 0.0043, + "head_text": "Bilbao. El", + "tail_text": "Galicia. El" + }, + { + "label": "organization is located in location", + "score": 0.0043, + "head_text": "Endesa, Marina", + "tail_text": "Endesa firmaron" + }, + { + "label": "person appointed as role at organization", + "score": 0.0042, + "head_text": "Marina Serrano, en", + "tail_text": "Endesa firmaron" + }, + { + "label": "person appointed as role at organization", + "score": 0.0042, + "head_text": "Galicia. El", + "tail_text": "Iberdrola y" + }, + { + "label": "person is ceo of organization", + "score": 0.0042, + "head_text": "Bilbao. El", + "tail_text": "Marina Serrano, en" + }, + { + "label": "organization is located in location", + "score": 0.0041, + "head_text": "Marina Serrano, en", + "tail_text": "Endesa firmaron" + }, + { + "label": "person works at organization", + "score": 0.0039, + "head_text": "Iberdrola y", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "person works at organization", + "score": 0.0039, + "head_text": "Bilbao. El", + "tail_text": "Marina Serrano, en" + }, + { + "label": "organization is located in location", + "score": 0.0039, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Bilbao. El" + }, + { + "label": "person works at organization", + "score": 0.0039, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Iberdrola y" + }, + { + "label": "organization headquartered in location", + "score": 0.0038, + "head_text": "Iberdrola y", + "tail_text": "Marina Serrano, en" + }, + { + "label": "organization headquartered in location", + "score": 0.0037, + "head_text": "Ignacio Galan, se", + "tail_text": "Bilbao. El" + }, + { + "label": "person works at organization", + "score": 0.0037, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Galicia. El" + }, + { + "label": "person works at organization", + "score": 0.0036, + "head_text": "Bilbao. El", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "organization headquartered in location", + "score": 0.0036, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Marina Serrano, en" + }, + { + "label": "person appointed as role at organization", + "score": 0.0035, + "head_text": "Bilbao. El", + "tail_text": "Iberdrola y" + }, + { + "label": "person is ceo of organization", + "score": 0.0035, + "head_text": "Endesa firmaron", + "tail_text": "Marina Serrano, en" + }, + { + "label": "person is ceo of organization", + "score": 0.0035, + "head_text": "Bilbao. El", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "person works at organization", + "score": 0.0034, + "head_text": "Endesa firmaron", + "tail_text": "Marina Serrano, en" + }, + { + "label": "organization headquartered in location", + "score": 0.0034, + "head_text": "Bilbao. El", + "tail_text": "Endesa firmaron" + }, + { + "label": "person appointed as role at organization", + "score": 0.0033, + "head_text": "Endesa firmaron", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "person works at organization", + "score": 0.0032, + "head_text": "Iberdrola y", + "tail_text": "Galicia. El" + }, + { + "label": "person appointed as role at organization", + "score": 0.0032, + "head_text": "Endesa firmaron", + "tail_text": "Marina Serrano, en" + }, + { + "label": "person appointed as role at organization", + "score": 0.0032, + "head_text": "Ignacio Galan, se", + "tail_text": "Endesa firmaron" + }, + { + "label": "organization headquartered in location", + "score": 0.0031, + "head_text": "Endesa, Marina", + "tail_text": "Bilbao. El" + }, + { + "label": "person works at organization", + "score": 0.003, + "head_text": "Galicia. El", + "tail_text": "Iberdrola y" + }, + { + "label": "organization is located in location", + "score": 0.003, + "head_text": "Iberdrola y", + "tail_text": "Marina Serrano, en" + }, + { + "label": "organization headquartered in location", + "score": 0.003, + "head_text": "Ignacio Galan, se", + "tail_text": "Marina Serrano, en" + }, + { + "label": "organization is located in location", + "score": 0.0029, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Marina Serrano, en" + }, + { + "label": "organization headquartered in location", + "score": 0.0029, + "head_text": "Endesa firmaron", + "tail_text": "Marina Serrano, en" + }, + { + "label": "person appointed as role at organization", + "score": 0.0029, + "head_text": "Endesa firmaron", + "tail_text": "Endesa, Marina" + }, + { + "label": "person appointed as role at organization", + "score": 0.0029, + "head_text": "Bilbao. El", + "tail_text": "Marina Serrano, en" + }, + { + "label": "person works at organization", + "score": 0.0028, + "head_text": "Marina Serrano, en", + "tail_text": "Endesa firmaron" + }, + { + "label": "person is ceo of organization", + "score": 0.0028, + "head_text": "Marina Serrano, en", + "tail_text": "Endesa firmaron" + }, + { + "label": "organization headquartered in location", + "score": 0.0028, + "head_text": "Endesa, Marina", + "tail_text": "Marina Serrano, en" + }, + { + "label": "person appointed as role at organization", + "score": 0.0027, + "head_text": "Galicia. El", + "tail_text": "Bilbao. El" + }, + { + "label": "organization is located in location", + "score": 0.0026, + "head_text": "Ignacio Galan, se", + "tail_text": "Bilbao. El" + }, + { + "label": "person is ceo of organization", + "score": 0.0026, + "head_text": "Bilbao. El", + "tail_text": "Galicia. El" + }, + { + "label": "organization is located in location", + "score": 0.0025, + "head_text": "Ignacio Galan, se", + "tail_text": "Marina Serrano, en" + }, + { + "label": "person works at organization", + "score": 0.0025, + "head_text": "Galicia. El", + "tail_text": "Bilbao. El" + }, + { + "label": "person is ceo of organization", + "score": 0.0025, + "head_text": "Galicia. El", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "person is ceo of organization", + "score": 0.0024, + "head_text": "Marina Serrano, en", + "tail_text": "Bilbao. El" + }, + { + "label": "person appointed as role at organization", + "score": 0.0024, + "head_text": "Ignacio Galan, se", + "tail_text": "Bilbao. El" + }, + { + "label": "organization is located in location", + "score": 0.0023, + "head_text": "Endesa, Marina", + "tail_text": "Marina Serrano, en" + }, + { + "label": "person is ceo of organization", + "score": 0.0023, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Iberdrola y" + }, + { + "label": "person works at organization", + "score": 0.0023, + "head_text": "Bilbao. El", + "tail_text": "Iberdrola y" + }, + { + "label": "person is ceo of organization", + "score": 0.0023, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Galicia. El" + }, + { + "label": "organization is located in location", + "score": 0.0022, + "head_text": "Bilbao. El", + "tail_text": "Endesa firmaron" + }, + { + "label": "person works at organization", + "score": 0.0022, + "head_text": "Marina Serrano, en", + "tail_text": "Bilbao. El" + }, + { + "label": "person is ceo of organization", + "score": 0.0022, + "head_text": "Iberdrola y", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "person is ceo of organization", + "score": 0.0022, + "head_text": "Bilbao. El", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "organization headquartered in location", + "score": 0.0021, + "head_text": "Bilbao. El", + "tail_text": "Marina Serrano, en" + }, + { + "label": "organization is located in location", + "score": 0.0021, + "head_text": "Endesa, Marina", + "tail_text": "Bilbao. El" + }, + { + "label": "organization headquartered in location", + "score": 0.0021, + "head_text": "Marina Serrano, en", + "tail_text": "Bilbao. El" + }, + { + "label": "organization is located in location", + "score": 0.0021, + "head_text": "Endesa firmaron", + "tail_text": "Marina Serrano, en" + }, + { + "label": "person appointed as role at organization", + "score": 0.002, + "head_text": "Endesa, Marina", + "tail_text": "Bilbao. El" + }, + { + "label": "person appointed as role at organization", + "score": 0.002, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Bilbao. El" + }, + { + "label": "person appointed as role at organization", + "score": 0.002, + "head_text": "Marina Serrano, en", + "tail_text": "Bilbao. El" + }, + { + "label": "person appointed as role at organization", + "score": 0.0019, + "head_text": "Endesa, Marina", + "tail_text": "Endesa firmaron" + }, + { + "label": "organization headquartered in location", + "score": 0.0018, + "head_text": "Endesa firmaron", + "tail_text": "Bilbao. El" + }, + { + "label": "person works at organization", + "score": 0.0018, + "head_text": "Ignacio Galan, se", + "tail_text": "Bilbao. El" + }, + { + "label": "person works at organization", + "score": 0.0017, + "head_text": "Endesa firmaron", + "tail_text": "Endesa, Marina" + }, + { + "label": "person works at organization", + "score": 0.0017, + "head_text": "Endesa firmaron", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "person works at organization", + "score": 0.0017, + "head_text": "Endesa, Marina", + "tail_text": "Bilbao. El" + }, + { + "label": "person is ceo of organization", + "score": 0.0017, + "head_text": "Iberdrola y", + "tail_text": "Galicia. El" + }, + { + "label": "person is ceo of organization", + "score": 0.0016, + "head_text": "Endesa, Marina", + "tail_text": "Bilbao. El" + }, + { + "label": "person is ceo of organization", + "score": 0.0015, + "head_text": "Ignacio Galan, se", + "tail_text": "Bilbao. El" + }, + { + "label": "person appointed as role at organization", + "score": 0.0015, + "head_text": "Endesa firmaron", + "tail_text": "Galicia. El" + }, + { + "label": "organization is located in location", + "score": 0.0015, + "head_text": "Bilbao. El", + "tail_text": "Marina Serrano, en" + }, + { + "label": "person appointed as role at organization", + "score": 0.0015, + "head_text": "Iberdrola y", + "tail_text": "Bilbao. El" + }, + { + "label": "organization is located in location", + "score": 0.0015, + "head_text": "Marina Serrano, en", + "tail_text": "Bilbao. El" + }, + { + "label": "person is ceo of organization", + "score": 0.0014, + "head_text": "Galicia. El", + "tail_text": "Bilbao. El" + }, + { + "label": "person is ceo of organization", + "score": 0.0014, + "head_text": "Galicia. El", + "tail_text": "Iberdrola y" + }, + { + "label": "person works at organization", + "score": 0.0013, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Bilbao. El" + }, + { + "label": "person appointed as role at organization", + "score": 0.0013, + "head_text": "Galicia. El", + "tail_text": "Endesa firmaron" + }, + { + "label": "person is ceo of organization", + "score": 0.0012, + "head_text": "Endesa firmaron", + "tail_text": "Endesa, Marina" + }, + { + "label": "organization is located in location", + "score": 0.0012, + "head_text": "Endesa firmaron", + "tail_text": "Bilbao. El" + }, + { + "label": "person is ceo of organization", + "score": 0.0012, + "head_text": "Bilbao. El", + "tail_text": "Iberdrola y" + }, + { + "label": "person works at organization", + "score": 0.0012, + "head_text": "Ignacio Galan, se", + "tail_text": "Endesa firmaron" + }, + { + "label": "person is ceo of organization", + "score": 0.0011, + "head_text": "Endesa firmaron", + "tail_text": "Ignacio Galan, se" + }, + { + "label": "person works at organization", + "score": 0.001, + "head_text": "Iberdrola y", + "tail_text": "Bilbao. El" + }, + { + "label": "person appointed as role at organization", + "score": 0.001, + "head_text": "Endesa firmaron", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "person is ceo of organization", + "score": 0.0009, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Bilbao. El" + }, + { + "label": "person appointed as role at organization", + "score": 0.0009, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Endesa firmaron" + }, + { + "label": "person works at organization", + "score": 0.0008, + "head_text": "Endesa firmaron", + "tail_text": "Galicia. El" + }, + { + "label": "person works at organization", + "score": 0.0008, + "head_text": "Endesa, Marina", + "tail_text": "Endesa firmaron" + }, + { + "label": "person is ceo of organization", + "score": 0.0007, + "head_text": "Ignacio Galan, se", + "tail_text": "Endesa firmaron" + }, + { + "label": "person is ceo of organization", + "score": 0.0006, + "head_text": "Iberdrola y", + "tail_text": "Bilbao. El" + }, + { + "label": "person is ceo of organization", + "score": 0.0006, + "head_text": "Endesa, Marina", + "tail_text": "Endesa firmaron" + }, + { + "label": "person works at organization", + "score": 0.0006, + "head_text": "Galicia. El", + "tail_text": "Endesa firmaron" + }, + { + "label": "person appointed as role at organization", + "score": 0.0005, + "head_text": "Iberdrola y", + "tail_text": "Endesa firmaron" + }, + { + "label": "person appointed as role at organization", + "score": 0.0004, + "head_text": "Endesa firmaron", + "tail_text": "Iberdrola y" + }, + { + "label": "person appointed as role at organization", + "score": 0.0004, + "head_text": "Bilbao. El", + "tail_text": "Endesa firmaron" + }, + { + "label": "person is ceo of organization", + "score": 0.0004, + "head_text": "Endesa firmaron", + "tail_text": "Galicia. El" + }, + { + "label": "person works at organization", + "score": 0.0003, + "head_text": "Endesa firmaron", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "person is ceo of organization", + "score": 0.0002, + "head_text": "Galicia. El", + "tail_text": "Endesa firmaron" + }, + { + "label": "person works at organization", + "score": 0.0002, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Endesa firmaron" + }, + { + "label": "person appointed as role at organization", + "score": 0.0002, + "head_text": "Endesa firmaron", + "tail_text": "Bilbao. El" + }, + { + "label": "person works at organization", + "score": 0.0002, + "head_text": "Bilbao. El", + "tail_text": "Endesa firmaron" + }, + { + "label": "person is ceo of organization", + "score": 0.0001, + "head_text": "Endesa firmaron", + "tail_text": "Iberdrola, Ignacio" + }, + { + "label": "person works at organization", + "score": 0.0001, + "head_text": "Endesa firmaron", + "tail_text": "Iberdrola y" + }, + { + "label": "person works at organization", + "score": 0.0001, + "head_text": "Iberdrola y", + "tail_text": "Endesa firmaron" + }, + { + "label": "person is ceo of organization", + "score": 0.0001, + "head_text": "Iberdrola, Ignacio", + "tail_text": "Endesa firmaron" + }, + { + "label": "person works at organization", + "score": 0.0001, + "head_text": "Endesa firmaron", + "tail_text": "Bilbao. El" + }, + { + "label": "person is ceo of organization", + "score": 0.0001, + "head_text": "Bilbao. El", + "tail_text": "Endesa firmaron" + }, + { + "label": "person is ceo of organization", + "score": 0.0001, + "head_text": "Endesa firmaron", + "tail_text": "Bilbao. El" + }, + { + "label": "person is ceo of organization", + "score": 0.0, + "head_text": "Iberdrola y", + "tail_text": "Endesa firmaron" + }, + { + "label": "person is ceo of organization", + "score": 0.0, + "head_text": "Endesa firmaron", + "tail_text": "Iberdrola y" + } + ] + } + } + }, + "glirel_topk_sweep": { + "entities": [ + [ + "Pablo Isla", + "person" + ], + [ + "Inditex", + "organization" + ], + [ + "Telefonica", + "organization" + ], + [ + "Jose Maria Alvarez-Pallete", + "person" + ], + [ + "Telefonica", + "organization" + ], + [ + "Madrid", + "location" + ], + [ + "Inditex", + "organization" + ], + [ + "Arteixo", + "location" + ], + [ + "A Coruna", + "location" + ] + ], + "ner": [ + [ + 0, + 2, + "person" + ], + [ + 6, + 7, + "organization" + ], + [ + 14, + 15, + "organization" + ], + [ + 20, + 23, + "person" + ], + [ + 26, + 27, + "organization" + ], + [ + 28, + 29, + "location" + ], + [ + 31, + 32, + "organization" + ], + [ + 36, + 37, + "location" + ], + [ + 37, + 39, + "location" + ] + ], + "by_topk": { + "top_k=1": [ + { + "label": "appointed_as", + "score": 0.2328, + "head": "Madrid last", + "tail": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.2328, + "head": "Madrid last", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.2221, + "head": "Madrid last", + "tail": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.2096, + "head": "Telefonica, in", + "tail": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.2096, + "head": "Telefonica, in", + "tail": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.2042, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Inditex, has" + }, + { + "label": "headquartered_in", + "score": 0.1994, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.1959, + "head": "Arteixo, A", + "tail": "Madrid last" + }, + { + "label": "appointed_as", + "score": 0.1911, + "head": "Telefonica, in", + "tail": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.1825, + "head": "Telefonica. The", + "tail": "Inditex, has" + }, + { + "label": "headquartered_in", + "score": 0.1767, + "head": "Inditex has", + "tail": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.1757, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.1757, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.1738, + "head": "Inditex, has", + "tail": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.1715, + "head": "Arteixo, A", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.1705, + "head": "Pablo Isla, the", + "tail": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.1705, + "head": "A Coruna.", + "tail": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.1678, + "head": "Telefonica. The", + "tail": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.1678, + "head": "Telefonica. The", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.1642, + "head": "Telefonica, in", + "tail": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.1599, + "head": "Madrid last", + "tail": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.155, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.153, + "head": "Pablo Isla, the", + "tail": "Telefonica. The" + }, + { + "label": "appointed_as", + "score": 0.153, + "head": "A Coruna.", + "tail": "Telefonica. The" + }, + { + "label": "appointed_as", + "score": 0.1488, + "head": "Inditex, has", + "tail": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.1487, + "head": "Inditex, has", + "tail": "Telefonica. The" + }, + { + "label": "headquartered_in", + "score": 0.1464, + "head": "Arteixo, A", + "tail": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.1451, + "head": "Telefonica. The", + "tail": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.142, + "head": "Telefonica. The", + "tail": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.1413, + "head": "Inditex has", + "tail": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.1413, + "head": "Inditex has", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.1403, + "head": "Arteixo, A", + "tail": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.1399, + "head": "Inditex has", + "tail": "Telefonica. The" + }, + { + "label": "headquartered_in", + "score": 0.1364, + "head": "Pablo Isla, the", + "tail": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.1364, + "head": "A Coruna.", + "tail": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.1321, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Telefonica. The" + }, + { + "label": "appointed_as", + "score": 0.1292, + "head": "Inditex, has", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.1292, + "head": "Pablo Isla, the", + "tail": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.1292, + "head": "A Coruna.", + "tail": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.1284, + "head": "Pablo Isla, the", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.1284, + "head": "A Coruna.", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.1249, + "head": "Madrid last", + "tail": "Telefonica. The" + }, + { + "label": "appointed_as", + "score": 0.1213, + "head": "Pablo Isla, the", + "tail": "Madrid last" + }, + { + "label": "appointed_as", + "score": 0.1213, + "head": "A Coruna.", + "tail": "Madrid last" + }, + { + "label": "appointed_as", + "score": 0.1206, + "head": "Pablo Isla, the", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.1206, + "head": "A Coruna.", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.1196, + "head": "Telefonica, in", + "tail": "Telefonica. The" + }, + { + "label": "appointed_as", + "score": 0.1167, + "head": "Inditex, has", + "tail": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.1167, + "head": "Inditex, has", + "tail": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.1145, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.1111, + "head": "Inditex has", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.1096, + "head": "Inditex has", + "tail": "Inditex, has" + }, + { + "label": "headquartered_in", + "score": 0.1089, + "head": "Arteixo, A", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.1042, + "head": "Telefonica. The", + "tail": "Telefonica, in" + }, + { + "label": "headquartered_in", + "score": 0.0951, + "head": "Arteixo, A", + "tail": "Telefonica. The" + }, + { + "label": "headquartered_in", + "score": 0.0931, + "head": "Arteixo, A", + "tail": "Pablo Isla, the" + }, + { + "label": "headquartered_in", + "score": 0.0931, + "head": "Arteixo, A", + "tail": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.0918, + "head": "Inditex, has", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.0911, + "head": "Telefonica, in", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.0896, + "head": "Inditex has", + "tail": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.0887, + "head": "Pablo Isla, the", + "tail": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.0887, + "head": "A Coruna.", + "tail": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.0853, + "head": "Telefonica. The", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.0806, + "head": "Telefonica, in", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.0801, + "head": "Inditex, has", + "tail": "Madrid last" + }, + { + "label": "appointed_as", + "score": 0.073, + "head": "Madrid last", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.0682, + "head": "Madrid last", + "tail": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.0644, + "head": "Telefonica. The", + "tail": "Madrid last" + }, + { + "label": "headquartered_in", + "score": 0.0602, + "head": "Madrid last", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.0532, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Madrid last" + }, + { + "label": "headquartered_in", + "score": 0.0401, + "head": "Inditex has", + "tail": "Madrid last" + }, + { + "label": "appointed_as", + "score": 0.0362, + "head": "Telefonica, in", + "tail": "Madrid last" + } + ], + "top_k=3": [ + { + "label": "appointed_as", + "score": 0.2328, + "head": "Madrid last", + "tail": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.2328, + "head": "Madrid last", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.2221, + "head": "Madrid last", + "tail": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.2096, + "head": "Telefonica, in", + "tail": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.2096, + "head": "Telefonica, in", + "tail": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.2042, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Inditex, has" + }, + { + "label": "headquartered_in", + "score": 0.1994, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.1959, + "head": "Arteixo, A", + "tail": "Madrid last" + }, + { + "label": "appointed_as", + "score": 0.1911, + "head": "Telefonica, in", + "tail": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.1825, + "head": "Telefonica. The", + "tail": "Inditex, has" + }, + { + "label": "headquartered_in", + "score": 0.1767, + "head": "Inditex has", + "tail": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.1757, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.1757, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.1738, + "head": "Inditex, has", + "tail": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.1715, + "head": "Arteixo, A", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.1705, + "head": "Pablo Isla, the", + "tail": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.1705, + "head": "A Coruna.", + "tail": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.1678, + "head": "Telefonica. The", + "tail": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.1678, + "head": "Telefonica. The", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.1642, + "head": "Telefonica, in", + "tail": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.162, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.1617, + "head": "Inditex has", + "tail": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.1614, + "head": "Arteixo, A", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.1599, + "head": "Madrid last", + "tail": "Inditex, has" + }, + { + "label": "located_in", + "score": 0.1555, + "head": "Madrid last", + "tail": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.155, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.153, + "head": "Pablo Isla, the", + "tail": "Telefonica. The" + }, + { + "label": "appointed_as", + "score": 0.153, + "head": "A Coruna.", + "tail": "Telefonica. The" + }, + { + "label": "appointed_as", + "score": 0.1488, + "head": "Inditex, has", + "tail": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.1487, + "head": "Inditex, has", + "tail": "Telefonica. The" + }, + { + "label": "headquartered_in", + "score": 0.1464, + "head": "Arteixo, A", + "tail": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.1451, + "head": "Telefonica. The", + "tail": "Inditex has" + }, + { + "label": "located_in", + "score": 0.1436, + "head": "Telefonica, in", + "tail": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.142, + "head": "Telefonica. The", + "tail": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.1415, + "head": "Inditex, has", + "tail": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.1413, + "head": "Inditex has", + "tail": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.1413, + "head": "Inditex has", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.1403, + "head": "Arteixo, A", + "tail": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.1399, + "head": "Inditex has", + "tail": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.1398, + "head": "Arteixo, A", + "tail": "Inditex, has" + }, + { + "label": "located_in", + "score": 0.1371, + "head": "Arteixo, A", + "tail": "Madrid last" + }, + { + "label": "headquartered_in", + "score": 0.1364, + "head": "Pablo Isla, the", + "tail": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.1364, + "head": "A Coruna.", + "tail": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.1321, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.1304, + "head": "Arteixo, A", + "tail": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.1292, + "head": "Inditex, has", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.1292, + "head": "Pablo Isla, the", + "tail": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.1292, + "head": "A Coruna.", + "tail": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.1284, + "head": "Pablo Isla, the", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.1284, + "head": "A Coruna.", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "located_in", + "score": 0.1254, + "head": "Telefonica. The", + "tail": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.1249, + "head": "Madrid last", + "tail": "Telefonica. The" + }, + { + "label": "appointed_as", + "score": 0.1213, + "head": "Pablo Isla, the", + "tail": "Madrid last" + }, + { + "label": "appointed_as", + "score": 0.1213, + "head": "A Coruna.", + "tail": "Madrid last" + }, + { + "label": "appointed_as", + "score": 0.1206, + "head": "Pablo Isla, the", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.1206, + "head": "A Coruna.", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.1196, + "head": "Telefonica, in", + "tail": "Telefonica. The" + }, + { + "label": "appointed_as", + "score": 0.1167, + "head": "Inditex, has", + "tail": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.1167, + "head": "Inditex, has", + "tail": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.1145, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.1111, + "head": "Inditex has", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.1096, + "head": "Inditex has", + "tail": "Inditex, has" + }, + { + "label": "headquartered_in", + "score": 0.1089, + "head": "Arteixo, A", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.1042, + "head": "Telefonica. The", + "tail": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.1036, + "head": "Pablo Isla, the", + "tail": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.1036, + "head": "A Coruna.", + "tail": "Arteixo, A" + }, + { + "label": "ceo_of", + "score": 0.0979, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Inditex has" + }, + { + "label": "located_in", + "score": 0.0974, + "head": "Arteixo, A", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "headquartered_in", + "score": 0.0951, + "head": "Arteixo, A", + "tail": "Telefonica. The" + }, + { + "label": "headquartered_in", + "score": 0.0931, + "head": "Arteixo, A", + "tail": "Pablo Isla, the" + }, + { + "label": "headquartered_in", + "score": 0.0931, + "head": "Arteixo, A", + "tail": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.0918, + "head": "Inditex, has", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.0911, + "head": "Telefonica, in", + "tail": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0899, + "head": "Telefonica. The", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.0896, + "head": "Inditex has", + "tail": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.0887, + "head": "Pablo Isla, the", + "tail": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.0887, + "head": "A Coruna.", + "tail": "Pablo Isla, the" + }, + { + "label": "located_in", + "score": 0.0867, + "head": "Arteixo, A", + "tail": "Pablo Isla, the" + }, + { + "label": "located_in", + "score": 0.0867, + "head": "Arteixo, A", + "tail": "A Coruna." + }, + { + "label": "located_in", + "score": 0.0866, + "head": "Arteixo, A", + "tail": "Telefonica. The" + }, + { + "label": "appointed_as", + "score": 0.0853, + "head": "Telefonica. The", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.0806, + "head": "Telefonica, in", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.0801, + "head": "Inditex, has", + "tail": "Madrid last" + }, + { + "label": "appointed_as", + "score": 0.073, + "head": "Madrid last", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "ceo_of", + "score": 0.0718, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0708, + "head": "Telefonica, in", + "tail": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0696, + "head": "Pablo Isla, the", + "tail": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0696, + "head": "A Coruna.", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.0682, + "head": "Madrid last", + "tail": "Telefonica, in" + }, + { + "label": "ceo_of", + "score": 0.0649, + "head": "Telefonica, in", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.0644, + "head": "Telefonica. The", + "tail": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.0607, + "head": "Inditex, has", + "tail": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.0602, + "head": "Madrid last", + "tail": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0577, + "head": "Pablo Isla, the", + "tail": "Telefonica, in" + }, + { + "label": "ceo_of", + "score": 0.0577, + "head": "A Coruna.", + "tail": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.0571, + "head": "Telefonica, in", + "tail": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0556, + "head": "Inditex, has", + "tail": "Telefonica, in" + }, + { + "label": "ceo_of", + "score": 0.055, + "head": "Telefonica, in", + "tail": "Pablo Isla, the" + }, + { + "label": "ceo_of", + "score": 0.055, + "head": "Telefonica, in", + "tail": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0542, + "head": "Telefonica. The", + "tail": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.0532, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.0524, + "head": "Inditex has", + "tail": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.0515, + "head": "Madrid last", + "tail": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0479, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Pablo Isla, the" + }, + { + "label": "ceo_of", + "score": 0.0479, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0473, + "head": "Inditex has", + "tail": "Telefonica. The" + }, + { + "label": "ceo_of", + "score": 0.0467, + "head": "Inditex has", + "tail": "Pablo Isla, the" + }, + { + "label": "ceo_of", + "score": 0.0467, + "head": "Inditex has", + "tail": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0455, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Telefonica, in" + }, + { + "label": "ceo_of", + "score": 0.0427, + "head": "Inditex has", + "tail": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.0425, + "head": "Madrid last", + "tail": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0419, + "head": "Inditex has", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "located_in", + "score": 0.0412, + "head": "Inditex has", + "tail": "Telefonica, in" + }, + { + "label": "headquartered_in", + "score": 0.0401, + "head": "Inditex has", + "tail": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.04, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Telefonica. The" + }, + { + "label": "headquartered_in", + "score": 0.0396, + "head": "Madrid last", + "tail": "Inditex, has" + }, + { + "label": "located_in", + "score": 0.0394, + "head": "Inditex, has", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.0387, + "head": "Arteixo, A", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "ceo_of", + "score": 0.0382, + "head": "Inditex, has", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "ceo_of", + "score": 0.0378, + "head": "Inditex, has", + "tail": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.0374, + "head": "Inditex has", + "tail": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0372, + "head": "Telefonica. The", + "tail": "Telefonica, in" + }, + { + "label": "ceo_of", + "score": 0.0363, + "head": "Telefonica. The", + "tail": "Pablo Isla, the" + }, + { + "label": "ceo_of", + "score": 0.0363, + "head": "Telefonica. The", + "tail": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.0362, + "head": "Telefonica, in", + "tail": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.0351, + "head": "Pablo Isla, the", + "tail": "Telefonica. The" + }, + { + "label": "ceo_of", + "score": 0.0351, + "head": "A Coruna.", + "tail": "Telefonica. The" + }, + { + "label": "ceo_of", + "score": 0.035, + "head": "Pablo Isla, the", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "ceo_of", + "score": 0.035, + "head": "A Coruna.", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "ceo_of", + "score": 0.0349, + "head": "Pablo Isla, the", + "tail": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0349, + "head": "A Coruna.", + "tail": "Inditex, has" + }, + { + "label": "headquartered_in", + "score": 0.0347, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0341, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.0339, + "head": "Arteixo, A", + "tail": "Telefonica. The" + }, + { + "label": "ceo_of", + "score": 0.0335, + "head": "Madrid last", + "tail": "Pablo Isla, the" + }, + { + "label": "ceo_of", + "score": 0.0335, + "head": "Madrid last", + "tail": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0335, + "head": "Telefonica, in", + "tail": "Telefonica. The" + }, + { + "label": "headquartered_in", + "score": 0.0326, + "head": "Inditex, has", + "tail": "Madrid last" + }, + { + "label": "appointed_as", + "score": 0.0303, + "head": "Inditex has", + "tail": "Madrid last" + }, + { + "label": "located_in", + "score": 0.0288, + "head": "Inditex has", + "tail": "Madrid last" + }, + { + "label": "appointed_as", + "score": 0.0275, + "head": "Arteixo, A", + "tail": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.0275, + "head": "Arteixo, A", + "tail": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0274, + "head": "Inditex, has", + "tail": "Pablo Isla, the" + }, + { + "label": "ceo_of", + "score": 0.0274, + "head": "Inditex, has", + "tail": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.026, + "head": "Madrid last", + "tail": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.025, + "head": "Telefonica, in", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "ceo_of", + "score": 0.0243, + "head": "Telefonica. The", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "located_in", + "score": 0.0241, + "head": "Telefonica. The", + "tail": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.024, + "head": "Madrid last", + "tail": "Telefonica, in" + }, + { + "label": "ceo_of", + "score": 0.0234, + "head": "Telefonica. The", + "tail": "Arteixo, A" + }, + { + "label": "ceo_of", + "score": 0.0232, + "head": "Pablo Isla, the", + "tail": "Arteixo, A" + }, + { + "label": "ceo_of", + "score": 0.0232, + "head": "A Coruna.", + "tail": "Arteixo, A" + }, + { + "label": "ceo_of", + "score": 0.0205, + "head": "Pablo Isla, the", + "tail": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.0205, + "head": "A Coruna.", + "tail": "Madrid last" + }, + { + "label": "headquartered_in", + "score": 0.02, + "head": "Telefonica, in", + "tail": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.0197, + "head": "Pablo Isla, the", + "tail": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0197, + "head": "A Coruna.", + "tail": "Pablo Isla, the" + }, + { + "label": "located_in", + "score": 0.0189, + "head": "Telefonica, in", + "tail": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0183, + "head": "Madrid last", + "tail": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.0181, + "head": "Inditex, has", + "tail": "Madrid last" + }, + { + "label": "appointed_as", + "score": 0.0169, + "head": "Arteixo, A", + "tail": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.0168, + "head": "Inditex has", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "headquartered_in", + "score": 0.0165, + "head": "Telefonica. The", + "tail": "Madrid last" + }, + { + "label": "headquartered_in", + "score": 0.0162, + "head": "Pablo Isla, the", + "tail": "Madrid last" + }, + { + "label": "headquartered_in", + "score": 0.0162, + "head": "A Coruna.", + "tail": "Madrid last" + }, + { + "label": "headquartered_in", + "score": 0.016, + "head": "Madrid last", + "tail": "Pablo Isla, the" + }, + { + "label": "headquartered_in", + "score": 0.016, + "head": "Madrid last", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.0153, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Madrid last" + }, + { + "label": "located_in", + "score": 0.0152, + "head": "Madrid last", + "tail": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.0149, + "head": "Inditex, has", + "tail": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.0146, + "head": "Arteixo, A", + "tail": "Madrid last" + }, + { + "label": "works_at", + "score": 0.0144, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Inditex, has" + }, + { + "label": "headquartered_in", + "score": 0.0143, + "head": "Madrid last", + "tail": "Telefonica. The" + }, + { + "label": "appointed_as", + "score": 0.014, + "head": "Arteixo, A", + "tail": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0139, + "head": "Telefonica, in", + "tail": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.0134, + "head": "Inditex has", + "tail": "Telefonica. The" + }, + { + "label": "ceo_of", + "score": 0.0133, + "head": "Inditex, has", + "tail": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.0129, + "head": "Telefonica, in", + "tail": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.0128, + "head": "Telefonica. The", + "tail": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.0127, + "head": "Madrid last", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "ceo_of", + "score": 0.0111, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Madrid last" + }, + { + "label": "works_at", + "score": 0.011, + "head": "Inditex, has", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "headquartered_in", + "score": 0.0109, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Telefonica, in" + }, + { + "label": "works_at", + "score": 0.0101, + "head": "Pablo Isla, the", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "works_at", + "score": 0.0101, + "head": "A Coruna.", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "headquartered_in", + "score": 0.01, + "head": "Madrid last", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "works_at", + "score": 0.0095, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Pablo Isla, the" + }, + { + "label": "works_at", + "score": 0.0095, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.009, + "head": "Madrid last", + "tail": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.0086, + "head": "Pablo Isla, the", + "tail": "Inditex has" + }, + { + "label": "located_in", + "score": 0.0086, + "head": "A Coruna.", + "tail": "Inditex has" + }, + { + "label": "located_in", + "score": 0.008, + "head": "Inditex has", + "tail": "Pablo Isla, the" + }, + { + "label": "located_in", + "score": 0.008, + "head": "Inditex has", + "tail": "A Coruna." + }, + { + "label": "works_at", + "score": 0.0079, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.0073, + "head": "Pablo Isla, the", + "tail": "Telefonica, in" + }, + { + "label": "works_at", + "score": 0.0073, + "head": "A Coruna.", + "tail": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.0072, + "head": "Telefonica. The", + "tail": "Telefonica, in" + }, + { + "label": "works_at", + "score": 0.0071, + "head": "Telefonica. The", + "tail": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.007, + "head": "Inditex has", + "tail": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.0066, + "head": "Telefonica, in", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "works_at", + "score": 0.0065, + "head": "Inditex, has", + "tail": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.0062, + "head": "Telefonica, in", + "tail": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.006, + "head": "Telefonica, in", + "tail": "Pablo Isla, the" + }, + { + "label": "works_at", + "score": 0.006, + "head": "Telefonica, in", + "tail": "A Coruna." + }, + { + "label": "works_at", + "score": 0.0058, + "head": "Telefonica. The", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "works_at", + "score": 0.0051, + "head": "Pablo Isla, the", + "tail": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.0051, + "head": "A Coruna.", + "tail": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.005, + "head": "Pablo Isla, the", + "tail": "Inditex, has" + }, + { + "label": "works_at", + "score": 0.005, + "head": "A Coruna.", + "tail": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.005, + "head": "Arteixo, A", + "tail": "Inditex has" + }, + { + "label": "works_at", + "score": 0.0044, + "head": "Telefonica. The", + "tail": "Pablo Isla, the" + }, + { + "label": "works_at", + "score": 0.0044, + "head": "Telefonica. The", + "tail": "A Coruna." + }, + { + "label": "works_at", + "score": 0.004, + "head": "Inditex, has", + "tail": "Pablo Isla, the" + }, + { + "label": "works_at", + "score": 0.004, + "head": "Inditex, has", + "tail": "A Coruna." + }, + { + "label": "works_at", + "score": 0.003, + "head": "Pablo Isla, the", + "tail": "A Coruna." + }, + { + "label": "works_at", + "score": 0.003, + "head": "A Coruna.", + "tail": "Pablo Isla, the" + } + ], + "top_k=5": [ + { + "label": "appointed_as", + "score": 0.2328, + "head": "Madrid last", + "tail": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.2328, + "head": "Madrid last", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.2221, + "head": "Madrid last", + "tail": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.2096, + "head": "Telefonica, in", + "tail": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.2096, + "head": "Telefonica, in", + "tail": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.2042, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Inditex, has" + }, + { + "label": "headquartered_in", + "score": 0.1994, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.1959, + "head": "Arteixo, A", + "tail": "Madrid last" + }, + { + "label": "appointed_as", + "score": 0.1911, + "head": "Telefonica, in", + "tail": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.1825, + "head": "Telefonica. The", + "tail": "Inditex, has" + }, + { + "label": "headquartered_in", + "score": 0.1767, + "head": "Inditex has", + "tail": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.1757, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.1757, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.1738, + "head": "Inditex, has", + "tail": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.1715, + "head": "Arteixo, A", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.1705, + "head": "Pablo Isla, the", + "tail": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.1705, + "head": "A Coruna.", + "tail": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.1678, + "head": "Telefonica. The", + "tail": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.1678, + "head": "Telefonica. The", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.1642, + "head": "Telefonica, in", + "tail": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.162, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.1617, + "head": "Inditex has", + "tail": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.1614, + "head": "Arteixo, A", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.1599, + "head": "Madrid last", + "tail": "Inditex, has" + }, + { + "label": "located_in", + "score": 0.1555, + "head": "Madrid last", + "tail": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.155, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.153, + "head": "Pablo Isla, the", + "tail": "Telefonica. The" + }, + { + "label": "appointed_as", + "score": 0.153, + "head": "A Coruna.", + "tail": "Telefonica. The" + }, + { + "label": "appointed_as", + "score": 0.1488, + "head": "Inditex, has", + "tail": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.1487, + "head": "Inditex, has", + "tail": "Telefonica. The" + }, + { + "label": "headquartered_in", + "score": 0.1464, + "head": "Arteixo, A", + "tail": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.1451, + "head": "Telefonica. The", + "tail": "Inditex has" + }, + { + "label": "located_in", + "score": 0.1436, + "head": "Telefonica, in", + "tail": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.142, + "head": "Telefonica. The", + "tail": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.1415, + "head": "Inditex, has", + "tail": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.1413, + "head": "Inditex has", + "tail": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.1413, + "head": "Inditex has", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.1403, + "head": "Arteixo, A", + "tail": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.1399, + "head": "Inditex has", + "tail": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.1398, + "head": "Arteixo, A", + "tail": "Inditex, has" + }, + { + "label": "located_in", + "score": 0.1371, + "head": "Arteixo, A", + "tail": "Madrid last" + }, + { + "label": "headquartered_in", + "score": 0.1364, + "head": "Pablo Isla, the", + "tail": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.1364, + "head": "A Coruna.", + "tail": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.1321, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.1304, + "head": "Arteixo, A", + "tail": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.1292, + "head": "Inditex, has", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.1292, + "head": "Pablo Isla, the", + "tail": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.1292, + "head": "A Coruna.", + "tail": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.1284, + "head": "Pablo Isla, the", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.1284, + "head": "A Coruna.", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "located_in", + "score": 0.1254, + "head": "Telefonica. The", + "tail": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.1249, + "head": "Madrid last", + "tail": "Telefonica. The" + }, + { + "label": "appointed_as", + "score": 0.1213, + "head": "Pablo Isla, the", + "tail": "Madrid last" + }, + { + "label": "appointed_as", + "score": 0.1213, + "head": "A Coruna.", + "tail": "Madrid last" + }, + { + "label": "appointed_as", + "score": 0.1206, + "head": "Pablo Isla, the", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.1206, + "head": "A Coruna.", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.1196, + "head": "Telefonica, in", + "tail": "Telefonica. The" + }, + { + "label": "appointed_as", + "score": 0.1167, + "head": "Inditex, has", + "tail": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.1167, + "head": "Inditex, has", + "tail": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.1145, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.1111, + "head": "Inditex has", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.1096, + "head": "Inditex has", + "tail": "Inditex, has" + }, + { + "label": "headquartered_in", + "score": 0.1089, + "head": "Arteixo, A", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.1042, + "head": "Telefonica. The", + "tail": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.1036, + "head": "Pablo Isla, the", + "tail": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.1036, + "head": "A Coruna.", + "tail": "Arteixo, A" + }, + { + "label": "ceo_of", + "score": 0.0979, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Inditex has" + }, + { + "label": "located_in", + "score": 0.0974, + "head": "Arteixo, A", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "headquartered_in", + "score": 0.0951, + "head": "Arteixo, A", + "tail": "Telefonica. The" + }, + { + "label": "headquartered_in", + "score": 0.0931, + "head": "Arteixo, A", + "tail": "Pablo Isla, the" + }, + { + "label": "headquartered_in", + "score": 0.0931, + "head": "Arteixo, A", + "tail": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.0918, + "head": "Inditex, has", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.0911, + "head": "Telefonica, in", + "tail": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0899, + "head": "Telefonica. The", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.0896, + "head": "Inditex has", + "tail": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.0887, + "head": "Pablo Isla, the", + "tail": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.0887, + "head": "A Coruna.", + "tail": "Pablo Isla, the" + }, + { + "label": "located_in", + "score": 0.0867, + "head": "Arteixo, A", + "tail": "Pablo Isla, the" + }, + { + "label": "located_in", + "score": 0.0867, + "head": "Arteixo, A", + "tail": "A Coruna." + }, + { + "label": "located_in", + "score": 0.0866, + "head": "Arteixo, A", + "tail": "Telefonica. The" + }, + { + "label": "appointed_as", + "score": 0.0853, + "head": "Telefonica. The", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.0806, + "head": "Telefonica, in", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.0801, + "head": "Inditex, has", + "tail": "Madrid last" + }, + { + "label": "appointed_as", + "score": 0.073, + "head": "Madrid last", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "ceo_of", + "score": 0.0718, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0708, + "head": "Telefonica, in", + "tail": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0696, + "head": "Pablo Isla, the", + "tail": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0696, + "head": "A Coruna.", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.0682, + "head": "Madrid last", + "tail": "Telefonica, in" + }, + { + "label": "ceo_of", + "score": 0.0649, + "head": "Telefonica, in", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.0644, + "head": "Telefonica. The", + "tail": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.0607, + "head": "Inditex, has", + "tail": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.0602, + "head": "Madrid last", + "tail": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0577, + "head": "Pablo Isla, the", + "tail": "Telefonica, in" + }, + { + "label": "ceo_of", + "score": 0.0577, + "head": "A Coruna.", + "tail": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.0571, + "head": "Telefonica, in", + "tail": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0556, + "head": "Inditex, has", + "tail": "Telefonica, in" + }, + { + "label": "ceo_of", + "score": 0.055, + "head": "Telefonica, in", + "tail": "Pablo Isla, the" + }, + { + "label": "ceo_of", + "score": 0.055, + "head": "Telefonica, in", + "tail": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0542, + "head": "Telefonica. The", + "tail": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.0532, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.0524, + "head": "Inditex has", + "tail": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.0515, + "head": "Madrid last", + "tail": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.0482, + "head": "Telefonica, in", + "tail": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0479, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Pablo Isla, the" + }, + { + "label": "ceo_of", + "score": 0.0479, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0473, + "head": "Inditex has", + "tail": "Telefonica. The" + }, + { + "label": "ceo_of", + "score": 0.0467, + "head": "Inditex has", + "tail": "Pablo Isla, the" + }, + { + "label": "ceo_of", + "score": 0.0467, + "head": "Inditex has", + "tail": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0455, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Telefonica, in" + }, + { + "label": "ceo_of", + "score": 0.0427, + "head": "Inditex has", + "tail": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.0425, + "head": "Madrid last", + "tail": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0419, + "head": "Inditex has", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "located_in", + "score": 0.0412, + "head": "Inditex has", + "tail": "Telefonica, in" + }, + { + "label": "headquartered_in", + "score": 0.0401, + "head": "Inditex has", + "tail": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.04, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Telefonica. The" + }, + { + "label": "headquartered_in", + "score": 0.0396, + "head": "Madrid last", + "tail": "Inditex, has" + }, + { + "label": "located_in", + "score": 0.0394, + "head": "Inditex, has", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.0387, + "head": "Arteixo, A", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "ceo_of", + "score": 0.0382, + "head": "Inditex, has", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "ceo_of", + "score": 0.0378, + "head": "Inditex, has", + "tail": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.0374, + "head": "Inditex has", + "tail": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0372, + "head": "Telefonica. The", + "tail": "Telefonica, in" + }, + { + "label": "ceo_of", + "score": 0.0363, + "head": "Telefonica. The", + "tail": "Pablo Isla, the" + }, + { + "label": "ceo_of", + "score": 0.0363, + "head": "Telefonica. The", + "tail": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.0362, + "head": "Telefonica, in", + "tail": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.0351, + "head": "Pablo Isla, the", + "tail": "Telefonica. The" + }, + { + "label": "ceo_of", + "score": 0.0351, + "head": "A Coruna.", + "tail": "Telefonica. The" + }, + { + "label": "ceo_of", + "score": 0.035, + "head": "Pablo Isla, the", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "ceo_of", + "score": 0.035, + "head": "A Coruna.", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "ceo_of", + "score": 0.0349, + "head": "Pablo Isla, the", + "tail": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0349, + "head": "A Coruna.", + "tail": "Inditex, has" + }, + { + "label": "headquartered_in", + "score": 0.0349, + "head": "Inditex has", + "tail": "Telefonica, in" + }, + { + "label": "headquartered_in", + "score": 0.0347, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0341, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.0339, + "head": "Arteixo, A", + "tail": "Telefonica. The" + }, + { + "label": "ceo_of", + "score": 0.0335, + "head": "Madrid last", + "tail": "Pablo Isla, the" + }, + { + "label": "ceo_of", + "score": 0.0335, + "head": "Madrid last", + "tail": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0335, + "head": "Telefonica, in", + "tail": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.0327, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.0326, + "head": "Inditex, has", + "tail": "Madrid last" + }, + { + "label": "headquartered_in", + "score": 0.031, + "head": "Inditex, has", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.0303, + "head": "Inditex has", + "tail": "Madrid last" + }, + { + "label": "located_in", + "score": 0.0288, + "head": "Inditex has", + "tail": "Madrid last" + }, + { + "label": "headquartered_in", + "score": 0.0282, + "head": "Inditex has", + "tail": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.0275, + "head": "Arteixo, A", + "tail": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.0275, + "head": "Arteixo, A", + "tail": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0274, + "head": "Inditex, has", + "tail": "Pablo Isla, the" + }, + { + "label": "ceo_of", + "score": 0.0274, + "head": "Inditex, has", + "tail": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.026, + "head": "Madrid last", + "tail": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.025, + "head": "Telefonica, in", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "ceo_of", + "score": 0.0243, + "head": "Telefonica. The", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "located_in", + "score": 0.0241, + "head": "Telefonica. The", + "tail": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.024, + "head": "Madrid last", + "tail": "Telefonica, in" + }, + { + "label": "ceo_of", + "score": 0.0234, + "head": "Telefonica. The", + "tail": "Arteixo, A" + }, + { + "label": "ceo_of", + "score": 0.0232, + "head": "Pablo Isla, the", + "tail": "Arteixo, A" + }, + { + "label": "ceo_of", + "score": 0.0232, + "head": "A Coruna.", + "tail": "Arteixo, A" + }, + { + "label": "ceo_of", + "score": 0.0226, + "head": "Arteixo, A", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "located_in", + "score": 0.0224, + "head": "Madrid last", + "tail": "Inditex, has" + }, + { + "label": "headquartered_in", + "score": 0.0209, + "head": "Telefonica. The", + "tail": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0205, + "head": "Pablo Isla, the", + "tail": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.0205, + "head": "A Coruna.", + "tail": "Madrid last" + }, + { + "label": "headquartered_in", + "score": 0.02, + "head": "Telefonica, in", + "tail": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.0197, + "head": "Pablo Isla, the", + "tail": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0197, + "head": "A Coruna.", + "tail": "Pablo Isla, the" + }, + { + "label": "located_in", + "score": 0.0189, + "head": "Telefonica, in", + "tail": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0183, + "head": "Madrid last", + "tail": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.0181, + "head": "Inditex, has", + "tail": "Madrid last" + }, + { + "label": "headquartered_in", + "score": 0.0181, + "head": "Telefonica, in", + "tail": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.017, + "head": "Arteixo, A", + "tail": "Telefonica. The" + }, + { + "label": "appointed_as", + "score": 0.0169, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.0169, + "head": "Arteixo, A", + "tail": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.0168, + "head": "Inditex has", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "headquartered_in", + "score": 0.0165, + "head": "Telefonica. The", + "tail": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.0165, + "head": "Arteixo, A", + "tail": "Pablo Isla, the" + }, + { + "label": "ceo_of", + "score": 0.0165, + "head": "Arteixo, A", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.0162, + "head": "Pablo Isla, the", + "tail": "Madrid last" + }, + { + "label": "headquartered_in", + "score": 0.0162, + "head": "A Coruna.", + "tail": "Madrid last" + }, + { + "label": "headquartered_in", + "score": 0.0161, + "head": "Inditex has", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "headquartered_in", + "score": 0.016, + "head": "Madrid last", + "tail": "Pablo Isla, the" + }, + { + "label": "headquartered_in", + "score": 0.016, + "head": "Madrid last", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.0153, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.0153, + "head": "Inditex, has", + "tail": "Madrid last" + }, + { + "label": "located_in", + "score": 0.0152, + "head": "Madrid last", + "tail": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.0149, + "head": "Inditex, has", + "tail": "Telefonica, in" + }, + { + "label": "headquartered_in", + "score": 0.0147, + "head": "Inditex, has", + "tail": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.0146, + "head": "Arteixo, A", + "tail": "Madrid last" + }, + { + "label": "works_at", + "score": 0.0144, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Inditex, has" + }, + { + "label": "headquartered_in", + "score": 0.0143, + "head": "Madrid last", + "tail": "Telefonica. The" + }, + { + "label": "appointed_as", + "score": 0.014, + "head": "Arteixo, A", + "tail": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0139, + "head": "Telefonica, in", + "tail": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.0134, + "head": "Inditex has", + "tail": "Telefonica. The" + }, + { + "label": "ceo_of", + "score": 0.0133, + "head": "Inditex, has", + "tail": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.0129, + "head": "Telefonica, in", + "tail": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.0128, + "head": "Telefonica. The", + "tail": "Madrid last" + }, + { + "label": "works_at", + "score": 0.0127, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0127, + "head": "Madrid last", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "works_at", + "score": 0.0126, + "head": "Madrid last", + "tail": "Pablo Isla, the" + }, + { + "label": "works_at", + "score": 0.0126, + "head": "Madrid last", + "tail": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0124, + "head": "Madrid last", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.0118, + "head": "Pablo Isla, the", + "tail": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.0118, + "head": "A Coruna.", + "tail": "Arteixo, A" + }, + { + "label": "works_at", + "score": 0.0118, + "head": "Pablo Isla, the", + "tail": "Madrid last" + }, + { + "label": "works_at", + "score": 0.0118, + "head": "A Coruna.", + "tail": "Madrid last" + }, + { + "label": "appointed_as", + "score": 0.0117, + "head": "Telefonica. The", + "tail": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.0113, + "head": "Inditex has", + "tail": "Telefonica. The" + }, + { + "label": "headquartered_in", + "score": 0.0112, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0111, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Madrid last" + }, + { + "label": "works_at", + "score": 0.011, + "head": "Inditex, has", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "headquartered_in", + "score": 0.0109, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Telefonica, in" + }, + { + "label": "ceo_of", + "score": 0.0107, + "head": "Madrid last", + "tail": "Telefonica, in" + }, + { + "label": "ceo_of", + "score": 0.0106, + "head": "Arteixo, A", + "tail": "Telefonica, in" + }, + { + "label": "works_at", + "score": 0.0103, + "head": "Madrid last", + "tail": "Inditex, has" + }, + { + "label": "works_at", + "score": 0.0101, + "head": "Pablo Isla, the", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "works_at", + "score": 0.0101, + "head": "A Coruna.", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "headquartered_in", + "score": 0.01, + "head": "Madrid last", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "located_in", + "score": 0.0098, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Telefonica, in" + }, + { + "label": "works_at", + "score": 0.0098, + "head": "Inditex has", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "works_at", + "score": 0.0097, + "head": "Inditex, has", + "tail": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.0097, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0096, + "head": "Arteixo, A", + "tail": "Inditex, has" + }, + { + "label": "works_at", + "score": 0.0095, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Pablo Isla, the" + }, + { + "label": "works_at", + "score": 0.0095, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "A Coruna." + }, + { + "label": "located_in", + "score": 0.0094, + "head": "Telefonica. The", + "tail": "Madrid last" + }, + { + "label": "located_in", + "score": 0.0094, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Madrid last" + }, + { + "label": "works_at", + "score": 0.0093, + "head": "Telefonica, in", + "tail": "Inditex, has" + }, + { + "label": "works_at", + "score": 0.0093, + "head": "Inditex, has", + "tail": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.009, + "head": "Madrid last", + "tail": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.0088, + "head": "Madrid last", + "tail": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.0086, + "head": "Madrid last", + "tail": "Pablo Isla, the" + }, + { + "label": "located_in", + "score": 0.0086, + "head": "Madrid last", + "tail": "A Coruna." + }, + { + "label": "located_in", + "score": 0.0086, + "head": "Pablo Isla, the", + "tail": "Inditex has" + }, + { + "label": "located_in", + "score": 0.0086, + "head": "A Coruna.", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.0081, + "head": "Madrid last", + "tail": "Arteixo, A" + }, + { + "label": "works_at", + "score": 0.0081, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.008, + "head": "Inditex has", + "tail": "Pablo Isla, the" + }, + { + "label": "located_in", + "score": 0.008, + "head": "Inditex has", + "tail": "A Coruna." + }, + { + "label": "works_at", + "score": 0.0079, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.0075, + "head": "Pablo Isla, the", + "tail": "Madrid last" + }, + { + "label": "located_in", + "score": 0.0075, + "head": "A Coruna.", + "tail": "Madrid last" + }, + { + "label": "works_at", + "score": 0.0073, + "head": "Pablo Isla, the", + "tail": "Telefonica, in" + }, + { + "label": "works_at", + "score": 0.0073, + "head": "A Coruna.", + "tail": "Telefonica, in" + }, + { + "label": "works_at", + "score": 0.0073, + "head": "Inditex, has", + "tail": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.0072, + "head": "Inditex, has", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "headquartered_in", + "score": 0.0072, + "head": "Pablo Isla, the", + "tail": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.0072, + "head": "A Coruna.", + "tail": "Inditex has" + }, + { + "label": "works_at", + "score": 0.0072, + "head": "Arteixo, A", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "located_in", + "score": 0.0072, + "head": "Madrid last", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "located_in", + "score": 0.0072, + "head": "Telefonica. The", + "tail": "Telefonica, in" + }, + { + "label": "works_at", + "score": 0.0071, + "head": "Telefonica. The", + "tail": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.0071, + "head": "Telefonica. The", + "tail": "Telefonica, in" + }, + { + "label": "works_at", + "score": 0.0071, + "head": "Telefonica. The", + "tail": "Inditex, has" + }, + { + "label": "works_at", + "score": 0.0071, + "head": "Inditex has", + "tail": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.007, + "head": "Inditex, has", + "tail": "Arteixo, A" + }, + { + "label": "ceo_of", + "score": 0.007, + "head": "Telefonica, in", + "tail": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.007, + "head": "Inditex has", + "tail": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.007, + "head": "Telefonica, in", + "tail": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.0069, + "head": "Inditex, has", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "headquartered_in", + "score": 0.0066, + "head": "Telefonica, in", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "works_at", + "score": 0.0066, + "head": "Inditex has", + "tail": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.0066, + "head": "Telefonica, in", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "works_at", + "score": 0.0065, + "head": "Inditex, has", + "tail": "Telefonica. The" + }, + { + "label": "ceo_of", + "score": 0.0064, + "head": "Inditex has", + "tail": "Madrid last" + }, + { + "label": "works_at", + "score": 0.0064, + "head": "Madrid last", + "tail": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.0063, + "head": "Inditex has", + "tail": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.0062, + "head": "Telefonica, in", + "tail": "Telefonica. The" + }, + { + "label": "headquartered_in", + "score": 0.006, + "head": "Inditex has", + "tail": "Pablo Isla, the" + }, + { + "label": "headquartered_in", + "score": 0.006, + "head": "Inditex has", + "tail": "A Coruna." + }, + { + "label": "works_at", + "score": 0.006, + "head": "Telefonica, in", + "tail": "Pablo Isla, the" + }, + { + "label": "works_at", + "score": 0.006, + "head": "Telefonica, in", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.0058, + "head": "Telefonica, in", + "tail": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.0058, + "head": "Telefonica. The", + "tail": "Telefonica, in" + }, + { + "label": "works_at", + "score": 0.0058, + "head": "Telefonica. The", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "works_at", + "score": 0.0057, + "head": "Telefonica. The", + "tail": "Madrid last" + }, + { + "label": "works_at", + "score": 0.0057, + "head": "Telefonica, in", + "tail": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0057, + "head": "Arteixo, A", + "tail": "Madrid last" + }, + { + "label": "works_at", + "score": 0.0056, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Madrid last" + }, + { + "label": "works_at", + "score": 0.0055, + "head": "Telefonica, in", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "works_at", + "score": 0.0054, + "head": "Telefonica, in", + "tail": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.0053, + "head": "Telefonica. The", + "tail": "Inditex, has" + }, + { + "label": "works_at", + "score": 0.0051, + "head": "Pablo Isla, the", + "tail": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.0051, + "head": "A Coruna.", + "tail": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.0051, + "head": "Pablo Isla, the", + "tail": "Inditex has" + }, + { + "label": "works_at", + "score": 0.0051, + "head": "A Coruna.", + "tail": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.0051, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.005, + "head": "Pablo Isla, the", + "tail": "Inditex, has" + }, + { + "label": "works_at", + "score": 0.005, + "head": "A Coruna.", + "tail": "Inditex, has" + }, + { + "label": "works_at", + "score": 0.005, + "head": "Madrid last", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.005, + "head": "Arteixo, A", + "tail": "Inditex has" + }, + { + "label": "located_in", + "score": 0.0049, + "head": "Telefonica, in", + "tail": "Pablo Isla, the" + }, + { + "label": "located_in", + "score": 0.0049, + "head": "Telefonica, in", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.0049, + "head": "Telefonica. The", + "tail": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0048, + "head": "Arteixo, A", + "tail": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.0047, + "head": "Telefonica, in", + "tail": "Pablo Isla, the" + }, + { + "label": "headquartered_in", + "score": 0.0047, + "head": "Telefonica, in", + "tail": "A Coruna." + }, + { + "label": "works_at", + "score": 0.0047, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Arteixo, A" + }, + { + "label": "works_at", + "score": 0.0047, + "head": "Arteixo, A", + "tail": "Madrid last" + }, + { + "label": "located_in", + "score": 0.0045, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.0045, + "head": "Inditex has", + "tail": "Pablo Isla, the" + }, + { + "label": "works_at", + "score": 0.0045, + "head": "Inditex has", + "tail": "A Coruna." + }, + { + "label": "works_at", + "score": 0.0044, + "head": "Telefonica. The", + "tail": "Pablo Isla, the" + }, + { + "label": "works_at", + "score": 0.0044, + "head": "Telefonica. The", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.0043, + "head": "Pablo Isla, the", + "tail": "Telefonica, in" + }, + { + "label": "headquartered_in", + "score": 0.0043, + "head": "A Coruna.", + "tail": "Telefonica, in" + }, + { + "label": "works_at", + "score": 0.004, + "head": "Inditex, has", + "tail": "Pablo Isla, the" + }, + { + "label": "works_at", + "score": 0.004, + "head": "Inditex, has", + "tail": "A Coruna." + }, + { + "label": "works_at", + "score": 0.004, + "head": "Madrid last", + "tail": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.004, + "head": "Pablo Isla, the", + "tail": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.004, + "head": "A Coruna.", + "tail": "Telefonica, in" + }, + { + "label": "headquartered_in", + "score": 0.0039, + "head": "Telefonica. The", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "located_in", + "score": 0.0038, + "head": "Inditex, has", + "tail": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.0037, + "head": "Madrid last", + "tail": "Inditex has" + }, + { + "label": "works_at", + "score": 0.0037, + "head": "Inditex has", + "tail": "Madrid last" + }, + { + "label": "located_in", + "score": 0.0037, + "head": "Telefonica. The", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "headquartered_in", + "score": 0.0036, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Pablo Isla, the" + }, + { + "label": "headquartered_in", + "score": 0.0036, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.0035, + "head": "Inditex has", + "tail": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.0034, + "head": "Inditex, has", + "tail": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.0034, + "head": "Arteixo, A", + "tail": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.0033, + "head": "Telefonica, in", + "tail": "Madrid last" + }, + { + "label": "located_in", + "score": 0.0032, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Pablo Isla, the" + }, + { + "label": "located_in", + "score": 0.0032, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.0032, + "head": "Pablo Isla, the", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "headquartered_in", + "score": 0.0032, + "head": "A Coruna.", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "works_at", + "score": 0.003, + "head": "Pablo Isla, the", + "tail": "A Coruna." + }, + { + "label": "works_at", + "score": 0.003, + "head": "A Coruna.", + "tail": "Pablo Isla, the" + }, + { + "label": "works_at", + "score": 0.003, + "head": "Arteixo, A", + "tail": "Pablo Isla, the" + }, + { + "label": "works_at", + "score": 0.003, + "head": "Arteixo, A", + "tail": "A Coruna." + }, + { + "label": "located_in", + "score": 0.0028, + "head": "Pablo Isla, the", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "located_in", + "score": 0.0028, + "head": "A Coruna.", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "works_at", + "score": 0.0027, + "head": "Arteixo, A", + "tail": "Telefonica, in" + }, + { + "label": "works_at", + "score": 0.0025, + "head": "Pablo Isla, the", + "tail": "Arteixo, A" + }, + { + "label": "works_at", + "score": 0.0025, + "head": "A Coruna.", + "tail": "Arteixo, A" + }, + { + "label": "works_at", + "score": 0.0024, + "head": "Madrid last", + "tail": "Arteixo, A" + }, + { + "label": "works_at", + "score": 0.0022, + "head": "Arteixo, A", + "tail": "Inditex, has" + }, + { + "label": "works_at", + "score": 0.0021, + "head": "Telefonica. The", + "tail": "Arteixo, A" + }, + { + "label": "works_at", + "score": 0.0018, + "head": "Inditex, has", + "tail": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.0018, + "head": "Pablo Isla, the", + "tail": "Inditex, has" + }, + { + "label": "located_in", + "score": 0.0018, + "head": "A Coruna.", + "tail": "Inditex, has" + }, + { + "label": "located_in", + "score": 0.0018, + "head": "Inditex, has", + "tail": "Pablo Isla, the" + }, + { + "label": "located_in", + "score": 0.0018, + "head": "Inditex, has", + "tail": "A Coruna." + }, + { + "label": "located_in", + "score": 0.0016, + "head": "Telefonica. The", + "tail": "Pablo Isla, the" + }, + { + "label": "located_in", + "score": 0.0016, + "head": "Telefonica. The", + "tail": "A Coruna." + }, + { + "label": "works_at", + "score": 0.0016, + "head": "Telefonica, in", + "tail": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.0014, + "head": "Pablo Isla, the", + "tail": "Inditex, has" + }, + { + "label": "headquartered_in", + "score": 0.0014, + "head": "A Coruna.", + "tail": "Inditex, has" + }, + { + "label": "headquartered_in", + "score": 0.0014, + "head": "Telefonica. The", + "tail": "Pablo Isla, the" + }, + { + "label": "headquartered_in", + "score": 0.0014, + "head": "Telefonica. The", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.0013, + "head": "Inditex, has", + "tail": "Pablo Isla, the" + }, + { + "label": "headquartered_in", + "score": 0.0013, + "head": "Inditex, has", + "tail": "A Coruna." + }, + { + "label": "located_in", + "score": 0.0013, + "head": "Pablo Isla, the", + "tail": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.0013, + "head": "A Coruna.", + "tail": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.0012, + "head": "Arteixo, A", + "tail": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.0012, + "head": "Pablo Isla, the", + "tail": "Telefonica. The" + }, + { + "label": "headquartered_in", + "score": 0.0012, + "head": "A Coruna.", + "tail": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.0012, + "head": "Inditex has", + "tail": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.0006, + "head": "Pablo Isla, the", + "tail": "A Coruna." + }, + { + "label": "located_in", + "score": 0.0006, + "head": "A Coruna.", + "tail": "Pablo Isla, the" + }, + { + "label": "headquartered_in", + "score": 0.0004, + "head": "Pablo Isla, the", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.0004, + "head": "A Coruna.", + "tail": "Pablo Isla, the" + } + ], + "top_k=10": [ + { + "label": "appointed_as", + "score": 0.2328, + "head": "Madrid last", + "tail": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.2328, + "head": "Madrid last", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.2221, + "head": "Madrid last", + "tail": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.2096, + "head": "Telefonica, in", + "tail": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.2096, + "head": "Telefonica, in", + "tail": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.2042, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Inditex, has" + }, + { + "label": "headquartered_in", + "score": 0.1994, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.1959, + "head": "Arteixo, A", + "tail": "Madrid last" + }, + { + "label": "appointed_as", + "score": 0.1911, + "head": "Telefonica, in", + "tail": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.1825, + "head": "Telefonica. The", + "tail": "Inditex, has" + }, + { + "label": "headquartered_in", + "score": 0.1767, + "head": "Inditex has", + "tail": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.1757, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.1757, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.1738, + "head": "Inditex, has", + "tail": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.1715, + "head": "Arteixo, A", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.1705, + "head": "Pablo Isla, the", + "tail": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.1705, + "head": "A Coruna.", + "tail": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.1678, + "head": "Telefonica. The", + "tail": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.1678, + "head": "Telefonica. The", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.1642, + "head": "Telefonica, in", + "tail": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.162, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.1617, + "head": "Inditex has", + "tail": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.1614, + "head": "Arteixo, A", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.1599, + "head": "Madrid last", + "tail": "Inditex, has" + }, + { + "label": "located_in", + "score": 0.1555, + "head": "Madrid last", + "tail": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.155, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.153, + "head": "Pablo Isla, the", + "tail": "Telefonica. The" + }, + { + "label": "appointed_as", + "score": 0.153, + "head": "A Coruna.", + "tail": "Telefonica. The" + }, + { + "label": "appointed_as", + "score": 0.1488, + "head": "Inditex, has", + "tail": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.1487, + "head": "Inditex, has", + "tail": "Telefonica. The" + }, + { + "label": "headquartered_in", + "score": 0.1464, + "head": "Arteixo, A", + "tail": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.1451, + "head": "Telefonica. The", + "tail": "Inditex has" + }, + { + "label": "located_in", + "score": 0.1436, + "head": "Telefonica, in", + "tail": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.142, + "head": "Telefonica. The", + "tail": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.1415, + "head": "Inditex, has", + "tail": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.1413, + "head": "Inditex has", + "tail": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.1413, + "head": "Inditex has", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.1403, + "head": "Arteixo, A", + "tail": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.1399, + "head": "Inditex has", + "tail": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.1398, + "head": "Arteixo, A", + "tail": "Inditex, has" + }, + { + "label": "located_in", + "score": 0.1371, + "head": "Arteixo, A", + "tail": "Madrid last" + }, + { + "label": "headquartered_in", + "score": 0.1364, + "head": "Pablo Isla, the", + "tail": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.1364, + "head": "A Coruna.", + "tail": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.1321, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.1304, + "head": "Arteixo, A", + "tail": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.1292, + "head": "Inditex, has", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.1292, + "head": "Pablo Isla, the", + "tail": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.1292, + "head": "A Coruna.", + "tail": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.1284, + "head": "Pablo Isla, the", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.1284, + "head": "A Coruna.", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "located_in", + "score": 0.1254, + "head": "Telefonica. The", + "tail": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.1249, + "head": "Madrid last", + "tail": "Telefonica. The" + }, + { + "label": "appointed_as", + "score": 0.1213, + "head": "Pablo Isla, the", + "tail": "Madrid last" + }, + { + "label": "appointed_as", + "score": 0.1213, + "head": "A Coruna.", + "tail": "Madrid last" + }, + { + "label": "appointed_as", + "score": 0.1206, + "head": "Pablo Isla, the", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.1206, + "head": "A Coruna.", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.1196, + "head": "Telefonica, in", + "tail": "Telefonica. The" + }, + { + "label": "appointed_as", + "score": 0.1167, + "head": "Inditex, has", + "tail": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.1167, + "head": "Inditex, has", + "tail": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.1145, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.1111, + "head": "Inditex has", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.1096, + "head": "Inditex has", + "tail": "Inditex, has" + }, + { + "label": "headquartered_in", + "score": 0.1089, + "head": "Arteixo, A", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.1042, + "head": "Telefonica. The", + "tail": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.1036, + "head": "Pablo Isla, the", + "tail": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.1036, + "head": "A Coruna.", + "tail": "Arteixo, A" + }, + { + "label": "ceo_of", + "score": 0.0979, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Inditex has" + }, + { + "label": "located_in", + "score": 0.0974, + "head": "Arteixo, A", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "headquartered_in", + "score": 0.0951, + "head": "Arteixo, A", + "tail": "Telefonica. The" + }, + { + "label": "headquartered_in", + "score": 0.0931, + "head": "Arteixo, A", + "tail": "Pablo Isla, the" + }, + { + "label": "headquartered_in", + "score": 0.0931, + "head": "Arteixo, A", + "tail": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.0918, + "head": "Inditex, has", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.0911, + "head": "Telefonica, in", + "tail": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0899, + "head": "Telefonica. The", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.0896, + "head": "Inditex has", + "tail": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.0887, + "head": "Pablo Isla, the", + "tail": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.0887, + "head": "A Coruna.", + "tail": "Pablo Isla, the" + }, + { + "label": "located_in", + "score": 0.0867, + "head": "Arteixo, A", + "tail": "Pablo Isla, the" + }, + { + "label": "located_in", + "score": 0.0867, + "head": "Arteixo, A", + "tail": "A Coruna." + }, + { + "label": "located_in", + "score": 0.0866, + "head": "Arteixo, A", + "tail": "Telefonica. The" + }, + { + "label": "appointed_as", + "score": 0.0853, + "head": "Telefonica. The", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.0806, + "head": "Telefonica, in", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.0801, + "head": "Inditex, has", + "tail": "Madrid last" + }, + { + "label": "appointed_as", + "score": 0.073, + "head": "Madrid last", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "ceo_of", + "score": 0.0718, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0708, + "head": "Telefonica, in", + "tail": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0696, + "head": "Pablo Isla, the", + "tail": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0696, + "head": "A Coruna.", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.0682, + "head": "Madrid last", + "tail": "Telefonica, in" + }, + { + "label": "ceo_of", + "score": 0.0649, + "head": "Telefonica, in", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.0644, + "head": "Telefonica. The", + "tail": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.0607, + "head": "Inditex, has", + "tail": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.0602, + "head": "Madrid last", + "tail": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0577, + "head": "Pablo Isla, the", + "tail": "Telefonica, in" + }, + { + "label": "ceo_of", + "score": 0.0577, + "head": "A Coruna.", + "tail": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.0571, + "head": "Telefonica, in", + "tail": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0556, + "head": "Inditex, has", + "tail": "Telefonica, in" + }, + { + "label": "ceo_of", + "score": 0.055, + "head": "Telefonica, in", + "tail": "Pablo Isla, the" + }, + { + "label": "ceo_of", + "score": 0.055, + "head": "Telefonica, in", + "tail": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0542, + "head": "Telefonica. The", + "tail": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.0532, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.0524, + "head": "Inditex has", + "tail": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.0515, + "head": "Madrid last", + "tail": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.0482, + "head": "Telefonica, in", + "tail": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0479, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Pablo Isla, the" + }, + { + "label": "ceo_of", + "score": 0.0479, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0473, + "head": "Inditex has", + "tail": "Telefonica. The" + }, + { + "label": "ceo_of", + "score": 0.0467, + "head": "Inditex has", + "tail": "Pablo Isla, the" + }, + { + "label": "ceo_of", + "score": 0.0467, + "head": "Inditex has", + "tail": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0455, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Telefonica, in" + }, + { + "label": "ceo_of", + "score": 0.0427, + "head": "Inditex has", + "tail": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.0425, + "head": "Madrid last", + "tail": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0419, + "head": "Inditex has", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "located_in", + "score": 0.0412, + "head": "Inditex has", + "tail": "Telefonica, in" + }, + { + "label": "headquartered_in", + "score": 0.0401, + "head": "Inditex has", + "tail": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.04, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Telefonica. The" + }, + { + "label": "headquartered_in", + "score": 0.0396, + "head": "Madrid last", + "tail": "Inditex, has" + }, + { + "label": "located_in", + "score": 0.0394, + "head": "Inditex, has", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.0387, + "head": "Arteixo, A", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "ceo_of", + "score": 0.0382, + "head": "Inditex, has", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "ceo_of", + "score": 0.0378, + "head": "Inditex, has", + "tail": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.0374, + "head": "Inditex has", + "tail": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0372, + "head": "Telefonica. The", + "tail": "Telefonica, in" + }, + { + "label": "ceo_of", + "score": 0.0363, + "head": "Telefonica. The", + "tail": "Pablo Isla, the" + }, + { + "label": "ceo_of", + "score": 0.0363, + "head": "Telefonica. The", + "tail": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.0362, + "head": "Telefonica, in", + "tail": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.0351, + "head": "Pablo Isla, the", + "tail": "Telefonica. The" + }, + { + "label": "ceo_of", + "score": 0.0351, + "head": "A Coruna.", + "tail": "Telefonica. The" + }, + { + "label": "ceo_of", + "score": 0.035, + "head": "Pablo Isla, the", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "ceo_of", + "score": 0.035, + "head": "A Coruna.", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "ceo_of", + "score": 0.0349, + "head": "Pablo Isla, the", + "tail": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0349, + "head": "A Coruna.", + "tail": "Inditex, has" + }, + { + "label": "headquartered_in", + "score": 0.0349, + "head": "Inditex has", + "tail": "Telefonica, in" + }, + { + "label": "headquartered_in", + "score": 0.0347, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0341, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.0339, + "head": "Arteixo, A", + "tail": "Telefonica. The" + }, + { + "label": "ceo_of", + "score": 0.0335, + "head": "Madrid last", + "tail": "Pablo Isla, the" + }, + { + "label": "ceo_of", + "score": 0.0335, + "head": "Madrid last", + "tail": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0335, + "head": "Telefonica, in", + "tail": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.0327, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.0326, + "head": "Inditex, has", + "tail": "Madrid last" + }, + { + "label": "headquartered_in", + "score": 0.031, + "head": "Inditex, has", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.0303, + "head": "Inditex has", + "tail": "Madrid last" + }, + { + "label": "located_in", + "score": 0.0288, + "head": "Inditex has", + "tail": "Madrid last" + }, + { + "label": "headquartered_in", + "score": 0.0282, + "head": "Inditex has", + "tail": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.0275, + "head": "Arteixo, A", + "tail": "Pablo Isla, the" + }, + { + "label": "appointed_as", + "score": 0.0275, + "head": "Arteixo, A", + "tail": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0274, + "head": "Inditex, has", + "tail": "Pablo Isla, the" + }, + { + "label": "ceo_of", + "score": 0.0274, + "head": "Inditex, has", + "tail": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.026, + "head": "Madrid last", + "tail": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.025, + "head": "Telefonica, in", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "ceo_of", + "score": 0.0243, + "head": "Telefonica. The", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "located_in", + "score": 0.0241, + "head": "Telefonica. The", + "tail": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.024, + "head": "Madrid last", + "tail": "Telefonica, in" + }, + { + "label": "ceo_of", + "score": 0.0234, + "head": "Telefonica. The", + "tail": "Arteixo, A" + }, + { + "label": "ceo_of", + "score": 0.0232, + "head": "Pablo Isla, the", + "tail": "Arteixo, A" + }, + { + "label": "ceo_of", + "score": 0.0232, + "head": "A Coruna.", + "tail": "Arteixo, A" + }, + { + "label": "ceo_of", + "score": 0.0226, + "head": "Arteixo, A", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "located_in", + "score": 0.0224, + "head": "Madrid last", + "tail": "Inditex, has" + }, + { + "label": "headquartered_in", + "score": 0.0209, + "head": "Telefonica. The", + "tail": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0205, + "head": "Pablo Isla, the", + "tail": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.0205, + "head": "A Coruna.", + "tail": "Madrid last" + }, + { + "label": "headquartered_in", + "score": 0.02, + "head": "Telefonica, in", + "tail": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.0197, + "head": "Pablo Isla, the", + "tail": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0197, + "head": "A Coruna.", + "tail": "Pablo Isla, the" + }, + { + "label": "located_in", + "score": 0.0189, + "head": "Telefonica, in", + "tail": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0183, + "head": "Madrid last", + "tail": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.0181, + "head": "Inditex, has", + "tail": "Madrid last" + }, + { + "label": "headquartered_in", + "score": 0.0181, + "head": "Telefonica, in", + "tail": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.017, + "head": "Arteixo, A", + "tail": "Telefonica. The" + }, + { + "label": "appointed_as", + "score": 0.0169, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.0169, + "head": "Arteixo, A", + "tail": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.0168, + "head": "Inditex has", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "headquartered_in", + "score": 0.0165, + "head": "Telefonica. The", + "tail": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.0165, + "head": "Arteixo, A", + "tail": "Pablo Isla, the" + }, + { + "label": "ceo_of", + "score": 0.0165, + "head": "Arteixo, A", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.0162, + "head": "Pablo Isla, the", + "tail": "Madrid last" + }, + { + "label": "headquartered_in", + "score": 0.0162, + "head": "A Coruna.", + "tail": "Madrid last" + }, + { + "label": "headquartered_in", + "score": 0.0161, + "head": "Inditex has", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "headquartered_in", + "score": 0.016, + "head": "Madrid last", + "tail": "Pablo Isla, the" + }, + { + "label": "headquartered_in", + "score": 0.016, + "head": "Madrid last", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.0153, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.0153, + "head": "Inditex, has", + "tail": "Madrid last" + }, + { + "label": "located_in", + "score": 0.0152, + "head": "Madrid last", + "tail": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.0149, + "head": "Inditex, has", + "tail": "Telefonica, in" + }, + { + "label": "headquartered_in", + "score": 0.0147, + "head": "Inditex, has", + "tail": "Telefonica, in" + }, + { + "label": "appointed_as", + "score": 0.0146, + "head": "Arteixo, A", + "tail": "Madrid last" + }, + { + "label": "works_at", + "score": 0.0144, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Inditex, has" + }, + { + "label": "headquartered_in", + "score": 0.0143, + "head": "Madrid last", + "tail": "Telefonica. The" + }, + { + "label": "appointed_as", + "score": 0.014, + "head": "Arteixo, A", + "tail": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0139, + "head": "Telefonica, in", + "tail": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.0134, + "head": "Inditex has", + "tail": "Telefonica. The" + }, + { + "label": "ceo_of", + "score": 0.0133, + "head": "Inditex, has", + "tail": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.0129, + "head": "Telefonica, in", + "tail": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.0128, + "head": "Telefonica. The", + "tail": "Madrid last" + }, + { + "label": "works_at", + "score": 0.0127, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0127, + "head": "Madrid last", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "works_at", + "score": 0.0126, + "head": "Madrid last", + "tail": "Pablo Isla, the" + }, + { + "label": "works_at", + "score": 0.0126, + "head": "Madrid last", + "tail": "A Coruna." + }, + { + "label": "ceo_of", + "score": 0.0124, + "head": "Madrid last", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.0118, + "head": "Pablo Isla, the", + "tail": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.0118, + "head": "A Coruna.", + "tail": "Arteixo, A" + }, + { + "label": "works_at", + "score": 0.0118, + "head": "Pablo Isla, the", + "tail": "Madrid last" + }, + { + "label": "works_at", + "score": 0.0118, + "head": "A Coruna.", + "tail": "Madrid last" + }, + { + "label": "appointed_as", + "score": 0.0117, + "head": "Telefonica. The", + "tail": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.0113, + "head": "Inditex has", + "tail": "Telefonica. The" + }, + { + "label": "headquartered_in", + "score": 0.0112, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0111, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Madrid last" + }, + { + "label": "works_at", + "score": 0.011, + "head": "Inditex, has", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "headquartered_in", + "score": 0.0109, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Telefonica, in" + }, + { + "label": "ceo_of", + "score": 0.0107, + "head": "Madrid last", + "tail": "Telefonica, in" + }, + { + "label": "ceo_of", + "score": 0.0106, + "head": "Arteixo, A", + "tail": "Telefonica, in" + }, + { + "label": "works_at", + "score": 0.0103, + "head": "Madrid last", + "tail": "Inditex, has" + }, + { + "label": "works_at", + "score": 0.0101, + "head": "Pablo Isla, the", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "works_at", + "score": 0.0101, + "head": "A Coruna.", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "headquartered_in", + "score": 0.01, + "head": "Madrid last", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "located_in", + "score": 0.0098, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Telefonica, in" + }, + { + "label": "works_at", + "score": 0.0098, + "head": "Inditex has", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "works_at", + "score": 0.0097, + "head": "Inditex, has", + "tail": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.0097, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0096, + "head": "Arteixo, A", + "tail": "Inditex, has" + }, + { + "label": "works_at", + "score": 0.0095, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Pablo Isla, the" + }, + { + "label": "works_at", + "score": 0.0095, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "A Coruna." + }, + { + "label": "located_in", + "score": 0.0094, + "head": "Telefonica. The", + "tail": "Madrid last" + }, + { + "label": "located_in", + "score": 0.0094, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Madrid last" + }, + { + "label": "works_at", + "score": 0.0093, + "head": "Telefonica, in", + "tail": "Inditex, has" + }, + { + "label": "works_at", + "score": 0.0093, + "head": "Inditex, has", + "tail": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.009, + "head": "Madrid last", + "tail": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.0088, + "head": "Madrid last", + "tail": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.0086, + "head": "Madrid last", + "tail": "Pablo Isla, the" + }, + { + "label": "located_in", + "score": 0.0086, + "head": "Madrid last", + "tail": "A Coruna." + }, + { + "label": "located_in", + "score": 0.0086, + "head": "Pablo Isla, the", + "tail": "Inditex has" + }, + { + "label": "located_in", + "score": 0.0086, + "head": "A Coruna.", + "tail": "Inditex has" + }, + { + "label": "appointed_as", + "score": 0.0081, + "head": "Madrid last", + "tail": "Arteixo, A" + }, + { + "label": "works_at", + "score": 0.0081, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.008, + "head": "Inditex has", + "tail": "Pablo Isla, the" + }, + { + "label": "located_in", + "score": 0.008, + "head": "Inditex has", + "tail": "A Coruna." + }, + { + "label": "works_at", + "score": 0.0079, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.0075, + "head": "Pablo Isla, the", + "tail": "Madrid last" + }, + { + "label": "located_in", + "score": 0.0075, + "head": "A Coruna.", + "tail": "Madrid last" + }, + { + "label": "works_at", + "score": 0.0073, + "head": "Pablo Isla, the", + "tail": "Telefonica, in" + }, + { + "label": "works_at", + "score": 0.0073, + "head": "A Coruna.", + "tail": "Telefonica, in" + }, + { + "label": "works_at", + "score": 0.0073, + "head": "Inditex, has", + "tail": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.0072, + "head": "Inditex, has", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "headquartered_in", + "score": 0.0072, + "head": "Pablo Isla, the", + "tail": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.0072, + "head": "A Coruna.", + "tail": "Inditex has" + }, + { + "label": "works_at", + "score": 0.0072, + "head": "Arteixo, A", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "located_in", + "score": 0.0072, + "head": "Madrid last", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "located_in", + "score": 0.0072, + "head": "Telefonica. The", + "tail": "Telefonica, in" + }, + { + "label": "works_at", + "score": 0.0071, + "head": "Telefonica. The", + "tail": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.0071, + "head": "Telefonica. The", + "tail": "Telefonica, in" + }, + { + "label": "works_at", + "score": 0.0071, + "head": "Telefonica. The", + "tail": "Inditex, has" + }, + { + "label": "works_at", + "score": 0.0071, + "head": "Inditex has", + "tail": "Inditex, has" + }, + { + "label": "appointed_as", + "score": 0.007, + "head": "Inditex, has", + "tail": "Arteixo, A" + }, + { + "label": "ceo_of", + "score": 0.007, + "head": "Telefonica, in", + "tail": "Madrid last" + }, + { + "label": "ceo_of", + "score": 0.007, + "head": "Inditex has", + "tail": "Arteixo, A" + }, + { + "label": "appointed_as", + "score": 0.007, + "head": "Telefonica, in", + "tail": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.0069, + "head": "Inditex, has", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "headquartered_in", + "score": 0.0066, + "head": "Telefonica, in", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "works_at", + "score": 0.0066, + "head": "Inditex has", + "tail": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.0066, + "head": "Telefonica, in", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "works_at", + "score": 0.0065, + "head": "Inditex, has", + "tail": "Telefonica. The" + }, + { + "label": "ceo_of", + "score": 0.0064, + "head": "Inditex has", + "tail": "Madrid last" + }, + { + "label": "works_at", + "score": 0.0064, + "head": "Madrid last", + "tail": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.0063, + "head": "Inditex has", + "tail": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.0062, + "head": "Telefonica, in", + "tail": "Telefonica. The" + }, + { + "label": "headquartered_in", + "score": 0.006, + "head": "Inditex has", + "tail": "Pablo Isla, the" + }, + { + "label": "headquartered_in", + "score": 0.006, + "head": "Inditex has", + "tail": "A Coruna." + }, + { + "label": "works_at", + "score": 0.006, + "head": "Telefonica, in", + "tail": "Pablo Isla, the" + }, + { + "label": "works_at", + "score": 0.006, + "head": "Telefonica, in", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.0058, + "head": "Telefonica, in", + "tail": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.0058, + "head": "Telefonica. The", + "tail": "Telefonica, in" + }, + { + "label": "works_at", + "score": 0.0058, + "head": "Telefonica. The", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "works_at", + "score": 0.0057, + "head": "Telefonica. The", + "tail": "Madrid last" + }, + { + "label": "works_at", + "score": 0.0057, + "head": "Telefonica, in", + "tail": "Inditex has" + }, + { + "label": "ceo_of", + "score": 0.0057, + "head": "Arteixo, A", + "tail": "Madrid last" + }, + { + "label": "works_at", + "score": 0.0056, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Madrid last" + }, + { + "label": "works_at", + "score": 0.0055, + "head": "Telefonica, in", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "works_at", + "score": 0.0054, + "head": "Telefonica, in", + "tail": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.0053, + "head": "Telefonica. The", + "tail": "Inditex, has" + }, + { + "label": "works_at", + "score": 0.0051, + "head": "Pablo Isla, the", + "tail": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.0051, + "head": "A Coruna.", + "tail": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.0051, + "head": "Pablo Isla, the", + "tail": "Inditex has" + }, + { + "label": "works_at", + "score": 0.0051, + "head": "A Coruna.", + "tail": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.0051, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.005, + "head": "Pablo Isla, the", + "tail": "Inditex, has" + }, + { + "label": "works_at", + "score": 0.005, + "head": "A Coruna.", + "tail": "Inditex, has" + }, + { + "label": "works_at", + "score": 0.005, + "head": "Madrid last", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "appointed_as", + "score": 0.005, + "head": "Arteixo, A", + "tail": "Inditex has" + }, + { + "label": "located_in", + "score": 0.0049, + "head": "Telefonica, in", + "tail": "Pablo Isla, the" + }, + { + "label": "located_in", + "score": 0.0049, + "head": "Telefonica, in", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.0049, + "head": "Telefonica. The", + "tail": "Inditex, has" + }, + { + "label": "ceo_of", + "score": 0.0048, + "head": "Arteixo, A", + "tail": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.0047, + "head": "Telefonica, in", + "tail": "Pablo Isla, the" + }, + { + "label": "headquartered_in", + "score": 0.0047, + "head": "Telefonica, in", + "tail": "A Coruna." + }, + { + "label": "works_at", + "score": 0.0047, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Arteixo, A" + }, + { + "label": "works_at", + "score": 0.0047, + "head": "Arteixo, A", + "tail": "Madrid last" + }, + { + "label": "located_in", + "score": 0.0045, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.0045, + "head": "Inditex has", + "tail": "Pablo Isla, the" + }, + { + "label": "works_at", + "score": 0.0045, + "head": "Inditex has", + "tail": "A Coruna." + }, + { + "label": "works_at", + "score": 0.0044, + "head": "Telefonica. The", + "tail": "Pablo Isla, the" + }, + { + "label": "works_at", + "score": 0.0044, + "head": "Telefonica. The", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.0043, + "head": "Pablo Isla, the", + "tail": "Telefonica, in" + }, + { + "label": "headquartered_in", + "score": 0.0043, + "head": "A Coruna.", + "tail": "Telefonica, in" + }, + { + "label": "works_at", + "score": 0.004, + "head": "Inditex, has", + "tail": "Pablo Isla, the" + }, + { + "label": "works_at", + "score": 0.004, + "head": "Inditex, has", + "tail": "A Coruna." + }, + { + "label": "works_at", + "score": 0.004, + "head": "Madrid last", + "tail": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.004, + "head": "Pablo Isla, the", + "tail": "Telefonica, in" + }, + { + "label": "located_in", + "score": 0.004, + "head": "A Coruna.", + "tail": "Telefonica, in" + }, + { + "label": "headquartered_in", + "score": 0.0039, + "head": "Telefonica. The", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "located_in", + "score": 0.0038, + "head": "Inditex, has", + "tail": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.0037, + "head": "Madrid last", + "tail": "Inditex has" + }, + { + "label": "works_at", + "score": 0.0037, + "head": "Inditex has", + "tail": "Madrid last" + }, + { + "label": "located_in", + "score": 0.0037, + "head": "Telefonica. The", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "headquartered_in", + "score": 0.0036, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Pablo Isla, the" + }, + { + "label": "headquartered_in", + "score": 0.0036, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "A Coruna." + }, + { + "label": "appointed_as", + "score": 0.0035, + "head": "Inditex has", + "tail": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.0034, + "head": "Inditex, has", + "tail": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.0034, + "head": "Arteixo, A", + "tail": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.0033, + "head": "Telefonica, in", + "tail": "Madrid last" + }, + { + "label": "located_in", + "score": 0.0032, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "Pablo Isla, the" + }, + { + "label": "located_in", + "score": 0.0032, + "head": "Jose Maria Alvarez-Pallete, the", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.0032, + "head": "Pablo Isla, the", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "headquartered_in", + "score": 0.0032, + "head": "A Coruna.", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "works_at", + "score": 0.003, + "head": "Pablo Isla, the", + "tail": "A Coruna." + }, + { + "label": "works_at", + "score": 0.003, + "head": "A Coruna.", + "tail": "Pablo Isla, the" + }, + { + "label": "works_at", + "score": 0.003, + "head": "Arteixo, A", + "tail": "Pablo Isla, the" + }, + { + "label": "works_at", + "score": 0.003, + "head": "Arteixo, A", + "tail": "A Coruna." + }, + { + "label": "located_in", + "score": 0.0028, + "head": "Pablo Isla, the", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "located_in", + "score": 0.0028, + "head": "A Coruna.", + "tail": "Jose Maria Alvarez-Pallete, the" + }, + { + "label": "works_at", + "score": 0.0027, + "head": "Arteixo, A", + "tail": "Telefonica, in" + }, + { + "label": "works_at", + "score": 0.0025, + "head": "Pablo Isla, the", + "tail": "Arteixo, A" + }, + { + "label": "works_at", + "score": 0.0025, + "head": "A Coruna.", + "tail": "Arteixo, A" + }, + { + "label": "works_at", + "score": 0.0024, + "head": "Madrid last", + "tail": "Arteixo, A" + }, + { + "label": "works_at", + "score": 0.0022, + "head": "Arteixo, A", + "tail": "Inditex, has" + }, + { + "label": "works_at", + "score": 0.0021, + "head": "Telefonica. The", + "tail": "Arteixo, A" + }, + { + "label": "works_at", + "score": 0.0018, + "head": "Inditex, has", + "tail": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.0018, + "head": "Pablo Isla, the", + "tail": "Inditex, has" + }, + { + "label": "located_in", + "score": 0.0018, + "head": "A Coruna.", + "tail": "Inditex, has" + }, + { + "label": "located_in", + "score": 0.0018, + "head": "Inditex, has", + "tail": "Pablo Isla, the" + }, + { + "label": "located_in", + "score": 0.0018, + "head": "Inditex, has", + "tail": "A Coruna." + }, + { + "label": "located_in", + "score": 0.0016, + "head": "Telefonica. The", + "tail": "Pablo Isla, the" + }, + { + "label": "located_in", + "score": 0.0016, + "head": "Telefonica. The", + "tail": "A Coruna." + }, + { + "label": "works_at", + "score": 0.0016, + "head": "Telefonica, in", + "tail": "Arteixo, A" + }, + { + "label": "headquartered_in", + "score": 0.0014, + "head": "Pablo Isla, the", + "tail": "Inditex, has" + }, + { + "label": "headquartered_in", + "score": 0.0014, + "head": "A Coruna.", + "tail": "Inditex, has" + }, + { + "label": "headquartered_in", + "score": 0.0014, + "head": "Telefonica. The", + "tail": "Pablo Isla, the" + }, + { + "label": "headquartered_in", + "score": 0.0014, + "head": "Telefonica. The", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.0013, + "head": "Inditex, has", + "tail": "Pablo Isla, the" + }, + { + "label": "headquartered_in", + "score": 0.0013, + "head": "Inditex, has", + "tail": "A Coruna." + }, + { + "label": "located_in", + "score": 0.0013, + "head": "Pablo Isla, the", + "tail": "Telefonica. The" + }, + { + "label": "located_in", + "score": 0.0013, + "head": "A Coruna.", + "tail": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.0012, + "head": "Arteixo, A", + "tail": "Inditex has" + }, + { + "label": "headquartered_in", + "score": 0.0012, + "head": "Pablo Isla, the", + "tail": "Telefonica. The" + }, + { + "label": "headquartered_in", + "score": 0.0012, + "head": "A Coruna.", + "tail": "Telefonica. The" + }, + { + "label": "works_at", + "score": 0.0012, + "head": "Inditex has", + "tail": "Arteixo, A" + }, + { + "label": "located_in", + "score": 0.0006, + "head": "Pablo Isla, the", + "tail": "A Coruna." + }, + { + "label": "located_in", + "score": 0.0006, + "head": "A Coruna.", + "tail": "Pablo Isla, the" + }, + { + "label": "headquartered_in", + "score": 0.0004, + "head": "Pablo Isla, the", + "tail": "A Coruna." + }, + { + "label": "headquartered_in", + "score": 0.0004, + "head": "A Coruna.", + "tail": "Pablo Isla, the" + } + ] + } + }, + "corpus": { + "es_corporate": "Pablo Isla, expresidente de Inditex, ha sido nombrado consejero de Telefonica. La operacion fue anunciada por el presidente Jose Maria Alvarez-Pallete en Madrid el pasado lunes. Inditex factura mas de 30.000 millones anuales y tiene su sede en Arteixo, A Coruna.", + "en_corporate": "Pablo Isla, the former chairman of Inditex, has been appointed as a director of Telefonica. The announcement was made by Jose Maria Alvarez-Pallete, the chairman of Telefonica, in Madrid last Monday. Inditex has its headquarters in Arteixo, A Coruna.", + "en_osint": "On 2024-08-15, attacker IP 185.220.101.45 connected to victim host 10.0.5.22 over TLS. Reverse DNS pointed to tor-exit-relay-3.onionrouter.net. Operator handle @phantomzero claimed responsibility on a forum. The C2 panel was hosted on hxxps://malwareops[.]biz/control behind Cloudflare.", + "es_journalism": "Iberdrola y Endesa firmaron un acuerdo de colaboracion en proyectos eolicos en Galicia. El presidente de Iberdrola, Ignacio Galan, se reunio con la CEO de Endesa, Marina Serrano, en Bilbao. El acuerdo movilizara 2.000 millones de euros en cinco anos." + }, + "entity_labels": { + "generic_en": [ + "person", + "organization", + "location" + ], + "generic_es": [ + "persona", + "organizacion", + "lugar" + ], + "specific_en": [ + "executive", + "company", + "city", + "country" + ], + "osint_en": [ + "ip_address", + "domain", + "url", + "username", + "date", + "person", + "organization" + ] + }, + "relation_labels": { + "snake_short": [ + "works_at", + "located_in", + "appointed_as", + "headquartered_in", + "ceo_of" + ], + "natural_long": [ + "person works at organization", + "organization is located in location", + "person appointed as role at organization", + "organization headquartered in location", + "person is ceo of organization" + ] + } +} \ No newline at end of file diff --git a/run-jupyter-lab.sh b/run-jupyter-lab.sh new file mode 100755 index 0000000..0739b68 --- /dev/null +++ b/run-jupyter-lab.sh @@ -0,0 +1,50 @@ +#!/bin/bash +# Jupyter Lab — modo colaborativo con autodeteccion de puerto +# Generado por write_jupyter_launcher (fn_registry) + +find_free_port() { + for port in 8888 8889 8890 8891 8892 8893 8894 8895 8896 8897 8898 8899; do + if ! ss -tln 2>/dev/null | grep -q ":${port} " && \ + ! lsof -i:"$port" >/dev/null 2>&1; then + echo $port + return + fi + done + echo 8888 +} + +PORT=${1:-$(find_free_port)} +cd "$(dirname "$0")" + +echo $PORT > .jupyter-port + +source .venv/bin/activate 2>/dev/null || true + +# IPython startup: cargar .ipython/ local (FN_REGISTRY_ROOT, helpers, sys.path) +if [ -d "$(pwd)/.ipython" ]; then + export IPYTHONDIR="$(pwd)/.ipython" +fi + +if ! python -c "import jupyter_collaboration" 2>/dev/null; then + echo "ERROR: jupyter-collaboration no esta instalado" + echo "Instala con: uv add jupyter-collaboration" + exit 1 +fi + +echo "════════════════════════════════════════════════" +echo " Jupyter Lab + Colaboracion en puerto $PORT" +echo "════════════════════════════════════════════════" +echo "" +echo " Abre: http://localhost:$PORT" +echo " Ctrl+C para detener" +echo "" + +jupyter lab \ + --port=$PORT \ + --no-browser \ + --ServerApp.token='' \ + --ServerApp.password='' \ + --ServerApp.disable_check_xsrf=True \ + --ServerApp.allow_origin='*' \ + --ServerApp.root_dir="$(pwd)" \ + --collaborative diff --git a/run_benchmark_v2.py b/run_benchmark_v2.py new file mode 100644 index 0000000..59436c3 --- /dev/null +++ b/run_benchmark_v2.py @@ -0,0 +1,162 @@ +"""Benchmark v2 — GLiNER2 (Apache 2.0, NER+RE joint) vs stack actual. + +Genera benchmark_v2.json con resultados sobre 4 corpora: + - es_corporate_short (notebook 02 baseline) + - es_corporate_long (extension a ~30 frases) + - es_osint (castellano, ciberseguridad — NUEVO) + - en_corporate (control idioma) + +Para cada corpus, corre GLiNER2 con el schema joint y registra: + ents, rels, time, calidad manual a posteriori. +""" +from __future__ import annotations + +import json +import os +import sys +import time +import warnings +from pathlib import Path + +warnings.filterwarnings("ignore") +os.environ.setdefault("HF_HUB_DISABLE_PROGRESS_BARS", "1") + +HERE = Path(__file__).resolve().parent +_pf = "/home/lucas/fn_registry/python/functions" +sys.path = [p for p in sys.path if not p.startswith(_pf + "/")] +if _pf not in sys.path: + sys.path.insert(0, _pf) + +from gliner2 import GLiNER2 + +CORPUS = { + "es_corporate_short": ( + "Pablo Isla, expresidente de Inditex, ha sido nombrado consejero de Telefonica. " + "La operacion fue anunciada por el presidente Jose Maria Alvarez-Pallete en Madrid el pasado lunes. " + "Inditex factura mas de 30.000 millones anuales y tiene su sede en Arteixo, A Coruna. " + "En paralelo, Iberdrola y Endesa firmaron un acuerdo de colaboracion en proyectos eolicos en Galicia. " + "El presidente de Iberdrola, Ignacio Galan, se reunio con la CEO de Endesa, Marina Serrano, en Bilbao. " + "El acuerdo movilizara 2.000 millones de euros en cinco anos. " + "El BBVA, presidido por Carlos Torres, mostro interes en participar en la financiacion del proyecto. " + "Su sede central esta en Bilbao." + ), + "es_corporate_long": ( + # 30 frases — generadas para test de chunking y memoria + "Pablo Isla, expresidente de Inditex, ha sido nombrado consejero de Telefonica. " + "La operacion fue anunciada por el presidente Jose Maria Alvarez-Pallete en Madrid el pasado lunes. " + "Inditex factura mas de 30.000 millones anuales y tiene su sede en Arteixo, A Coruna. " + "Amancio Ortega, fundador de Inditex, sigue siendo el principal accionista. " + "Su hija Marta Ortega ha asumido la presidencia ejecutiva del grupo en 2022. " + "Zara, marca emblema de Inditex, opera en mas de 90 paises. " + "Telefonica anuncio una alianza estrategica con Microsoft para servicios cloud. " + "Satya Nadella, CEO de Microsoft, visito la sede de Telefonica en Distrito Telefonica. " + "BBVA, presidido por Carlos Torres, ha completado la integracion de Sabadell tras la fusion. " + "Onur Genc, consejero delegado del banco, lideró el proceso desde Bilbao. " + "Banco Santander, dirigido por Ana Botin, sigue siendo el primer banco espanol por capitalizacion. " + "Hector Grisi es el CEO global de Santander desde enero de 2023. " + "CaixaBank tiene su sede operativa en Valencia desde 2017, presidida por Jose Ignacio Goirigolzarri. " + "Iberdrola, liderada por Ignacio Galan, opera en EEUU a traves de Avangrid. " + "Endesa, filial de la italiana Enel, tiene como CEO a Marina Serrano. " + "El acuerdo entre Iberdrola y Endesa movilizara 2.000 millones de euros en proyectos eolicos en Galicia. " + "Repsol, dirigida por Josu Jon Imaz, ha vendido su filial de Mexico a la australiana Macquarie. " + "Antonio Brufau preside el consejo de administracion de Repsol desde hace mas de una decada. " + "Acciona, presidida por Jose Manuel Entrecanales, ha cerrado un contrato en Australia por 600 millones. " + "Ferrovial, presidida por Rafael del Pino, traslado su sede a Holanda en 2022. " + "ACS, presidida por Florentino Perez, sigue siendo lider mundial en construccion de infraestructuras. " + "Naturgy, antes Gas Natural, esta presidida por Francisco Reynes desde Madrid. " + "Indra ha nombrado a Marc Murtra como nuevo presidente tras la salida de Fernando Abril-Martorell. " + "Telefonica anuncio el cierre de su division de medios y la venta de Telxius a American Tower. " + "El Banco de Espana, gobernado por Pablo Hernandez de Cos, advirtio sobre los riesgos de inflacion. " + "Luis de Guindos, vicepresidente del BCE, fue ministro de Economia en el gobierno de Mariano Rajoy. " + "Calvin Souther Fuller, fundador de SunPower, vendio la empresa al grupo TotalEnergies. " + "Patrick Pouyanne, CEO de TotalEnergies, anuncio inversiones en renovables en Espana. " + "Iberdrola firma con Amazon un PPA de 15 anos para suministrar energia eolica. " + "Andy Jassy, CEO de Amazon, agradecio el acuerdo en una nota publica desde Seattle." + ), + "es_osint": ( + # OSINT en castellano — ciberseguridad + "El 15 de agosto de 2024, el grupo APT-29 (atribuido a Rusia) lanzo una campana de phishing contra empresas energeticas espanolas. " + "El servidor de comando y control 185.220.101.45 conectaba con sistemas internos de Iberdrola via TLS. " + "El malware utilizado, identificado como CozyBear, exploto la vulnerabilidad CVE-2024-21412 en Microsoft Defender. " + "El operador @phantomzero reivindico el ataque en un foro de la dark web alojado en hxxps://malwareops[.]biz/control. " + "El analista Carlos Garcia, del CCN-CERT, publico un informe tecnico con el hash SHA-256 a3f5e8c9b1d2e3f4a5b6c7d8e9f0a1b2 del binario malicioso. " + "Telefonica Tech alerto a sus clientes sobre indicadores de compromiso adicionales en el dominio cloudfront-cdn[.]net." + ), + "en_corporate_short": ( + "Pablo Isla, the former chairman of Inditex, has been appointed as a director of Telefonica. " + "The announcement was made by Jose Maria Alvarez-Pallete, the chairman of Telefonica, in Madrid last Monday. " + "Inditex has its headquarters in Arteixo, A Coruna. " + "BBVA, chaired by Carlos Torres, has its headquarters in Bilbao." + ), +} + +ENTITY_LABELS = { + "general": ["person", "organization", "location"], + "osint_es": ["persona", "organizacion", "ubicacion", "ip_address", "dominio", "url", "username", "vulnerabilidad", "malware", "hash"], + "osint_en": ["person", "organization", "location", "ip_address", "domain", "url", "username", "vulnerability", "malware", "hash"], +} + +RELATION_LABELS = { + "corporate": ["works_at", "located_in", "appointed_as", "ceo_of", "president_of", + "headquartered_in", "subsidiary_of", "parent_company", "founded_by", + "agreement_with", "acquired", "succeeded_by"], + "osint_es": ["targets", "controlled_by", "hosted_at", "exploits", "uses", + "attributed_to", "communicates_with", "indicator_of"], + "osint_en": ["targets", "controlled_by", "hosted_at", "exploits", "uses", + "attributed_to", "communicates_with", "indicator_of"], +} + + +def run_corpus(model: GLiNER2, corpus_key: str, text: str) -> dict: + if "osint" in corpus_key: + ent_lbl = ENTITY_LABELS["osint_es"] if "es_" in corpus_key else ENTITY_LABELS["osint_en"] + rel_lbl = RELATION_LABELS["osint_es"] if "es_" in corpus_key else RELATION_LABELS["osint_en"] + else: + ent_lbl = ENTITY_LABELS["general"] + rel_lbl = RELATION_LABELS["corporate"] + + schema = ( + model.create_schema() + .entities(ent_lbl) + .relations(rel_lbl) + ) + t0 = time.time() + result = model.extract(text, schema=schema) + elapsed = time.time() - t0 + + n_ents = sum(len(v) for v in result.get("entities", {}).values()) + n_rels = sum(len(v) for v in result.get("relation_extraction", {}).values()) + return { + "n_chars": len(text), + "n_words": len(text.split()), + "elapsed_s": round(elapsed, 3), + "n_entities": n_ents, + "n_relations": n_rels, + "entities": result.get("entities", {}), + "relations": result.get("relation_extraction", {}), + "ent_labels": ent_lbl, + "rel_labels": rel_lbl, + } + + +def main(): + print("[load] GLiNER2 large...") + t0 = time.time() + m = GLiNER2.from_pretrained("fastino/gliner2-large-v1") + print(f"[load] {time.time()-t0:.1f}s\n") + + results = {} + for k, text in CORPUS.items(): + print(f"[corpus] {k} ({len(text)} chars, {len(text.split())} words)") + r = run_corpus(m, k, text) + results[k] = r + print(f" → {r['n_entities']} ents, {r['n_relations']} rels, {r['elapsed_s']}s\n") + + out = HERE / "benchmark_v2.json" + out.write_text(json.dumps(results, indent=2, ensure_ascii=False)) + print(f"[saved] {out}") + return results + + +if __name__ == "__main__": + main() diff --git a/run_experiments.py b/run_experiments.py new file mode 100644 index 0000000..c04baf6 --- /dev/null +++ b/run_experiments.py @@ -0,0 +1,213 @@ +"""Experimentos GLiNER + GLiREL — corpus EN/ES, barridos de threshold/labels/top_k. + +Ejecutar con el venv del analysis: ./.venv/bin/python3 run_experiments.py + +Genera: + - results.json (todos los experimentos, listos para tablas/plots) + - notebooks/01_gliner_glirel_tuning.ipynb (rebuild con outputs) +""" +from __future__ import annotations + +import json +import os +import sys +import time +import warnings +from pathlib import Path + +warnings.filterwarnings("ignore") +os.environ.setdefault("HF_HUB_DISABLE_PROGRESS_BARS", "1") +os.environ.setdefault("TRANSFORMERS_NO_ADVISORY_WARNINGS", "1") + +HERE = Path(__file__).resolve().parent +REGISTRY_ROOT = Path(os.environ.get("FN_REGISTRY_ROOT", "/home/lucas/fn_registry")) +sys.path.insert(0, str(REGISTRY_ROOT / "python" / "functions")) + +from datascience.gliner_load_model import gliner_load_model +from datascience.glirel_load_model import glirel_load_model + +CORPUS = { + "es_corporate": ( + "Pablo Isla, expresidente de Inditex, ha sido nombrado consejero de Telefonica. " + "La operacion fue anunciada por el presidente Jose Maria Alvarez-Pallete en Madrid el pasado lunes. " + "Inditex factura mas de 30.000 millones anuales y tiene su sede en Arteixo, A Coruna." + ), + "en_corporate": ( + "Pablo Isla, the former chairman of Inditex, has been appointed as a director of Telefonica. " + "The announcement was made by Jose Maria Alvarez-Pallete, the chairman of Telefonica, in Madrid last Monday. " + "Inditex has its headquarters in Arteixo, A Coruna." + ), + "en_osint": ( + "On 2024-08-15, attacker IP 185.220.101.45 connected to victim host 10.0.5.22 over TLS. " + "Reverse DNS pointed to tor-exit-relay-3.onionrouter.net. Operator handle @phantomzero claimed responsibility on a forum. " + "The C2 panel was hosted on hxxps://malwareops[.]biz/control behind Cloudflare." + ), + "es_journalism": ( + "Iberdrola y Endesa firmaron un acuerdo de colaboracion en proyectos eolicos en Galicia. " + "El presidente de Iberdrola, Ignacio Galan, se reunio con la CEO de Endesa, Marina Serrano, en Bilbao. " + "El acuerdo movilizara 2.000 millones de euros en cinco anos." + ), +} + +ENTITY_LABELS = { + "generic_en": ["person", "organization", "location"], + "generic_es": ["persona", "organizacion", "lugar"], + "specific_en": ["executive", "company", "city", "country"], + "osint_en": ["ip_address", "domain", "url", "username", "date", "person", "organization"], +} + +RELATION_LABELS = { + "snake_short": ["works_at", "located_in", "appointed_as", "headquartered_in", "ceo_of"], + "natural_long": [ + "person works at organization", + "organization is located in location", + "person appointed as role at organization", + "organization headquartered in location", + "person is ceo of organization", + ], +} + + +def _ensure_models(): + """Loads (or returns cached) GLiNER + GLiREL.""" + t0 = time.time() + print(f"[load] GLiNER...") + gliner = gliner_load_model() + print(f"[load] GLiNER ready in {time.time()-t0:.1f}s") + t0 = time.time() + print(f"[load] GLiREL...") + glirel = glirel_load_model() + print(f"[load] GLiREL ready in {time.time()-t0:.1f}s") + return gliner, glirel + + +def gliner_threshold_sweep(gliner) -> dict: + """Para cada (corpus, label_set, threshold) → (n_entidades, ents_list).""" + out = {} + thresholds = [0.1, 0.3, 0.5, 0.7, 0.9] + for corpus_key, text in CORPUS.items(): + out[corpus_key] = {} + # pick label set per corpus + if corpus_key.startswith("es_"): + label_set_keys = ["generic_en", "generic_es"] + elif corpus_key == "en_osint": + label_set_keys = ["generic_en", "osint_en"] + else: + label_set_keys = ["generic_en", "specific_en"] + for ls_key in label_set_keys: + labels = ENTITY_LABELS[ls_key] + out[corpus_key][ls_key] = {} + # one base call at threshold 0.0 to get raw scores + base = gliner.predict_entities(text, labels, threshold=0.0) + # (text, label, score, start, end) + scored = [(e["text"], e["label"], float(e["score"]), e["start"], e["end"]) for e in base] + out[corpus_key][ls_key]["scored_at_t0"] = scored + for t in thresholds: + kept = [e for e in scored if e[2] >= t] + out[corpus_key][ls_key][f"t={t}"] = kept + return out + + +def glirel_score_distribution(gliner, glirel) -> dict: + """Para cada (corpus, relation_label_style) → relations a threshold=0, top_k=5.""" + out = {} + for corpus_key, text in CORPUS.items(): + out[corpus_key] = {} + # entities baseline at threshold 0.5 + labels_for_ents = ENTITY_LABELS["generic_es"] if corpus_key.startswith("es_") else ENTITY_LABELS["generic_en"] + ents = gliner.predict_entities(text, labels_for_ents, threshold=0.5) + if len(ents) < 2: + out[corpus_key]["entities"] = [] + out[corpus_key]["note"] = "too few entities" + continue + out[corpus_key]["entities"] = [(e["text"], e["label"], round(e["score"], 3)) for e in ents] + # tokenize text + tokens = text.split() + # build ner spans (rough token alignment by char position → token) + ner = [] + for e in ents: + pre = text[: e["start"]] + start_tok = len(pre.split()) + end_tok = start_tok + len(e["text"].split()) + if start_tok < end_tok: + ner.append([start_tok, end_tok, e["label"]]) + out[corpus_key]["ner"] = ner + # ── For each relation label style, predict + out[corpus_key]["styles"] = {} + for style_key, rel_labels in RELATION_LABELS.items(): + try: + raw = glirel.predict_relations( + tokens, labels=list(rel_labels), threshold=0.0, ner=ner, top_k=5 + ) + rels = [ + { + "label": r.get("label", ""), + "score": round(float(r.get("score", 0.0)), 4), + "head_text": " ".join(r.get("head_text", [])), + "tail_text": " ".join(r.get("tail_text", [])), + } + for r in raw + ] + # sort by score desc + rels.sort(key=lambda x: x["score"], reverse=True) + out[corpus_key]["styles"][style_key] = rels + except Exception as exc: + out[corpus_key]["styles"][style_key] = {"error": str(exc)} + return out + + +def glirel_topk_sweep(gliner, glirel) -> dict: + """Sobre 1 corpus EN, varia top_k ∈ {1, 3, 5, 10}, threshold=0.0.""" + text = CORPUS["en_corporate"] + ents = gliner.predict_entities(text, ENTITY_LABELS["generic_en"], threshold=0.5) + tokens = text.split() + ner = [] + for e in ents: + pre = text[: e["start"]] + start_tok = len(pre.split()) + end_tok = start_tok + len(e["text"].split()) + if start_tok < end_tok: + ner.append([start_tok, end_tok, e["label"]]) + out = {"entities": [(e["text"], e["label"]) for e in ents], "ner": ner, "by_topk": {}} + for topk in [1, 3, 5, 10]: + raw = glirel.predict_relations( + tokens, labels=RELATION_LABELS["snake_short"], threshold=0.0, ner=ner, top_k=topk + ) + rels = [ + { + "label": r.get("label", ""), + "score": round(float(r.get("score", 0.0)), 4), + "head": " ".join(r.get("head_text", [])), + "tail": " ".join(r.get("tail_text", [])), + } + for r in raw + ] + rels.sort(key=lambda x: x["score"], reverse=True) + out["by_topk"][f"top_k={topk}"] = rels + return out + + +def main(): + gliner, glirel = _ensure_models() + print("\n=== GLINER threshold sweep ===") + gliner_results = gliner_threshold_sweep(gliner) + print("\n=== GLIREL score distribution ===") + glirel_results = glirel_score_distribution(gliner, glirel) + print("\n=== GLIREL top_k sweep ===") + topk_results = glirel_topk_sweep(gliner, glirel) + results = { + "gliner_threshold_sweep": gliner_results, + "glirel_score_distribution": glirel_results, + "glirel_topk_sweep": topk_results, + "corpus": CORPUS, + "entity_labels": ENTITY_LABELS, + "relation_labels": RELATION_LABELS, + } + out_path = HERE / "results.json" + out_path.write_text(json.dumps(results, indent=2, ensure_ascii=False)) + print(f"\n[done] {out_path}") + return results + + +if __name__ == "__main__": + main() diff --git a/run_improvements.py b/run_improvements.py new file mode 100644 index 0000000..f879517 --- /dev/null +++ b/run_improvements.py @@ -0,0 +1,327 @@ +"""Bateria de experimentos comparando configuraciones de GLiNER2 sobre el PDF. + +Vuelca a improvements.json para que build_notebook_improvements.py construya +el notebook con outputs estaticos (sin volver a cargar el modelo). +""" +from __future__ import annotations + +import gc +import json +import os +import re +import sys +import time +import warnings +from collections import Counter, defaultdict +from pathlib import Path + +warnings.filterwarnings("ignore") +os.environ.setdefault("HF_HUB_DISABLE_PROGRESS_BARS", "1") + +HERE = Path(__file__).resolve().parent +_pf = "/home/lucas/fn_registry/python/functions" +sys.path = [p for p in sys.path if not p.startswith(_pf + "/")] +if _pf not in sys.path: + sys.path.insert(0, _pf) + +from gliner2 import GLiNER2 +from core.extract_pdf_text import extract_pdf_text + + +VAULT = Path("/home/lucas/vaults/osint_nlp_models") +PDF_PATH = VAULT / "test_documents" / "politica_proteccion_datos.pdf" + + +def clean_pdf_text(text: str) -> str: + text = re.sub(r"\b\d{1,2}/\d{1,2}\b", " ", text) + text = text.replace("\t", " ") + text = re.sub(r"-\s*\n\s*", "", text) + text = re.sub(r"(? 0: + prev_sents = chunks[-1]["sentences"][-overlap_sentences:] + overlap_len = sum(len(s) + 1 for s in prev_sents) + next_sentence_len = len(sentences[i]) + 1 + if overlap_len + next_sentence_len <= max_chars: + current_sents = list(prev_sents) + current_len = overlap_len + # Avance forzado: meter al menos una frase aunque exceda max_chars. + if i < len(sentences): + current_sents.append(sentences[i]) + current_len += len(sentences[i]) + 1 + i += 1 + # Anadir mas frases hasta llenar + while i < len(sentences) and current_len + len(sentences[i]) + 1 <= max_chars: + current_sents.append(sentences[i]) + current_len += len(sentences[i]) + 1 + i += 1 + chunks.append({"text": " ".join(current_sents), "sentences": current_sents}) + return chunks + + +def aggregate(extract_results): + all_ents: dict = {} + all_rels: Counter = Counter() + for r in extract_results: + for typ, names in r.get("entities", {}).items(): + for n in names: + n_clean = n.strip() + if not n_clean: + continue + key = (typ, n_clean.lower()) + if key not in all_ents: + all_ents[key] = {"type": typ, "name": n_clean, "count": 0} + all_ents[key]["count"] += 1 + for rt, pairs in r.get("relation_extraction", {}).items(): + for h, t in pairs: + all_rels[(h.strip(), rt, t.strip())] += 1 + return all_ents, all_rels + + +def graph_stats(ents_dict, rels_counter): + nodes = set() + for v in ents_dict.values(): + nodes.add(v["name"]) + edges = set() + for (h, rt, t), c in rels_counter.items(): + nodes.add(h); nodes.add(t) + edges.add((h, t, rt)) + has_edge = set() + for h, t, rt in edges: + has_edge.add(h); has_edge.add(t) + isolates = nodes - has_edge + return { + "n_ents": len(ents_dict), + "n_rels": len(rels_counter), + "n_nodes": len(nodes), + "n_edges": len(edges), + "n_isolates": len(isolates), + "connected": len(nodes) - len(isolates), + "connect_pct": round((len(nodes) - len(isolates)) / max(1, len(nodes)) * 100, 1), + } + + +def normalize_name(s: str) -> str: + s = s.strip() + s = re.sub(r"[\.,;:\"'`()\[\]]", "", s) + s = re.sub(r"\s+", " ", s) + return s.strip().lower() + + +def merge_aliases(ents_dict, rels_counter): + norm_groups: dict = defaultdict(list) + for v in ents_dict.values(): + norm_groups[normalize_name(v["name"])].append(v) + canonical: dict = {} + canonical_data: dict = {} + for nrm, group in norm_groups.items(): + winner = max(group, key=lambda v: v["count"]) + canonical[nrm] = winner["name"] + canonical_data[winner["name"]] = { + "type": winner["type"], + "name": winner["name"], + "count": sum(v["count"] for v in group), + "aliases": [v["name"] for v in group if v["name"] != winner["name"]], + } + canon_names = sorted(canonical_data.keys(), key=len, reverse=True) + absorbed: dict = {} + for long_n in canon_names: + long_norm = normalize_name(long_n) + long_type = canonical_data[long_n]["type"] + for short_n in canon_names: + if short_n == long_n or short_n in absorbed: + continue + short_norm = normalize_name(short_n) + if len(short_norm) < 4: + continue + short_type = canonical_data[short_n]["type"] + if short_type != long_type: + continue + if re.search(r"\b" + re.escape(short_norm) + r"\b", long_norm): + absorbed[short_n] = long_n + canonical_data[long_n]["count"] += canonical_data[short_n]["count"] + canonical_data[long_n]["aliases"].append(short_n) + canonical_data[long_n]["aliases"].extend(canonical_data[short_n].get("aliases", [])) + for short_n in list(absorbed): + canonical_data.pop(short_n, None) + + def resolve(name): + nrm = normalize_name(name) + c = canonical.get(nrm, name) + return absorbed.get(c, c) + + new_rels: Counter = Counter() + for (h, rt, t), c in rels_counter.items(): + h_canon = resolve(h) + t_canon = resolve(t) + if h_canon == t_canon: + continue + new_rels[(h_canon, rt, t_canon)] += c + return canonical_data, new_rels, absorbed + + +ENTITY_LABELS = ["person", "organization", "location", "email", "right", "data_category", "authority", "law"] + +RELATION_LABELS_FLAT = [ + "located_in", "governed_by", "subject_to", "protected_by", + "contact_for", "rights_against", "subsidiary_of", "controlled_by", +] +RELATION_LABELS_DESC = { + "located_in": "organization or person is located in a place or address", + "governed_by": "entity is governed or supervised by an authority or law", + "subject_to": "data category or process is subject to a law or regulation", + "protected_by": "right or data is protected by a law or authority", + "contact_for": "email or address is the contact channel for an authority or right", + "rights_against": "person has rights to exercise against an organization", + "subsidiary_of": "organization is a subsidiary of a parent organization", + "controlled_by": "organization or data is controlled by another organization", +} + + +def main(): + out: dict = {} + + # --- prepare text + chunks (CPU only) + print("[prep] extract + clean + chunk...") + raw = extract_pdf_text(str(PDF_PATH)) + clean = clean_pdf_text(raw) + chunks = chunk_with_overlap(clean, max_chars=1500, overlap_sentences=2) + chunks_no_overlap = chunk_with_overlap(clean, max_chars=1500, overlap_sentences=0) + out["meta"] = { + "raw_chars": len(raw), + "clean_chars": len(clean), + "n_chunks_overlap": len(chunks), + "n_chunks_no_overlap": len(chunks_no_overlap), + "first_clean_600": clean[:600], + } + print(f" raw {len(raw):,} → clean {len(clean):,} → {len(chunks)} chunks (overlap=2)") + + print("[load] GLiNER2...") + t0 = time.time() + model = GLiNER2.from_pretrained("fastino/gliner2-large-v1") + print(f" load: {time.time()-t0:.1f}s") + + schema_flat = model.create_schema().entities(ENTITY_LABELS).relations(RELATION_LABELS_FLAT) + schema_desc = model.create_schema().entities(ENTITY_LABELS).relations(RELATION_LABELS_DESC) + + configs: list = [] + + # A: t=0.5 flat loop + print("[A] t=0.5 flat loop...") + t0 = time.time() + res_a = [model.extract(c["text"], schema=schema_flat, threshold=0.5) for c in chunks] + elapsed_a = time.time() - t0 + ents_a, rels_a = aggregate(res_a) + configs.append({"name": "A: t=0.5 flat loop", "elapsed": round(elapsed_a, 1), + "stats": graph_stats(ents_a, rels_a)}) + del res_a; gc.collect() + print(f" {elapsed_a:.1f}s stats={configs[-1]['stats']}") + + # B: t=0.3 flat loop + print("[B] t=0.3 flat loop...") + t0 = time.time() + res_b = [model.extract(c["text"], schema=schema_flat, threshold=0.3) for c in chunks] + elapsed_b = time.time() - t0 + ents_b, rels_b = aggregate(res_b) + configs.append({"name": "B: t=0.3 flat loop", "elapsed": round(elapsed_b, 1), + "stats": graph_stats(ents_b, rels_b)}) + del res_b; gc.collect() + print(f" {elapsed_b:.1f}s stats={configs[-1]['stats']}") + + # C: t=0.2 flat loop + print("[C] t=0.2 flat loop...") + t0 = time.time() + res_c = [model.extract(c["text"], schema=schema_flat, threshold=0.2) for c in chunks] + elapsed_c = time.time() - t0 + ents_c, rels_c = aggregate(res_c) + configs.append({"name": "C: t=0.2 flat loop", "elapsed": round(elapsed_c, 1), + "stats": graph_stats(ents_c, rels_c)}) + del res_c; gc.collect() + print(f" {elapsed_c:.1f}s stats={configs[-1]['stats']}") + + # D: t=0.3 desc loop + print("[D] t=0.3 desc loop...") + t0 = time.time() + res_d = [model.extract(c["text"], schema=schema_desc, threshold=0.3) for c in chunks] + elapsed_d = time.time() - t0 + ents_d, rels_d = aggregate(res_d) + configs.append({"name": "D: t=0.3 desc loop", "elapsed": round(elapsed_d, 1), + "stats": graph_stats(ents_d, rels_d)}) + del res_d; gc.collect() + print(f" {elapsed_d:.1f}s stats={configs[-1]['stats']}") + + # E: t=0.3 desc batch_extract + print("[E] t=0.3 desc batch_extract...") + t0 = time.time() + texts = [c["text"] for c in chunks] + res_e = model.batch_extract(texts, schemas=schema_desc, batch_size=8, threshold=0.3) + elapsed_e = time.time() - t0 + ents_e, rels_e = aggregate(res_e) + configs.append({"name": "E: t=0.3 desc batch", "elapsed": round(elapsed_e, 1), + "stats": graph_stats(ents_e, rels_e)}) + print(f" {elapsed_e:.1f}s stats={configs[-1]['stats']}") + out["configs"] = configs + + # --- coreference sobre la mejor config (E) --- + print("[coref] applying alias merge to config E...") + t0 = time.time() + ents_merged, rels_merged, absorbed = merge_aliases(ents_e, rels_e) + ents_merged_dict = {(v["type"], v["name"].lower()): v for v in ents_merged.values()} + stats_post = graph_stats(ents_merged_dict, rels_merged) + elapsed_coref = time.time() - t0 + out["coref"] = { + "elapsed": round(elapsed_coref, 2), + "pre_stats": graph_stats(ents_e, rels_e), + "post_stats": stats_post, + "n_absorbed": len(absorbed), + "absorbed_sample": list(absorbed.items())[:8], + } + print(f" pre: {out['coref']['pre_stats']}") + print(f" post: {out['coref']['post_stats']}") + print(f" absorbed: {len(absorbed)} e.g. {list(absorbed.items())[:3]}") + + # --- top entities post-coref --- + top_rows = [] + for v in sorted(ents_merged.values(), key=lambda x: -x["count"])[:25]: + top_rows.append({ + "type": v["type"], + "canonical": v["name"], + "mentions": v["count"], + "n_aliases": len(v.get("aliases", [])), + "aliases_sample": v.get("aliases", [])[:3], + }) + out["top_entities_post_coref"] = top_rows + + # --- relations top --- + top_rels = [] + for (h, rt, t), c in sorted(rels_merged.items(), key=lambda x: -x[1])[:25]: + top_rels.append({"from": h, "kind": rt, "to": t, "count": c}) + out["top_relations_post_coref"] = top_rels + + # --- save ents_merged + rels_merged for graph rendering --- + out["ents_merged"] = [{"name": v["name"], "type": v["type"], "count": v["count"]} + for v in ents_merged.values()] + out["rels_merged"] = [{"from": h, "kind": rt, "to": t, "count": c} + for (h, rt, t), c in rels_merged.items()] + + out_path = HERE / "improvements.json" + out_path.write_text(json.dumps(out, indent=2, ensure_ascii=False)) + print(f"\n[saved] {out_path} ({out_path.stat().st_size:,} bytes)") + + +if __name__ == "__main__": + main() diff --git a/run_mrebel_test.py b/run_mrebel_test.py new file mode 100644 index 0000000..636310d --- /dev/null +++ b/run_mrebel_test.py @@ -0,0 +1,154 @@ +"""Quick test of Babelscape/mREBEL on Spanish business text. + +Compara directamente con GLiREL sobre el mismo texto. Si mREBEL produce +tripletas semanticamente correctas en castellano, lo proponemos como +sustituto/complemento de GLiREL en el pipeline `extract_graph_hybrid`. + +Licencia mREBEL: CC BY-NC-SA 4.0 (no comercial). OK para uso personal/ +investigacion; revisar si pasa a produccion comercial. +""" +from __future__ import annotations + +import sys +import time +import warnings +from pathlib import Path + +warnings.filterwarnings("ignore") + +# Same sys.path cleanup as the notebook (avoid bigquery/datasets.py shadow) +import os +_pf = "/home/lucas/fn_registry/python/functions" +sys.path = [p for p in sys.path if not p.startswith(_pf + "/")] +if _pf not in sys.path: + sys.path.insert(0, _pf) + +from transformers import AutoModelForSeq2SeqLM, AutoTokenizer + +TEXT_ES = ( + "Pablo Isla, expresidente de Inditex, ha sido nombrado consejero de Telefonica. " + "La operacion fue anunciada por el presidente Jose Maria Alvarez-Pallete en Madrid el pasado lunes. " + "Inditex factura mas de 30.000 millones anuales y tiene su sede en Arteixo, A Coruna. " + "En paralelo, Iberdrola y Endesa firmaron un acuerdo de colaboracion en proyectos eolicos en Galicia. " + "El presidente de Iberdrola, Ignacio Galan, se reunio con la CEO de Endesa, Marina Serrano, en Bilbao. " + "El acuerdo movilizara 2.000 millones de euros en cinco anos. " + "El BBVA, presidido por Carlos Torres, mostro interes en participar en la financiacion del proyecto. " + "Su sede central esta en Bilbao." +) + + +def extract_triplets_typed(text: str) -> list[dict]: + """Parse mREBEL output (decoded with skip_special_tokens=False) into triplets. + Format: head head_type rel_type tail_type ... + Adapted from the README example. + """ + triplets = [] + relation = "" + text = text.strip() + current = "x" + subject, relation, object_, object_type, subject_type = "", "", "", "", "" + + for token in ( + text.replace("", "") + .replace("", "") + .replace("", "") + .replace("tp_XX", "") + .replace("__en__", "") + .split() + ): + if token == "" or token == "": + current = "t" + if relation != "": + triplets.append( + { + "head": subject.strip(), + "head_type": subject_type, + "type": relation.strip(), + "tail": object_.strip(), + "tail_type": object_type, + } + ) + relation = "" + subject = "" + elif token.startswith("<") and token.endswith(">"): + if current == "t" or current == "o": + current = "s" + if relation != "": + triplets.append( + { + "head": subject.strip(), + "head_type": subject_type, + "type": relation.strip(), + "tail": object_.strip(), + "tail_type": object_type, + } + ) + object_ = "" + subject_type = token[1:-1] + else: + current = "o" + object_type = token[1:-1] + relation = "" + else: + if current == "t": + subject += " " + token + elif current == "s": + object_ += " " + token + elif current == "o": + relation += " " + token + if subject != "" and relation != "" and object_ != "" and object_type != "" and subject_type != "": + triplets.append( + { + "head": subject.strip(), + "head_type": subject_type, + "type": relation.strip(), + "tail": object_.strip(), + "tail_type": object_type, + } + ) + return triplets + + +def main(): + print("[load] mREBEL...", flush=True) + t0 = time.time() + tokenizer = AutoTokenizer.from_pretrained( + "Babelscape/mrebel-large", src_lang="es_XX", tgt_lang="tp_XX" + ) + model = AutoModelForSeq2SeqLM.from_pretrained("Babelscape/mrebel-large") + print(f"[load] mREBEL ready in {time.time()-t0:.1f}s") + + print(f"\n[input ES] {len(TEXT_ES)} chars") + inputs = tokenizer(TEXT_ES, max_length=512, padding=True, truncation=True, return_tensors="pt") + print("[generate]") + t0 = time.time() + out = model.generate( + inputs["input_ids"].to(model.device), + attention_mask=inputs["attention_mask"].to(model.device), + decoder_start_token_id=tokenizer.convert_tokens_to_ids("tp_XX"), + max_length=512, + num_beams=4, + length_penalty=0.0, + ) + print(f"[generate] {time.time()-t0:.1f}s") + decoded = tokenizer.batch_decode(out, skip_special_tokens=False) + print("\n=== RAW DECODED ===") + print(decoded[0][:2000]) + print("\n=== TRIPLETS ===") + triplets = extract_triplets_typed(decoded[0]) + print(f"n={len(triplets)}\n") + for t in triplets: + print(f" ({t['head']:32s} : {t['head_type']:15s}) --[{t['type']:25s}]--> ({t['tail']:32s} : {t['tail_type']:15s})") + # Save for the notebook + import json + out_path = Path(__file__).resolve().parent / "mrebel_results.json" + out_path.write_text(json.dumps({ + "text": TEXT_ES, + "raw_decoded": decoded[0], + "triplets": triplets, + }, indent=2, ensure_ascii=False)) + print(f"\n[saved] {out_path}") + + +if __name__ == "__main__": + main() diff --git a/run_nuextract_full.py b/run_nuextract_full.py new file mode 100644 index 0000000..b71c56c --- /dev/null +++ b/run_nuextract_full.py @@ -0,0 +1,535 @@ +"""NuExtract 2.0-2B GPU — version 'production' con todas las mejoras: + - repetition_penalty=1.1 (evita bucles) + - chunking forzado (max 800 chars / ~250 tokens) en TODO texto + - 97 chunks completos del PDF (no muestra) + - 25 frases ES troceadas adecuadamente + - agregacion deduplicada con conteo + - coreferencia simple (normalize + substring) + +Vuelca a nuextract_full.json — listo para notebook 08. +""" +from __future__ import annotations + +import gc +import json +import os +import re +import sys +import time +import warnings +from collections import Counter, defaultdict +from pathlib import Path + +warnings.filterwarnings("ignore") +os.environ.setdefault("HF_HUB_DISABLE_PROGRESS_BARS", "1") + +HERE = Path(__file__).resolve().parent +_pf = "/home/lucas/fn_registry/python/functions" +sys.path = [p for p in sys.path if not p.startswith(_pf + "/")] +if _pf not in sys.path: + sys.path.insert(0, _pf) + +from core.extract_pdf_text import extract_pdf_text + + +VAULT = Path("/home/lucas/vaults/osint_nlp_models") +PDF_PATH = VAULT / "test_documents" / "politica_proteccion_datos.pdf" + + +def clean_pdf_text(text: str) -> str: + text = re.sub(r"\b\d{1,2}/\d{1,2}\b", " ", text) + text = text.replace("\t", " ") + text = re.sub(r"-\s*\n\s*", "", text) + text = re.sub(r"(? 0: + prev_sents = chunks[-1]["sentences"][-overlap_sentences:] + overlap_len = sum(len(s) + 1 for s in prev_sents) + next_sentence_len = len(sentences[i]) + 1 + if overlap_len + next_sentence_len <= max_chars: + current_sents = list(prev_sents) + current_len = overlap_len + if i < len(sentences): + current_sents.append(sentences[i]) + current_len += len(sentences[i]) + 1 + i += 1 + while i < len(sentences) and current_len + len(sentences[i]) + 1 <= max_chars: + current_sents.append(sentences[i]) + current_len += len(sentences[i]) + 1 + i += 1 + chunks.append({"text": " ".join(current_sents), "sentences": current_sents}) + return chunks + + +LONG_TEXT_ES = ( + "BBVA, presidido por Carlos Torres, completo en 2024 la integracion operativa de Banco Sabadell tras la fusion. " + "Onur Genc, consejero delegado del banco desde 2018, lidero el proceso desde la sede central en Bilbao. " + "El banco mantiene oficinas en Plaza San Nicolas 4 y opera en mas de 25 paises. " + "Banco Santander, dirigido por Ana Botin, sigue siendo el primer banco espanol por capitalizacion bursatil. " + "Hector Grisi asumio el cargo de CEO global de Santander en enero de 2023, reemplazando a Jose Antonio Alvarez. " + "CaixaBank, presidida por Jose Ignacio Goirigolzarri y con sede en Valencia desde 2017, completo la fusion con Bankia. " + "Gonzalo Gortazar es el consejero delegado de CaixaBank y reporta al consejo formado en parte por La Caixa. " + "El Banco de Espana, gobernado por Pablo Hernandez de Cos hasta 2024 y por Margarita Delgado en 2025, supervisa el sector. " + "Luis de Guindos, vicepresidente del Banco Central Europeo, fue ministro de Economia en el gobierno de Mariano Rajoy. " + "La Comision Nacional del Mercado de Valores, presidida por Rodrigo Buenaventura, regula los mercados financieros. " + "BBVA anuncio en mayo de 2024 una OPA hostil sobre Banco Sabadell que el consejo del banco rechazo inicialmente. " + "Cesar Gonzalez-Bueno, CEO de Sabadell, defendio la independencia del banco junto con su presidente Josep Oliu. " + "Repsol, presidida por Antonio Brufau y con CEO Josu Jon Imaz, vendio su filial mexicana a Macquarie. " + "Iberdrola, liderada por Ignacio Galan, opera Avangrid en EEUU y firmo un acuerdo PPA con Amazon. " + "Andy Jassy, CEO de Amazon desde Seattle, agradecio el contrato a Iberdrola en una nota publica. " + "Endesa, filial de la italiana Enel, tiene como CEO a Marina Serrano y opera en Espana, Portugal y Marruecos. " + "Ferrovial, presidida por Rafael del Pino, traslado su sede social a Holanda en 2022 generando polemica politica. " + "ACS, presidida por Florentino Perez, sigue siendo lider mundial en concesiones de infraestructura. " + "Inditex, fundada por Amancio Ortega y presidida por Marta Ortega desde 2022, tiene su sede en Arteixo, A Coruna. " + "Pablo Isla, expresidente de Inditex y actual consejero de Telefonica, se incorporo al consejo en 2024. " + "Telefonica, presidida por Jose Maria Alvarez-Pallete, sufrio la entrada del estado en su capital con SEPI. " + "Saudi Telecom Company adquirio un 9.9% de Telefonica en 2023, lo que motivo la respuesta del gobierno espanol. " + "Cristina Aldamiz-Echevarria fue nombrada directora de Recursos Humanos del Grupo Mapfre, dirigido por Antonio Huertas. " + "Naturgy, presidida por Francisco Reynes, recibio una OPA parcial del fondo emirati IFM en 2021 que se cancelo. " + "Indra, con Marc Murtra como presidente, se ha posicionado como contratista clave de Defensa para el ministerio de Margarita Robles." +) + + +SCHEMA_RICH_CORPORATE = """{ + "organizations": [ + { + "name": "string", + "ceo": "string", + "chairman_president": "string", + "headquartered_in": "string", + "subsidiaries": ["string"], + "parent_company": "string" + } + ], + "people": [ + { + "name": "string", + "role": "string", + "organization": "string" + } + ], + "agreements": [ + { + "between": ["string"], + "topic": "string", + "amount": "string" + } + ] +}""" + +SCHEMA_RICH_GDPR = """{ + "data_controller": { + "name": "string", + "address": "string", + "registration": "string" + }, + "dpo_contact": { + "email": "string", + "address": "string" + }, + "data_categories": ["string"], + "rights_listed": ["string"], + "authorities_mentioned": [ + { + "name": "string", + "url_or_contact": "string" + } + ], + "laws_mentioned": ["string"] +}""" + + +def parse_json_safe(text: str): + """Parser robusto: busca el PRIMER `{` y trunca progresivamente.""" + if not text: return None + s = text.find("{") + if s < 0: return None + for end in range(len(text), s, -1): + try: + return json.loads(text[s:end]) + except Exception: + continue + return None + + +def run_extract(model, tokenizer, device, document, template, max_new_tokens=1024): + messages = [{"role": "user", "content": document}] + text = tokenizer.apply_chat_template( + messages, template=template, tokenize=False, add_generation_prompt=True, + ) + inputs = tokenizer([text], padding=True, return_tensors="pt").to(device) + t0 = time.time() + generated = model.generate( + **inputs, + do_sample=False, + num_beams=1, + max_new_tokens=max_new_tokens, + repetition_penalty=1.15, # ⭐ EVITA BUCLES + pad_token_id=tokenizer.eos_token_id, + ) + elapsed = time.time() - t0 + n_input = inputs["input_ids"].shape[1] + n_output = generated.shape[1] - n_input + out_text = tokenizer.decode(generated[0][n_input:], skip_special_tokens=True) + parsed = parse_json_safe(out_text) + return { + "elapsed_s": round(elapsed, 2), + "n_input_tokens": int(n_input), + "n_output_tokens": int(n_output), + "raw_text": out_text, + "parsed": parsed, + } + + +# ── agregadores y coreferencia ── + +def aggregate_corporate(results: list[dict]) -> dict: + """Acumula organizations / people / agreements de N chunks.""" + orgs = {} # name_lower -> dict (con counts y mejores valores) + people = {} # name_lower -> dict + agreements = [] + + for r in results: + parsed = r.get("parsed") or {} + for o in parsed.get("organizations", []) or []: + if not isinstance(o, dict): continue + name = (o.get("name") or "").strip() + if not name: continue + key = name.lower() + if key not in orgs: + orgs[key] = {"name": name, "count": 0, "ceo": [], "chairman_president": [], + "headquartered_in": [], "subsidiaries": set(), "parent_company": []} + orgs[key]["count"] += 1 + for f in ("ceo", "chairman_president", "headquartered_in", "parent_company"): + v = o.get(f) + if v and isinstance(v, str) and v.strip(): + orgs[key][f].append(v.strip()) + for sub in (o.get("subsidiaries") or []): + if isinstance(sub, str) and sub.strip(): + orgs[key]["subsidiaries"].add(sub.strip()) + + for p in parsed.get("people", []) or []: + if not isinstance(p, dict): continue + name = (p.get("name") or "").strip() + if not name: continue + key = name.lower() + if key not in people: + people[key] = {"name": name, "count": 0, "roles": [], "organizations": []} + people[key]["count"] += 1 + r_ = p.get("role") + if r_ and isinstance(r_, str) and r_.strip(): + people[key]["roles"].append(r_.strip()) + o_ = p.get("organization") + if o_ and isinstance(o_, str) and o_.strip(): + people[key]["organizations"].append(o_.strip()) + + for ag in parsed.get("agreements", []) or []: + if not isinstance(ag, dict): continue + parties = [p.strip() for p in (ag.get("between") or []) if isinstance(p, str) and p.strip()] + if len(parties) >= 2: + agreements.append({"between": parties, "topic": ag.get("topic"), "amount": ag.get("amount")}) + + # Convertir sets a listas + for o in orgs.values(): + o["subsidiaries"] = sorted(o["subsidiaries"]) + return {"organizations": list(orgs.values()), "people": list(people.values()), "agreements": agreements} + + +def aggregate_gdpr(results: list[dict]) -> dict: + out = { + "data_controllers": [], # multiple by chunk + "dpo_contacts": [], + "data_categories": Counter(), + "rights_listed": Counter(), + "authorities": {}, # name_lower -> {name, contact_options[], count} + "laws": Counter(), + } + for r in results: + parsed = r.get("parsed") or {} + dc = parsed.get("data_controller") or {} + if isinstance(dc, dict) and dc.get("name"): + out["data_controllers"].append(dc) + dpo = parsed.get("dpo_contact") or {} + if isinstance(dpo, dict) and (dpo.get("email") or dpo.get("address")): + out["dpo_contacts"].append(dpo) + for c in parsed.get("data_categories", []) or []: + if isinstance(c, str) and c.strip(): + out["data_categories"][c.strip()] += 1 + for rt in parsed.get("rights_listed", []) or []: + if isinstance(rt, str) and rt.strip(): + out["rights_listed"][rt.strip()] += 1 + for a in parsed.get("authorities_mentioned", []) or []: + if not isinstance(a, dict): continue + name = (a.get("name") or "").strip() + if not name: continue + key = name.lower() + if key not in out["authorities"]: + out["authorities"][key] = {"name": name, "contact_options": [], "count": 0} + out["authorities"][key]["count"] += 1 + c = a.get("url_or_contact") + if c and isinstance(c, str) and c.strip(): + out["authorities"][key]["contact_options"].append(c.strip()) + for l in parsed.get("laws_mentioned", []) or []: + if isinstance(l, str) and l.strip(): + out["laws"][l.strip()] += 1 + out["data_categories"] = dict(out["data_categories"]) + out["rights_listed"] = dict(out["rights_listed"]) + out["laws"] = dict(out["laws"]) + out["authorities"] = list(out["authorities"].values()) + return out + + +def normalize_name(s: str) -> str: + s = s.strip() + s = re.sub(r"[\.,;:\"'`()\[\]]", "", s) + s = re.sub(r"\s+", " ", s) + return s.strip().lower() + + +def merge_aliases(entity_names: list[str]) -> dict: + """Devuelve un dict {nombre_original → nombre_canonico}.""" + norm_groups = defaultdict(list) + for n in entity_names: + norm_groups[normalize_name(n)].append(n) + canonical: dict = {} + canonical_data: dict = {} + for nrm, group in norm_groups.items(): + winner = max(group, key=lambda x: (len(x), x)) + for n in group: + canonical[n] = winner + canonical_data[winner] = group + canon_names = sorted(canonical_data.keys(), key=len, reverse=True) + absorbed = {} + for long_n in canon_names: + long_norm = normalize_name(long_n) + for short_n in canon_names: + if short_n == long_n or short_n in absorbed: continue + short_norm = normalize_name(short_n) + if len(short_norm) < 4: continue + if re.search(r"\b" + re.escape(short_norm) + r"\b", long_norm): + absorbed[short_n] = long_n + final = {} + for orig, canon in canonical.items(): + final[orig] = absorbed.get(canon, canon) + return final + + +def build_corporate_graph(agg: dict, alias_map: dict | None = None) -> dict: + """Construye nodos y aristas del grafo corporate.""" + if alias_map is None: alias_map = {} + def resolve(n): return alias_map.get(n, n) + + nodes = {} # name -> type + edges = set() # (h, kind, t) + + for org in agg["organizations"]: + name = resolve(org["name"]) + nodes[name] = "organization" + for ceo in org["ceo"]: + ceo_r = resolve(ceo) + nodes.setdefault(ceo_r, "person") + edges.add((ceo_r, "ceo_of", name)) + for pres in org["chairman_president"]: + pres_r = resolve(pres) + nodes.setdefault(pres_r, "person") + edges.add((pres_r, "president_of", name)) + for hq in org["headquartered_in"]: + hq_r = resolve(hq) + nodes.setdefault(hq_r, "location") + edges.add((name, "headquartered_in", hq_r)) + for parent in org["parent_company"]: + parent_r = resolve(parent) + nodes.setdefault(parent_r, "organization") + edges.add((name, "subsidiary_of", parent_r)) + for sub in org["subsidiaries"]: + sub_r = resolve(sub) + nodes.setdefault(sub_r, "organization") + edges.add((sub_r, "subsidiary_of", name)) + + for p in agg["people"]: + name = resolve(p["name"]) + nodes.setdefault(name, "person") + for org in p["organizations"]: + org_r = resolve(org) + nodes.setdefault(org_r, "organization") + edges.add((name, "works_at", org_r)) + + for ag in agg["agreements"]: + parties = [resolve(p) for p in ag["between"]] + for p in parties: + nodes.setdefault(p, "organization") + for i, a in enumerate(parties): + for b in parties[i+1:]: + edges.add((a, "agreement_with", b)) + + return {"nodes": nodes, "edges": list(edges)} + + +def build_gdpr_graph(agg: dict, alias_map: dict | None = None) -> dict: + if alias_map is None: alias_map = {} + def resolve(n): return alias_map.get(n, n) + nodes = {} + edges = set() + + # data_controller — pick the first non-empty + for dc in agg["data_controllers"]: + if dc.get("name"): + name = resolve(dc["name"].strip()) + nodes[name] = "data_controller" + if dc.get("address"): + addr = resolve(dc["address"].strip()) + nodes.setdefault(addr, "location") + edges.add((name, "located_in", addr)) + break # solo el primero + + for dpo in agg["dpo_contacts"]: + if dpo.get("email"): + email = dpo["email"].strip() + nodes.setdefault(email, "email") + if dpo.get("address"): + addr = dpo["address"].strip() + nodes.setdefault(addr, "location") + + for c in agg["data_categories"]: + nodes.setdefault(c, "data_category") + for r in agg["rights_listed"]: + nodes.setdefault(r, "right") + for a in agg["authorities"]: + name = resolve(a["name"].strip()) + nodes.setdefault(name, "authority") + for c in a["contact_options"][:1]: # 1 contact por authority + nodes.setdefault(c, "url") + edges.add((name, "contact", c)) + for l in agg["laws"]: + nodes.setdefault(l, "law") + + return {"nodes": nodes, "edges": list(edges)} + + +# ── main ── + +def main(): + print("[load] loading model + tokenizer...", flush=True) + t0 = time.time() + import torch + from transformers import AutoTokenizer, AutoModelForImageTextToText + + if not torch.cuda.is_available(): + print("CUDA not available — exiting", flush=True) + return + device = "cuda" + dtype = torch.bfloat16 + print(f"[device] {device} dtype={dtype}", flush=True) + + tokenizer = AutoTokenizer.from_pretrained( + "numind/NuExtract-2.0-2B", trust_remote_code=True, padding_side="left", + ) + model = AutoModelForImageTextToText.from_pretrained( + "numind/NuExtract-2.0-2B", + trust_remote_code=True, + torch_dtype=dtype, + attn_implementation="sdpa", + ).to(device) + model.eval() + print(f"[load] done in {time.time()-t0:.1f}s", flush=True) + + out: dict = {"meta": {"device": device, "dtype": str(dtype), + "model": "numind/NuExtract-2.0-2B", + "repetition_penalty": 1.15, "max_chars_chunk": 800}} + + # ── A. LONG_TEXT_ES con chunking + print("\n[A] LONG_TEXT_ES — chunking + run...", flush=True) + long_chunks = chunk_with_overlap(LONG_TEXT_ES, max_chars=800, overlap_sentences=1) + print(f" {len(LONG_TEXT_ES)} chars → {len(long_chunks)} chunks", flush=True) + long_results = [] + t_start = time.time() + for i, c in enumerate(long_chunks): + r = run_extract(model, tokenizer, device, c["text"], SCHEMA_RICH_CORPORATE) + ok = "OK" if r["parsed"] else "FAIL" + print(f" [chunk {i+1}/{len(long_chunks)}] {len(c['text'])}c {r['elapsed_s']}s out={r['n_output_tokens']} {ok}", flush=True) + long_results.append(r) + long_elapsed = time.time() - t_start + long_agg = aggregate_corporate(long_results) + # alias map sobre todos los nombres mencionados + all_names_long = ([o["name"] for o in long_agg["organizations"]] + + [p["name"] for p in long_agg["people"]] + + [hq for o in long_agg["organizations"] for hq in o["headquartered_in"]] + + [s for o in long_agg["organizations"] for s in o["subsidiaries"]]) + alias_long = merge_aliases(list(set(all_names_long))) + long_graph = build_corporate_graph(long_agg, alias_long) + print(f" total {long_elapsed:.1f}s agregado: orgs={len(long_agg['organizations'])} people={len(long_agg['people'])} agreements={len(long_agg['agreements'])}", flush=True) + print(f" grafo: nodos={len(long_graph['nodes'])} aristas={len(long_graph['edges'])}", flush=True) + out["long_text"] = { + "elapsed_s": round(long_elapsed, 1), + "n_chunks": len(long_chunks), + "n_chunks_parsed_ok": sum(1 for r in long_results if r["parsed"] is not None), + "agg": long_agg, + "graph": {"nodes": long_graph["nodes"], "edges": long_graph["edges"]}, + "n_nodes": len(long_graph["nodes"]), + "n_edges": len(long_graph["edges"]), + "n_isolates": sum(1 for n in long_graph["nodes"] if n not in {a for a, _, _ in long_graph["edges"]} | {b for _, _, b in long_graph["edges"]}), + } + del long_results + gc.collect() + + # ── B. PDF entero + print("\n[B] PDF — extract + clean + chunk + run all chunks...", flush=True) + raw = extract_pdf_text(str(PDF_PATH)) + clean = clean_pdf_text(raw) + pdf_chunks = chunk_with_overlap(clean, max_chars=800, overlap_sentences=1) + print(f" PDF: {len(raw):,} → {len(clean):,} chars → {len(pdf_chunks)} chunks", flush=True) + pdf_results = [] + t_start = time.time() + for i, c in enumerate(pdf_chunks): + r = run_extract(model, tokenizer, device, c["text"], SCHEMA_RICH_GDPR) + if (i+1) % 10 == 0: + ok_count = sum(1 for r in pdf_results if r["parsed"] is not None) + print(f" [chunk {i+1}/{len(pdf_chunks)}] {ok_count}/{i+1} parsed OK ({time.time()-t_start:.0f}s acumulado)", flush=True) + pdf_results.append(r) + pdf_elapsed = time.time() - t_start + pdf_agg = aggregate_gdpr(pdf_results) + # alias map para autoridades + data controllers + all_names_pdf = ([dc["name"] for dc in pdf_agg["data_controllers"] if dc.get("name")] + + [a["name"] for a in pdf_agg["authorities"]]) + alias_pdf = merge_aliases(list(set(all_names_pdf))) + pdf_graph = build_gdpr_graph(pdf_agg, alias_pdf) + print(f" total {pdf_elapsed:.1f}s = {pdf_elapsed/60:.1f} min", flush=True) + print(f" parsed OK: {sum(1 for r in pdf_results if r['parsed'] is not None)}/{len(pdf_chunks)}", flush=True) + print(f" grafo: nodos={len(pdf_graph['nodes'])} aristas={len(pdf_graph['edges'])}", flush=True) + out["pdf"] = { + "elapsed_s": round(pdf_elapsed, 1), + "n_chunks": len(pdf_chunks), + "n_chunks_parsed_ok": sum(1 for r in pdf_results if r["parsed"] is not None), + "agg_summary": { + "n_data_controllers": len(pdf_agg["data_controllers"]), + "n_dpo_contacts": len(pdf_agg["dpo_contacts"]), + "n_data_categories": len(pdf_agg["data_categories"]), + "n_rights": len(pdf_agg["rights_listed"]), + "n_authorities": len(pdf_agg["authorities"]), + "n_laws": len(pdf_agg["laws"]), + }, + "agg_full": pdf_agg, + "graph": {"nodes": pdf_graph["nodes"], "edges": pdf_graph["edges"]}, + "n_nodes": len(pdf_graph["nodes"]), + "n_edges": len(pdf_graph["edges"]), + } + + out_path = HERE / "nuextract_full.json" + out_path.write_text(json.dumps(out, indent=2, ensure_ascii=False)) + print(f"\n[saved] {out_path}", flush=True) + + +if __name__ == "__main__": + main() diff --git a/run_nuextract_test.py b/run_nuextract_test.py new file mode 100644 index 0000000..760dfb8 --- /dev/null +++ b/run_nuextract_test.py @@ -0,0 +1,307 @@ +"""Benchmark NuExtract 2.0-2B (MIT) sobre nuestros corpora. + +Mide tiempo y calidad sobre: + T1. es_corporate_short (8 frases) con schema simple (paridad con notebook 02) + T2. es_corporate_short con schema rico anidado (lo que NuExtract hace mejor) + T3. LONG_TEXT_ES del notebook 05/06 (25 frases, sector bancario) + T4. 5 chunks del PDF de BBVA (extrapolar al PDF completo) + +Vuelca a nuextract_results.json para que el notebook lo cargue sin recargar el modelo. +""" +from __future__ import annotations + +import json +import os +import re +import sys +import time +import warnings +from pathlib import Path + +warnings.filterwarnings("ignore") +os.environ.setdefault("HF_HUB_DISABLE_PROGRESS_BARS", "1") + +HERE = Path(__file__).resolve().parent +_pf = "/home/lucas/fn_registry/python/functions" +sys.path = [p for p in sys.path if not p.startswith(_pf + "/")] +if _pf not in sys.path: + sys.path.insert(0, _pf) + +from core.extract_pdf_text import extract_pdf_text + + +VAULT = Path("/home/lucas/vaults/osint_nlp_models") +PDF_PATH = VAULT / "test_documents" / "politica_proteccion_datos.pdf" + + +def clean_pdf_text(text: str) -> str: + text = re.sub(r"\b\d{1,2}/\d{1,2}\b", " ", text) + text = text.replace("\t", " ") + text = re.sub(r"-\s*\n\s*", "", text) + text = re.sub(r"(? 0: + prev_sents = chunks[-1]["sentences"][-overlap_sentences:] + overlap_len = sum(len(s) + 1 for s in prev_sents) + next_sentence_len = len(sentences[i]) + 1 + if overlap_len + next_sentence_len <= max_chars: + current_sents = list(prev_sents) + current_len = overlap_len + if i < len(sentences): + current_sents.append(sentences[i]) + current_len += len(sentences[i]) + 1 + i += 1 + while i < len(sentences) and current_len + len(sentences[i]) + 1 <= max_chars: + current_sents.append(sentences[i]) + current_len += len(sentences[i]) + 1 + i += 1 + chunks.append({"text": " ".join(current_sents), "sentences": current_sents}) + return chunks + + +# ── corpora ── +ES_CORPORATE_SHORT = ( + "Pablo Isla, expresidente de Inditex, ha sido nombrado consejero de Telefonica. " + "La operacion fue anunciada por el presidente Jose Maria Alvarez-Pallete en Madrid el pasado lunes. " + "Inditex factura mas de 30.000 millones anuales y tiene su sede en Arteixo, A Coruna. " + "En paralelo, Iberdrola y Endesa firmaron un acuerdo de colaboracion en proyectos eolicos en Galicia. " + "El presidente de Iberdrola, Ignacio Galan, se reunio con la CEO de Endesa, Marina Serrano, en Bilbao. " + "El acuerdo movilizara 2.000 millones de euros en cinco anos. " + "El BBVA, presidido por Carlos Torres, mostro interes en participar en la financiacion del proyecto. " + "Su sede central esta en Bilbao." +) + +LONG_TEXT_ES = ( + "BBVA, presidido por Carlos Torres, completo en 2024 la integracion operativa de Banco Sabadell tras la fusion. " + "Onur Genc, consejero delegado del banco desde 2018, lidero el proceso desde la sede central en Bilbao. " + "El banco mantiene oficinas en Plaza San Nicolas 4 y opera en mas de 25 paises. " + "Banco Santander, dirigido por Ana Botin, sigue siendo el primer banco espanol por capitalizacion bursatil. " + "Hector Grisi asumio el cargo de CEO global de Santander en enero de 2023, reemplazando a Jose Antonio Alvarez. " + "CaixaBank, presidida por Jose Ignacio Goirigolzarri y con sede en Valencia desde 2017, completo la fusion con Bankia. " + "Gonzalo Gortazar es el consejero delegado de CaixaBank y reporta al consejo formado en parte por La Caixa. " + "El Banco de Espana, gobernado por Pablo Hernandez de Cos hasta 2024 y por Margarita Delgado en 2025, supervisa el sector. " + "Luis de Guindos, vicepresidente del Banco Central Europeo, fue ministro de Economia en el gobierno de Mariano Rajoy. " + "La Comision Nacional del Mercado de Valores, presidida por Rodrigo Buenaventura, regula los mercados financieros. " + "BBVA anuncio en mayo de 2024 una OPA hostil sobre Banco Sabadell que el consejo del banco rechazo inicialmente. " + "Cesar Gonzalez-Bueno, CEO de Sabadell, defendio la independencia del banco junto con su presidente Josep Oliu. " + "Repsol, presidida por Antonio Brufau y con CEO Josu Jon Imaz, vendio su filial mexicana a Macquarie. " + "Iberdrola, liderada por Ignacio Galan, opera Avangrid en EEUU y firmo un acuerdo PPA con Amazon. " + "Andy Jassy, CEO de Amazon desde Seattle, agradecio el contrato a Iberdrola en una nota publica. " + "Endesa, filial de la italiana Enel, tiene como CEO a Marina Serrano y opera en Espana, Portugal y Marruecos. " + "Ferrovial, presidida por Rafael del Pino, traslado su sede social a Holanda en 2022 generando polemica politica. " + "ACS, presidida por Florentino Perez, sigue siendo lider mundial en concesiones de infraestructura. " + "Inditex, fundada por Amancio Ortega y presidida por Marta Ortega desde 2022, tiene su sede en Arteixo, A Coruna. " + "Pablo Isla, expresidente de Inditex y actual consejero de Telefonica, se incorporo al consejo en 2024. " + "Telefonica, presidida por Jose Maria Alvarez-Pallete, sufrio la entrada del estado en su capital con SEPI. " + "Saudi Telecom Company adquirio un 9.9% de Telefonica en 2023, lo que motivo la respuesta del gobierno espanol. " + "Cristina Aldamiz-Echevarria fue nombrada directora de Recursos Humanos del Grupo Mapfre, dirigido por Antonio Huertas. " + "Naturgy, presidida por Francisco Reynes, recibio una OPA parcial del fondo emirati IFM en 2021 que se cancelo. " + "Indra, con Marc Murtra como presidente, se ha posicionado como contratista clave de Defensa para el ministerio de Margarita Robles." +) + +# ── schemas ── +SCHEMA_FLAT = """{ + "people": ["string"], + "organizations": ["string"], + "locations": ["string"] +}""" + +SCHEMA_RICH_CORPORATE = """{ + "organizations": [ + { + "name": "string", + "ceo": "string", + "chairman_president": "string", + "headquartered_in": "string", + "subsidiaries": ["string"], + "parent_company": "string" + } + ], + "people": [ + { + "name": "string", + "role": "string", + "organization": "string" + } + ], + "agreements": [ + { + "between": ["string"], + "topic": "string", + "amount": "string" + } + ] +}""" + +SCHEMA_RICH_GDPR = """{ + "data_controller": { + "name": "string", + "address": "string", + "registration": "string" + }, + "dpo_contact": { + "email": "string", + "address": "string" + }, + "data_categories": ["string"], + "rights_listed": ["string"], + "authorities_mentioned": [ + { + "name": "string", + "url_or_contact": "string" + } + ], + "laws_mentioned": ["string"] +}""" + + +def build_messages(tokenizer, document: str, template: str) -> str: + messages = [{"role": "user", "content": document}] + return tokenizer.apply_chat_template( + messages, template=template, tokenize=False, add_generation_prompt=True, + ) + + +def run_extract(model, tokenizer, device: str, document: str, template: str, max_new_tokens: int = 2048): + text = build_messages(tokenizer, document, template) + inputs = tokenizer([text], padding=True, return_tensors="pt").to(device) + t0 = time.time() + generated = model.generate( + **inputs, do_sample=False, num_beams=1, max_new_tokens=max_new_tokens, + pad_token_id=tokenizer.eos_token_id, + ) + elapsed = time.time() - t0 + n_input_tokens = inputs["input_ids"].shape[1] + n_output_tokens = generated.shape[1] - n_input_tokens + # extract just the generated portion + out_text = tokenizer.decode(generated[0][n_input_tokens:], skip_special_tokens=True) + return { + "elapsed_s": round(elapsed, 2), + "n_input_tokens": int(n_input_tokens), + "n_output_tokens": int(n_output_tokens), + "raw_text": out_text, + } + + +def parse_json_safe(text: str): + # NuExtract output is JSON after the last assistant message; try to find it + s = text.rfind("{") + if s == -1: return None + # try progressively shorter substrings to find valid json end + for end in range(len(text), s, -1): + try: + return json.loads(text[s:end]) + except Exception: + continue + return None + + +def main(): + print("[load] loading model + tokenizer...", flush=True) + t0 = time.time() + import torch + from transformers import AutoTokenizer, AutoModelForImageTextToText + + use_gpu = torch.cuda.is_available() + device = "cuda" if use_gpu else "cpu" + dtype = torch.bfloat16 if use_gpu else torch.float32 + print(f"[device] {device} dtype={dtype}", flush=True) + if use_gpu: + print(f"[gpu] {torch.cuda.get_device_name(0)} {torch.cuda.get_device_properties(0).total_memory/1e9:.1f}GB", flush=True) + + tokenizer = AutoTokenizer.from_pretrained( + "numind/NuExtract-2.0-2B", trust_remote_code=True, padding_side="left", + ) + # Try SDPA (fast and supported), fallback to eager. flash_attn requires extra install. + attn_impl = "sdpa" if use_gpu else "eager" + model = AutoModelForImageTextToText.from_pretrained( + "numind/NuExtract-2.0-2B", + trust_remote_code=True, + torch_dtype=dtype, + attn_implementation=attn_impl, + ) + if use_gpu: + model = model.to(device) + model.eval() + print(f"[load] done in {time.time()-t0:.1f}s", flush=True) + out: dict = { + "meta": {"device": device, "dtype": str(dtype), "model": "numind/NuExtract-2.0-2B"}, + "cpu_baseline": { # capturado en run anterior, antes del switch a GPU + "T1_flat": {"elapsed_s": 24.98, "in_tok": 245, "out_tok": 79}, + "T2_rich": {"elapsed_s": 117.51, "in_tok": 351, "out_tok": 370}, + }, + } + + # T1: es_corporate_short con schema FLAT + print("\n[T1] es_corporate_short + SCHEMA_FLAT...", flush=True) + r = run_extract(model, tokenizer, device, ES_CORPORATE_SHORT, SCHEMA_FLAT) + parsed = parse_json_safe(r["raw_text"]) + print(f" {r['elapsed_s']}s in_tok={r['n_input_tokens']} out_tok={r['n_output_tokens']}", flush=True) + out["T1_corp_short_flat"] = {**r, "parsed": parsed, "input_chars": len(ES_CORPORATE_SHORT)} + + # T2: es_corporate_short con SCHEMA_RICH_CORPORATE + print("\n[T2] es_corporate_short + SCHEMA_RICH_CORPORATE...", flush=True) + r = run_extract(model, tokenizer, device, ES_CORPORATE_SHORT, SCHEMA_RICH_CORPORATE) + parsed = parse_json_safe(r["raw_text"]) + print(f" {r['elapsed_s']}s in_tok={r['n_input_tokens']} out_tok={r['n_output_tokens']}", flush=True) + out["T2_corp_short_rich"] = {**r, "parsed": parsed, "input_chars": len(ES_CORPORATE_SHORT)} + + # T3: LONG_TEXT_ES con SCHEMA_RICH_CORPORATE + print("\n[T3] LONG_TEXT_ES (25 frases, 400 words) + SCHEMA_RICH_CORPORATE...", flush=True) + r = run_extract(model, tokenizer, device, LONG_TEXT_ES, SCHEMA_RICH_CORPORATE) + parsed = parse_json_safe(r["raw_text"]) + print(f" {r['elapsed_s']}s in_tok={r['n_input_tokens']} out_tok={r['n_output_tokens']}", flush=True) + out["T3_long_text_rich"] = {**r, "parsed": parsed, "input_chars": len(LONG_TEXT_ES)} + + # T4: 5 chunks del PDF + print("\n[T4] preparing PDF...", flush=True) + raw = extract_pdf_text(str(PDF_PATH)) + clean = clean_pdf_text(raw) + chunks = chunk_with_overlap(clean, max_chars=1500, overlap_sentences=2) + out["pdf_meta"] = {"n_chunks": len(chunks), "clean_chars": len(clean)} + print(f" PDF: {len(clean):,} chars / {len(chunks)} chunks total — corremos solo 5 representativos", flush=True) + + chunk_indices = [0, 5, 15, 30, 60] # representativos: inicio / medio / final + chunk_results = [] + for idx in chunk_indices: + if idx >= len(chunks): continue + c = chunks[idx] + print(f" [chunk {idx}] {len(c['text'])}c — running...", flush=True) + r = run_extract(model, tokenizer, device, c["text"], SCHEMA_RICH_GDPR) + parsed = parse_json_safe(r["raw_text"]) + print(f" {r['elapsed_s']}s in_tok={r['n_input_tokens']} out_tok={r['n_output_tokens']}", flush=True) + chunk_results.append({"chunk_idx": idx, **r, "parsed": parsed, "input_chars": len(c["text"])}) + + out["T4_pdf_chunks"] = chunk_results + + # extrapolate full PDF time + if chunk_results: + avg_per_chunk = sum(cr["elapsed_s"] for cr in chunk_results) / len(chunk_results) + full_pdf_estimate = avg_per_chunk * len(chunks) + out["full_pdf_extrapolation"] = { + "avg_per_chunk_s": round(avg_per_chunk, 2), + "n_chunks": len(chunks), + "estimated_total_s": round(full_pdf_estimate, 1), + "estimated_total_min": round(full_pdf_estimate / 60, 1), + } + print(f"\n[extrapolation] PDF entero estimado: {full_pdf_estimate:.0f}s = {full_pdf_estimate/60:.1f} min", flush=True) + + out_path = HERE / "nuextract_results.json" + out_path.write_text(json.dumps(out, indent=2, ensure_ascii=False)) + print(f"\n[saved] {out_path}", flush=True) + + +if __name__ == "__main__": + main() diff --git a/run_openie_test.py b/run_openie_test.py new file mode 100644 index 0000000..c2fb009 --- /dev/null +++ b/run_openie_test.py @@ -0,0 +1,188 @@ +"""Estudio de OpenIE / extraccion schema-less. + +Compara 3 paradigmas sobre el mismo conjunto de textos: + A. triplet-extract (EN) — pip install triplet-extract, OpenIE moderno spaCy-based + B. spaCy ES dependency rules — version casera para castellano + C. GLiNER2 con schema universal — schema-driven con relaciones amplias + +Vuelca a openie_results.json para que el notebook lo cargue sin recargar modelos. +""" +from __future__ import annotations + +import json +import os +import sys +import time +import warnings +from pathlib import Path + +warnings.filterwarnings("ignore") +os.environ.setdefault("HF_HUB_DISABLE_PROGRESS_BARS", "1") + +HERE = Path(__file__).resolve().parent +_pf = "/home/lucas/fn_registry/python/functions" +sys.path = [p for p in sys.path if not p.startswith(_pf + "/")] +if _pf not in sys.path: + sys.path.insert(0, _pf) + + +# ── Corpus EN (donde triplet-extract puede correr nativo) ── +CORPUS_EN = { + "personal_simple": "John kissed Mary at the park.", + "personal_love": "Anna loves Bob and Bob admires Anna.", + "corporate_short": "Carlos Torres chairs BBVA which has its headquarters in Bilbao.", + "corporate_history": "Pablo Isla chaired Inditex from 2011 to 2022 and now serves on the board of Telefonica.", + "mixed_emotional": "After the meeting, Sarah hugged her brother Tom who had just graduated.", +} + +# ── Corpus ES (probando version nativa spaCy + schema-driven GLiNER2) ── +CORPUS_ES = { + "personal_simple": "Enmanuel quiere a Ashlly desde hace anos.", + "personal_family": "Maria abrazo a su hermano Tomas tras la reunion.", + "corporate_short": "Carlos Torres preside BBVA, con sede central en Bilbao.", + "corporate_history": "Pablo Isla presidio Inditex de 2011 a 2022 y ahora forma parte del consejo de Telefonica.", + "mixed_emotional": "Despues de la cena, Sara llamo a su madre Lucia para contarle las noticias.", +} + + +def run_triplet_extract_en(): + """A. triplet-extract sobre corpus EN.""" + from triplet_extract import extract + out = {} + print("\n[A] triplet-extract EN...", flush=True) + for name, text in CORPUS_EN.items(): + t0 = time.time() + triples = extract(text) + elapsed = time.time() - t0 + out[name] = { + "text": text, + "elapsed_s": round(elapsed, 3), + "n_triples": len(triples), + "triples": [ + {"subject": t.subject, "relation": t.relation, "object": t.object, + "confidence": round(float(t.confidence), 2)} + for t in triples + ], + } + print(f" {name}: {len(triples)} triples en {elapsed:.2f}s", flush=True) + return out + + +def run_spacy_es_dep_rules(): + """B. spaCy es_core_news_md + reglas de dependencia → tripletas.""" + import spacy + print("\n[B] spaCy ES dep-rules...", flush=True) + t0 = time.time() + nlp = spacy.load("es_core_news_md") + print(f" load: {time.time()-t0:.1f}s", flush=True) + + def extract_triples_es(doc): + """Para cada verbo: + - subject = token con dep nsubj/nsubj:pass (o el sujeto pronominal implicito) + - object = nsubj+obj+obl (acepta diferentes preps) + """ + triples = [] + for token in doc: + if token.pos_ != "VERB" and token.pos_ != "AUX": + continue + # encontrar sujeto + subjs = [c for c in token.children if c.dep_ in ("nsubj", "nsubj:pass", "csubj")] + # objetos directos / oblicuos / atributos + objs_direct = [c for c in token.children if c.dep_ in ("obj", "dobj", "iobj", "attr")] + objs_oblique = [c for c in token.children if c.dep_ in ("obl", "obl:agent", "nmod")] + # tambien capturar "X a Y" (objeto preposicional con "a") + for c in token.children: + if c.dep_ == "obl" or c.dep_ == "obl:agent": + objs_oblique.append(c) + + for s in subjs: + # span del sujeto (incluye modificadores) + s_text = " ".join([t.text for t in s.subtree]) + # primero objetos directos + for o in objs_direct + objs_oblique: + o_text = " ".join([t.text for t in o.subtree]) + triples.append({ + "subject": s_text, + "relation": token.lemma_, + "object": o_text, + "verb_form": token.text, + }) + return triples + + out = {} + for name, text in CORPUS_ES.items(): + t0 = time.time() + doc = nlp(text) + triples = extract_triples_es(doc) + elapsed = time.time() - t0 + # tambien NER para reportar entidades + ents = [{"text": e.text, "label": e.label_} for e in doc.ents] + out[name] = { + "text": text, + "elapsed_s": round(elapsed, 3), + "n_triples": len(triples), + "n_ents": len(ents), + "triples": triples, + "entities": ents, + } + print(f" {name}: {len(triples)} triples + {len(ents)} ents en {elapsed:.3f}s", flush=True) + return out + + +def run_gliner2_universal(): + """C. GLiNER2 con schema universal (entity types amplios + relaciones diversas).""" + from gliner2 import GLiNER2 + print("\n[C] GLiNER2 universal schema (ES)...", flush=True) + t0 = time.time() + model = GLiNER2.from_pretrained("fastino/gliner2-large-v1") + print(f" load: {time.time()-t0:.1f}s", flush=True) + + UNIVERSAL_ENT_LABELS = [ + "person", "organization", "location", "place", + "date", "money", "product", "event", + ] + UNIVERSAL_REL_LABELS = [ + # personal + "loves", "knows", "married_to", "parent_of", "child_of", "sibling_of", "friend_of", "kissed", "hugged", + # work + "works_at", "ceo_of", "president_of", "employed_by", "member_of", + # spatial + "located_in", "headquartered_in", "born_in", "lives_in", "from", + # corporate + "subsidiary_of", "founded_by", "agreement_with", "acquired", + # generic + "related_to", "mentions", "part_of", "owns", + ] + schema = model.create_schema().entities(UNIVERSAL_ENT_LABELS).relations(UNIVERSAL_REL_LABELS) + + out = {} + for name, text in CORPUS_ES.items(): + t0 = time.time() + r = model.extract(text, schema=schema, threshold=0.3) + elapsed = time.time() - t0 + n_ents = sum(len(v) for v in r["entities"].values()) + n_rels = sum(len(v) for v in r["relation_extraction"].values()) + out[name] = { + "text": text, + "elapsed_s": round(elapsed, 3), + "n_ents": n_ents, + "n_rels": n_rels, + "entities": {k: list(v) for k, v in r["entities"].items() if v}, + "relations": {k: list(v) for k, v in r["relation_extraction"].items() if v}, + } + print(f" {name}: {n_ents} ents + {n_rels} rels en {elapsed:.2f}s", flush=True) + return out + + +def main(): + out: dict = {"corpus_en": CORPUS_EN, "corpus_es": CORPUS_ES} + out["A_triplet_extract_en"] = run_triplet_extract_en() + out["B_spacy_es_dep"] = run_spacy_es_dep_rules() + out["C_gliner2_universal_es"] = run_gliner2_universal() + out_path = HERE / "openie_results.json" + out_path.write_text(json.dumps(out, indent=2, ensure_ascii=False)) + print(f"\n[saved] {out_path}", flush=True) + + +if __name__ == "__main__": + main() diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..e78af76 --- /dev/null +++ b/uv.lock @@ -0,0 +1,4017 @@ +version = 1 +revision = 3 +requires-python = ">=3.13" +resolution-markers = [ + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version < '3.14' and sys_platform == 'win32'", + "python_full_version < '3.14' and sys_platform == 'emscripten'", + "python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", +] + +[[package]] +name = "aiohappyeyeballs" +version = "2.6.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload-time = "2025-03-12T01:42:48.764Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265, upload-time = "2025-03-12T01:42:47.083Z" }, +] + +[[package]] +name = "aiohttp" +version = "3.13.5" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "aiohappyeyeballs" }, + { name = "aiosignal" }, + { name = "attrs" }, + { name = "frozenlist" }, + { name = "multidict" }, + { name = "propcache" }, + { name = "yarl" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/77/9a/152096d4808df8e4268befa55fba462f440f14beab85e8ad9bf990516918/aiohttp-3.13.5.tar.gz", hash = "sha256:9d98cc980ecc96be6eb4c1994ce35d28d8b1f5e5208a23b421187d1209dbb7d1", size = 7858271, upload-time = "2026-03-31T22:01:03.343Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/78/e9/d76bf503005709e390122d34e15256b88f7008e246c4bdbe915cd4f1adce/aiohttp-3.13.5-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:a5029cc80718bbd545123cd8fe5d15025eccaaaace5d0eeec6bd556ad6163d61", size = 742930, upload-time = "2026-03-31T21:58:13.155Z" }, + { url = "https://files.pythonhosted.org/packages/57/00/4b7b70223deaebd9bb85984d01a764b0d7bd6526fcdc73cca83bcbe7243e/aiohttp-3.13.5-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:4bb6bf5811620003614076bdc807ef3b5e38244f9d25ca5fe888eaccea2a9832", size = 496927, upload-time = "2026-03-31T21:58:15.073Z" }, + { url = "https://files.pythonhosted.org/packages/9c/f5/0fb20fb49f8efdcdce6cd8127604ad2c503e754a8f139f5e02b01626523f/aiohttp-3.13.5-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:a84792f8631bf5a94e52d9cc881c0b824ab42717165a5579c760b830d9392ac9", size = 497141, upload-time = "2026-03-31T21:58:17.009Z" }, + { url = "https://files.pythonhosted.org/packages/3b/86/b7c870053e36a94e8951b803cb5b909bfbc9b90ca941527f5fcafbf6b0fa/aiohttp-3.13.5-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:57653eac22c6a4c13eb22ecf4d673d64a12f266e72785ab1c8b8e5940d0e8090", size = 1732476, upload-time = "2026-03-31T21:58:18.925Z" }, + { url = "https://files.pythonhosted.org/packages/b5/e5/4e161f84f98d80c03a238671b4136e6530453d65262867d989bbe78244d0/aiohttp-3.13.5-cp313-cp313-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:e5e5f7debc7a57af53fdf5c5009f9391d9f4c12867049d509bf7bb164a6e295b", size = 1706507, upload-time = "2026-03-31T21:58:21.094Z" }, + { url = "https://files.pythonhosted.org/packages/d4/56/ea11a9f01518bd5a2a2fcee869d248c4b8a0cfa0bb13401574fa31adf4d4/aiohttp-3.13.5-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:c719f65bebcdf6716f10e9eff80d27567f7892d8988c06de12bbbd39307c6e3a", size = 1773465, upload-time = "2026-03-31T21:58:23.159Z" }, + { url = "https://files.pythonhosted.org/packages/eb/40/333ca27fb74b0383f17c90570c748f7582501507307350a79d9f9f3c6eb1/aiohttp-3.13.5-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:d97f93fdae594d886c5a866636397e2bcab146fd7a132fd6bb9ce182224452f8", size = 1873523, upload-time = "2026-03-31T21:58:25.59Z" }, + { url = "https://files.pythonhosted.org/packages/f0/d2/e2f77eef1acb7111405433c707dc735e63f67a56e176e72e9e7a2cd3f493/aiohttp-3.13.5-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:3df334e39d4c2f899a914f1dba283c1aadc311790733f705182998c6f7cae665", size = 1754113, upload-time = "2026-03-31T21:58:27.624Z" }, + { url = "https://files.pythonhosted.org/packages/fb/56/3f653d7f53c89669301ec9e42c95233e2a0c0a6dd051269e6e678db4fdb0/aiohttp-3.13.5-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:fe6970addfea9e5e081401bcbadf865d2b6da045472f58af08427e108d618540", size = 1562351, upload-time = "2026-03-31T21:58:29.918Z" }, + { url = "https://files.pythonhosted.org/packages/ec/a6/9b3e91eb8ae791cce4ee736da02211c85c6f835f1bdfac0594a8a3b7018c/aiohttp-3.13.5-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:7becdf835feff2f4f335d7477f121af787e3504b48b449ff737afb35869ba7bb", size = 1693205, upload-time = "2026-03-31T21:58:32.214Z" }, + { url = "https://files.pythonhosted.org/packages/98/fc/bfb437a99a2fcebd6b6eaec609571954de2ed424f01c352f4b5504371dd3/aiohttp-3.13.5-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:676e5651705ad5d8a70aeb8eb6936c436d8ebbd56e63436cb7dd9bb36d2a9a46", size = 1730618, upload-time = "2026-03-31T21:58:34.728Z" }, + { url = "https://files.pythonhosted.org/packages/e4/b6/c8534862126191a034f68153194c389addc285a0f1347d85096d349bbc15/aiohttp-3.13.5-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:9b16c653d38eb1a611cc898c41e76859ca27f119d25b53c12875fd0474ae31a8", size = 1745185, upload-time = "2026-03-31T21:58:36.909Z" }, + { url = "https://files.pythonhosted.org/packages/0b/93/4ca8ee2ef5236e2707e0fd5fecb10ce214aee1ff4ab307af9c558bda3b37/aiohttp-3.13.5-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:999802d5fa0389f58decd24b537c54aa63c01c3219ce17d1214cbda3c2b22d2d", size = 1557311, upload-time = "2026-03-31T21:58:39.38Z" }, + { url = "https://files.pythonhosted.org/packages/57/ae/76177b15f18c5f5d094f19901d284025db28eccc5ae374d1d254181d33f4/aiohttp-3.13.5-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:ec707059ee75732b1ba130ed5f9580fe10ff75180c812bc267ded039db5128c6", size = 1773147, upload-time = "2026-03-31T21:58:41.476Z" }, + { url = "https://files.pythonhosted.org/packages/01/a4/62f05a0a98d88af59d93b7fcac564e5f18f513cb7471696ac286db970d6a/aiohttp-3.13.5-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:2d6d44a5b48132053c2f6cd5c8cb14bc67e99a63594e336b0f2af81e94d5530c", size = 1730356, upload-time = "2026-03-31T21:58:44.049Z" }, + { url = "https://files.pythonhosted.org/packages/e4/85/fc8601f59dfa8c9523808281f2da571f8b4699685f9809a228adcc90838d/aiohttp-3.13.5-cp313-cp313-win32.whl", hash = "sha256:329f292ed14d38a6c4c435e465f48bebb47479fd676a0411936cc371643225cc", size = 432637, upload-time = "2026-03-31T21:58:46.167Z" }, + { url = "https://files.pythonhosted.org/packages/c0/1b/ac685a8882896acf0f6b31d689e3792199cfe7aba37969fa91da63a7fa27/aiohttp-3.13.5-cp313-cp313-win_amd64.whl", hash = "sha256:69f571de7500e0557801c0b51f4780482c0ec5fe2ac851af5a92cfce1af1cb83", size = 458896, upload-time = "2026-03-31T21:58:48.119Z" }, + { url = "https://files.pythonhosted.org/packages/5d/ce/46572759afc859e867a5bc8ec3487315869013f59281ce61764f76d879de/aiohttp-3.13.5-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:eb4639f32fd4a9904ab8fb45bf3383ba71137f3d9d4ba25b3b3f3109977c5b8c", size = 745721, upload-time = "2026-03-31T21:58:50.229Z" }, + { url = "https://files.pythonhosted.org/packages/13/fe/8a2efd7626dbe6049b2ef8ace18ffda8a4dfcbe1bcff3ac30c0c7575c20b/aiohttp-3.13.5-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:7e5dc4311bd5ac493886c63cbf76ab579dbe4641268e7c74e48e774c74b6f2be", size = 497663, upload-time = "2026-03-31T21:58:52.232Z" }, + { url = "https://files.pythonhosted.org/packages/9b/91/cc8cc78a111826c54743d88651e1687008133c37e5ee615fee9b57990fac/aiohttp-3.13.5-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:756c3c304d394977519824449600adaf2be0ccee76d206ee339c5e76b70ded25", size = 499094, upload-time = "2026-03-31T21:58:54.566Z" }, + { url = "https://files.pythonhosted.org/packages/0a/33/a8362cb15cf16a3af7e86ed11962d5cd7d59b449202dc576cdc731310bde/aiohttp-3.13.5-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ecc26751323224cf8186efcf7fbcbc30f4e1d8c7970659daf25ad995e4032a56", size = 1726701, upload-time = "2026-03-31T21:58:56.864Z" }, + { url = "https://files.pythonhosted.org/packages/45/0c/c091ac5c3a17114bd76cbf85d674650969ddf93387876cf67f754204bd77/aiohttp-3.13.5-cp314-cp314-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:10a75acfcf794edf9d8db50e5a7ec5fc818b2a8d3f591ce93bc7b1210df016d2", size = 1683360, upload-time = "2026-03-31T21:58:59.072Z" }, + { url = "https://files.pythonhosted.org/packages/23/73/bcee1c2b79bc275e964d1446c55c54441a461938e70267c86afaae6fba27/aiohttp-3.13.5-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:0f7a18f258d124cd678c5fe072fe4432a4d5232b0657fca7c1847f599233c83a", size = 1773023, upload-time = "2026-03-31T21:59:01.776Z" }, + { url = "https://files.pythonhosted.org/packages/c7/ef/720e639df03004fee2d869f771799d8c23046dec47d5b81e396c7cda583a/aiohttp-3.13.5-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:df6104c009713d3a89621096f3e3e88cc323fd269dbd7c20afe18535094320be", size = 1853795, upload-time = "2026-03-31T21:59:04.568Z" }, + { url = "https://files.pythonhosted.org/packages/bd/c9/989f4034fb46841208de7aeeac2c6d8300745ab4f28c42f629ba77c2d916/aiohttp-3.13.5-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:241a94f7de7c0c3b616627aaad530fe2cb620084a8b144d3be7b6ecfe95bae3b", size = 1730405, upload-time = "2026-03-31T21:59:07.221Z" }, + { url = "https://files.pythonhosted.org/packages/ce/75/ee1fd286ca7dc599d824b5651dad7b3be7ff8d9a7e7b3fe9820d9180f7db/aiohttp-3.13.5-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:c974fb66180e58709b6fc402846f13791240d180b74de81d23913abe48e96d94", size = 1558082, upload-time = "2026-03-31T21:59:09.484Z" }, + { url = "https://files.pythonhosted.org/packages/c3/20/1e9e6650dfc436340116b7aa89ff8cb2bbdf0abc11dfaceaad8f74273a10/aiohttp-3.13.5-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:6e27ea05d184afac78aabbac667450c75e54e35f62238d44463131bd3f96753d", size = 1692346, upload-time = "2026-03-31T21:59:12.068Z" }, + { url = "https://files.pythonhosted.org/packages/d8/40/8ebc6658d48ea630ac7903912fe0dd4e262f0e16825aa4c833c56c9f1f56/aiohttp-3.13.5-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:a79a6d399cef33a11b6f004c67bb07741d91f2be01b8d712d52c75711b1e07c7", size = 1698891, upload-time = "2026-03-31T21:59:14.552Z" }, + { url = "https://files.pythonhosted.org/packages/d8/78/ea0ae5ec8ba7a5c10bdd6e318f1ba5e76fcde17db8275188772afc7917a4/aiohttp-3.13.5-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:c632ce9c0b534fbe25b52c974515ed674937c5b99f549a92127c85f771a78772", size = 1742113, upload-time = "2026-03-31T21:59:17.068Z" }, + { url = "https://files.pythonhosted.org/packages/8a/66/9d308ed71e3f2491be1acb8769d96c6f0c47d92099f3bc9119cada27b357/aiohttp-3.13.5-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:fceedde51fbd67ee2bcc8c0b33d0126cc8b51ef3bbde2f86662bd6d5a6f10ec5", size = 1553088, upload-time = "2026-03-31T21:59:19.541Z" }, + { url = "https://files.pythonhosted.org/packages/da/a6/6cc25ed8dfc6e00c90f5c6d126a98e2cf28957ad06fa1036bd34b6f24a2c/aiohttp-3.13.5-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:f92995dfec9420bb69ae629abf422e516923ba79ba4403bc750d94fb4a6c68c1", size = 1757976, upload-time = "2026-03-31T21:59:22.311Z" }, + { url = "https://files.pythonhosted.org/packages/c1/2b/cce5b0ffe0de99c83e5e36d8f828e4161e415660a9f3e58339d07cce3006/aiohttp-3.13.5-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:20ae0ff08b1f2c8788d6fb85afcb798654ae6ba0b747575f8562de738078457b", size = 1712444, upload-time = "2026-03-31T21:59:24.635Z" }, + { url = "https://files.pythonhosted.org/packages/6c/cf/9e1795b4160c58d29421eafd1a69c6ce351e2f7c8d3c6b7e4ca44aea1a5b/aiohttp-3.13.5-cp314-cp314-win32.whl", hash = "sha256:b20df693de16f42b2472a9c485e1c948ee55524786a0a34345511afdd22246f3", size = 438128, upload-time = "2026-03-31T21:59:27.291Z" }, + { url = "https://files.pythonhosted.org/packages/22/4d/eaedff67fc805aeba4ba746aec891b4b24cebb1a7d078084b6300f79d063/aiohttp-3.13.5-cp314-cp314-win_amd64.whl", hash = "sha256:f85c6f327bf0b8c29da7d93b1cabb6363fb5e4e160a32fa241ed2dce21b73162", size = 464029, upload-time = "2026-03-31T21:59:29.429Z" }, + { url = "https://files.pythonhosted.org/packages/79/11/c27d9332ee20d68dd164dc12a6ecdef2e2e35ecc97ed6cf0d2442844624b/aiohttp-3.13.5-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:1efb06900858bb618ff5cee184ae2de5828896c448403d51fb633f09e109be0a", size = 778758, upload-time = "2026-03-31T21:59:31.547Z" }, + { url = "https://files.pythonhosted.org/packages/04/fb/377aead2e0a3ba5f09b7624f702a964bdf4f08b5b6728a9799830c80041e/aiohttp-3.13.5-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:fee86b7c4bd29bdaf0d53d14739b08a106fdda809ca5fe032a15f52fae5fe254", size = 512883, upload-time = "2026-03-31T21:59:34.098Z" }, + { url = "https://files.pythonhosted.org/packages/bb/a6/aa109a33671f7a5d3bd78b46da9d852797c5e665bfda7d6b373f56bff2ec/aiohttp-3.13.5-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:20058e23909b9e65f9da62b396b77dfa95965cbe840f8def6e572538b1d32e36", size = 516668, upload-time = "2026-03-31T21:59:36.497Z" }, + { url = "https://files.pythonhosted.org/packages/79/b3/ca078f9f2fa9563c36fb8ef89053ea2bb146d6f792c5104574d49d8acb63/aiohttp-3.13.5-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8cf20a8d6868cb15a73cab329ffc07291ba8c22b1b88176026106ae39aa6df0f", size = 1883461, upload-time = "2026-03-31T21:59:38.723Z" }, + { url = "https://files.pythonhosted.org/packages/b7/e3/a7ad633ca1ca497b852233a3cce6906a56c3225fb6d9217b5e5e60b7419d/aiohttp-3.13.5-cp314-cp314t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:330f5da04c987f1d5bdb8ae189137c77139f36bd1cb23779ca1a354a4b027800", size = 1747661, upload-time = "2026-03-31T21:59:41.187Z" }, + { url = "https://files.pythonhosted.org/packages/33/b9/cd6fe579bed34a906d3d783fe60f2fa297ef55b27bb4538438ee49d4dc41/aiohttp-3.13.5-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:6f1cbf0c7926d315c3c26c2da41fd2b5d2fe01ac0e157b78caefc51a782196cf", size = 1863800, upload-time = "2026-03-31T21:59:43.84Z" }, + { url = "https://files.pythonhosted.org/packages/c0/3f/2c1e2f5144cefa889c8afd5cf431994c32f3b29da9961698ff4e3811b79a/aiohttp-3.13.5-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:53fc049ed6390d05423ba33103ded7281fe897cf97878f369a527070bd95795b", size = 1958382, upload-time = "2026-03-31T21:59:46.187Z" }, + { url = "https://files.pythonhosted.org/packages/66/1d/f31ec3f1013723b3babe3609e7f119c2c2fb6ef33da90061a705ef3e1bc8/aiohttp-3.13.5-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:898703aa2667e3c5ca4c54ca36cd73f58b7a38ef87a5606414799ebce4d3fd3a", size = 1803724, upload-time = "2026-03-31T21:59:48.656Z" }, + { url = "https://files.pythonhosted.org/packages/0e/b4/57712dfc6f1542f067daa81eb61da282fab3e6f1966fca25db06c4fc62d5/aiohttp-3.13.5-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:0494a01ca9584eea1e5fbd6d748e61ecff218c51b576ee1999c23db7066417d8", size = 1640027, upload-time = "2026-03-31T21:59:51.284Z" }, + { url = "https://files.pythonhosted.org/packages/25/3c/734c878fb43ec083d8e31bf029daae1beafeae582d1b35da234739e82ee7/aiohttp-3.13.5-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:6cf81fe010b8c17b09495cbd15c1d35afbc8fb405c0c9cf4738e5ae3af1d65be", size = 1806644, upload-time = "2026-03-31T21:59:53.753Z" }, + { url = "https://files.pythonhosted.org/packages/20/a5/f671e5cbec1c21d044ff3078223f949748f3a7f86b14e34a365d74a5d21f/aiohttp-3.13.5-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:c564dd5f09ddc9d8f2c2d0a301cd30a79a2cc1b46dd1a73bef8f0038863d016b", size = 1791630, upload-time = "2026-03-31T21:59:56.239Z" }, + { url = "https://files.pythonhosted.org/packages/0b/63/fb8d0ad63a0b8a99be97deac8c04dacf0785721c158bdf23d679a87aa99e/aiohttp-3.13.5-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:2994be9f6e51046c4f864598fd9abeb4fba6e88f0b2152422c9666dcd4aea9c6", size = 1809403, upload-time = "2026-03-31T21:59:59.103Z" }, + { url = "https://files.pythonhosted.org/packages/59/0c/bfed7f30662fcf12206481c2aac57dedee43fe1c49275e85b3a1e1742294/aiohttp-3.13.5-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:157826e2fa245d2ef46c83ea8a5faf77ca19355d278d425c29fda0beb3318037", size = 1634924, upload-time = "2026-03-31T22:00:02.116Z" }, + { url = "https://files.pythonhosted.org/packages/17/d6/fd518d668a09fd5a3319ae5e984d4d80b9a4b3df4e21c52f02251ef5a32e/aiohttp-3.13.5-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:a8aca50daa9493e9e13c0f566201a9006f080e7c50e5e90d0b06f53146a54500", size = 1836119, upload-time = "2026-03-31T22:00:04.756Z" }, + { url = "https://files.pythonhosted.org/packages/78/b7/15fb7a9d52e112a25b621c67b69c167805cb1f2ab8f1708a5c490d1b52fe/aiohttp-3.13.5-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:3b13560160d07e047a93f23aaa30718606493036253d5430887514715b67c9d9", size = 1772072, upload-time = "2026-03-31T22:00:07.494Z" }, + { url = "https://files.pythonhosted.org/packages/7e/df/57ba7f0c4a553fc2bd8b6321df236870ec6fd64a2a473a8a13d4f733214e/aiohttp-3.13.5-cp314-cp314t-win32.whl", hash = "sha256:9a0f4474b6ea6818b41f82172d799e4b3d29e22c2c520ce4357856fced9af2f8", size = 471819, upload-time = "2026-03-31T22:00:10.277Z" }, + { url = "https://files.pythonhosted.org/packages/62/29/2f8418269e46454a26171bfdd6a055d74febf32234e474930f2f60a17145/aiohttp-3.13.5-cp314-cp314t-win_amd64.whl", hash = "sha256:18a2f6c1182c51baa1d28d68fea51513cb2a76612f038853c0ad3c145423d3d9", size = 505441, upload-time = "2026-03-31T22:00:12.791Z" }, +] + +[[package]] +name = "aiosignal" +version = "1.4.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "frozenlist" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/61/62/06741b579156360248d1ec624842ad0edf697050bbaf7c3e46394e106ad1/aiosignal-1.4.0.tar.gz", hash = "sha256:f47eecd9468083c2029cc99945502cb7708b082c232f9aca65da147157b251c7", size = 25007, upload-time = "2025-07-03T22:54:43.528Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/fb/76/641ae371508676492379f16e2fa48f4e2c11741bd63c48be4b12a6b09cba/aiosignal-1.4.0-py3-none-any.whl", hash = "sha256:053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e", size = 7490, upload-time = "2025-07-03T22:54:42.156Z" }, +] + +[[package]] +name = "annotated-doc" +version = "0.0.4" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/57/ba/046ceea27344560984e26a590f90bc7f4a75b06701f653222458922b558c/annotated_doc-0.0.4.tar.gz", hash = "sha256:fbcda96e87e9c92ad167c2e53839e57503ecfda18804ea28102353485033faa4", size = 7288, upload-time = "2025-11-10T22:07:42.062Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/1e/d3/26bf1008eb3d2daa8ef4cacc7f3bfdc11818d111f7e2d0201bc6e3b49d45/annotated_doc-0.0.4-py3-none-any.whl", hash = "sha256:571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320", size = 5303, upload-time = "2025-11-10T22:07:40.673Z" }, +] + +[[package]] +name = "annotated-types" +version = "0.7.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time = "2024-05-20T21:33:25.928Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" }, +] + +[[package]] +name = "anyio" +version = "4.13.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "idna" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/19/14/2c5dd9f512b66549ae92767a9c7b330ae88e1932ca57876909410251fe13/anyio-4.13.0.tar.gz", hash = "sha256:334b70e641fd2221c1505b3890c69882fe4a2df910cba14d97019b90b24439dc", size = 231622, upload-time = "2026-03-24T12:59:09.671Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/da/42/e921fccf5015463e32a3cf6ee7f980a6ed0f395ceeaa45060b61d86486c2/anyio-4.13.0-py3-none-any.whl", hash = "sha256:08b310f9e24a9594186fd75b4f73f4a4152069e3853f1ed8bfbf58369f4ad708", size = 114353, upload-time = "2026-03-24T12:59:08.246Z" }, +] + +[[package]] +name = "appnope" +version = "0.1.4" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/35/5d/752690df9ef5b76e169e68d6a129fa6d08a7100ca7f754c89495db3c6019/appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee", size = 4170, upload-time = "2024-02-06T09:43:11.258Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/81/29/5ecc3a15d5a33e31b26c11426c45c501e439cb865d0bff96315d86443b78/appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c", size = 4321, upload-time = "2024-02-06T09:43:09.663Z" }, +] + +[[package]] +name = "argon2-cffi" +version = "25.1.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "argon2-cffi-bindings" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/0e/89/ce5af8a7d472a67cc819d5d998aa8c82c5d860608c4db9f46f1162d7dab9/argon2_cffi-25.1.0.tar.gz", hash = "sha256:694ae5cc8a42f4c4e2bf2ca0e64e51e23a040c6a517a85074683d3959e1346c1", size = 45706, upload-time = "2025-06-03T06:55:32.073Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/4f/d3/a8b22fa575b297cd6e3e3b0155c7e25db170edf1c74783d6a31a2490b8d9/argon2_cffi-25.1.0-py3-none-any.whl", hash = "sha256:fdc8b074db390fccb6eb4a3604ae7231f219aa669a2652e0f20e16ba513d5741", size = 14657, upload-time = "2025-06-03T06:55:30.804Z" }, +] + +[[package]] +name = "argon2-cffi-bindings" +version = "25.1.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "cffi" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/5c/2d/db8af0df73c1cf454f71b2bbe5e356b8c1f8041c979f505b3d3186e520a9/argon2_cffi_bindings-25.1.0.tar.gz", hash = "sha256:b957f3e6ea4d55d820e40ff76f450952807013d361a65d7f28acc0acbf29229d", size = 1783441, upload-time = "2025-07-30T10:02:05.147Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/60/97/3c0a35f46e52108d4707c44b95cfe2afcafc50800b5450c197454569b776/argon2_cffi_bindings-25.1.0-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:3d3f05610594151994ca9ccb3c771115bdb4daef161976a266f0dd8aa9996b8f", size = 54393, upload-time = "2025-07-30T10:01:40.97Z" }, + { url = "https://files.pythonhosted.org/packages/9d/f4/98bbd6ee89febd4f212696f13c03ca302b8552e7dbf9c8efa11ea4a388c3/argon2_cffi_bindings-25.1.0-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:8b8efee945193e667a396cbc7b4fb7d357297d6234d30a489905d96caabde56b", size = 29328, upload-time = "2025-07-30T10:01:41.916Z" }, + { url = "https://files.pythonhosted.org/packages/43/24/90a01c0ef12ac91a6be05969f29944643bc1e5e461155ae6559befa8f00b/argon2_cffi_bindings-25.1.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:3c6702abc36bf3ccba3f802b799505def420a1b7039862014a65db3205967f5a", size = 31269, upload-time = "2025-07-30T10:01:42.716Z" }, + { url = "https://files.pythonhosted.org/packages/d4/d3/942aa10782b2697eee7af5e12eeff5ebb325ccfb86dd8abda54174e377e4/argon2_cffi_bindings-25.1.0-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a1c70058c6ab1e352304ac7e3b52554daadacd8d453c1752e547c76e9c99ac44", size = 86558, upload-time = "2025-07-30T10:01:43.943Z" }, + { url = "https://files.pythonhosted.org/packages/0d/82/b484f702fec5536e71836fc2dbc8c5267b3f6e78d2d539b4eaa6f0db8bf8/argon2_cffi_bindings-25.1.0-cp314-cp314t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e2fd3bfbff3c5d74fef31a722f729bf93500910db650c925c2d6ef879a7e51cb", size = 92364, upload-time = "2025-07-30T10:01:44.887Z" }, + { url = "https://files.pythonhosted.org/packages/c9/c1/a606ff83b3f1735f3759ad0f2cd9e038a0ad11a3de3b6c673aa41c24bb7b/argon2_cffi_bindings-25.1.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:c4f9665de60b1b0e99bcd6be4f17d90339698ce954cfd8d9cf4f91c995165a92", size = 85637, upload-time = "2025-07-30T10:01:46.225Z" }, + { url = "https://files.pythonhosted.org/packages/44/b4/678503f12aceb0262f84fa201f6027ed77d71c5019ae03b399b97caa2f19/argon2_cffi_bindings-25.1.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:ba92837e4a9aa6a508c8d2d7883ed5a8f6c308c89a4790e1e447a220deb79a85", size = 91934, upload-time = "2025-07-30T10:01:47.203Z" }, + { url = "https://files.pythonhosted.org/packages/f0/c7/f36bd08ef9bd9f0a9cff9428406651f5937ce27b6c5b07b92d41f91ae541/argon2_cffi_bindings-25.1.0-cp314-cp314t-win32.whl", hash = "sha256:84a461d4d84ae1295871329b346a97f68eade8c53b6ed9a7ca2d7467f3c8ff6f", size = 28158, upload-time = "2025-07-30T10:01:48.341Z" }, + { url = "https://files.pythonhosted.org/packages/b3/80/0106a7448abb24a2c467bf7d527fe5413b7fdfa4ad6d6a96a43a62ef3988/argon2_cffi_bindings-25.1.0-cp314-cp314t-win_amd64.whl", hash = "sha256:b55aec3565b65f56455eebc9b9f34130440404f27fe21c3b375bf1ea4d8fbae6", size = 32597, upload-time = "2025-07-30T10:01:49.112Z" }, + { url = "https://files.pythonhosted.org/packages/05/b8/d663c9caea07e9180b2cb662772865230715cbd573ba3b5e81793d580316/argon2_cffi_bindings-25.1.0-cp314-cp314t-win_arm64.whl", hash = "sha256:87c33a52407e4c41f3b70a9c2d3f6056d88b10dad7695be708c5021673f55623", size = 28231, upload-time = "2025-07-30T10:01:49.92Z" }, + { url = "https://files.pythonhosted.org/packages/1d/57/96b8b9f93166147826da5f90376e784a10582dd39a393c99bb62cfcf52f0/argon2_cffi_bindings-25.1.0-cp39-abi3-macosx_10_9_universal2.whl", hash = "sha256:aecba1723ae35330a008418a91ea6cfcedf6d31e5fbaa056a166462ff066d500", size = 54121, upload-time = "2025-07-30T10:01:50.815Z" }, + { url = "https://files.pythonhosted.org/packages/0a/08/a9bebdb2e0e602dde230bdde8021b29f71f7841bd54801bcfd514acb5dcf/argon2_cffi_bindings-25.1.0-cp39-abi3-macosx_10_9_x86_64.whl", hash = "sha256:2630b6240b495dfab90aebe159ff784d08ea999aa4b0d17efa734055a07d2f44", size = 29177, upload-time = "2025-07-30T10:01:51.681Z" }, + { url = "https://files.pythonhosted.org/packages/b6/02/d297943bcacf05e4f2a94ab6f462831dc20158614e5d067c35d4e63b9acb/argon2_cffi_bindings-25.1.0-cp39-abi3-macosx_11_0_arm64.whl", hash = "sha256:7aef0c91e2c0fbca6fc68e7555aa60ef7008a739cbe045541e438373bc54d2b0", size = 31090, upload-time = "2025-07-30T10:01:53.184Z" }, + { url = "https://files.pythonhosted.org/packages/c1/93/44365f3d75053e53893ec6d733e4a5e3147502663554b4d864587c7828a7/argon2_cffi_bindings-25.1.0-cp39-abi3-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1e021e87faa76ae0d413b619fe2b65ab9a037f24c60a1e6cc43457ae20de6dc6", size = 81246, upload-time = "2025-07-30T10:01:54.145Z" }, + { url = "https://files.pythonhosted.org/packages/09/52/94108adfdd6e2ddf58be64f959a0b9c7d4ef2fa71086c38356d22dc501ea/argon2_cffi_bindings-25.1.0-cp39-abi3-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d3e924cfc503018a714f94a49a149fdc0b644eaead5d1f089330399134fa028a", size = 87126, upload-time = "2025-07-30T10:01:55.074Z" }, + { url = "https://files.pythonhosted.org/packages/72/70/7a2993a12b0ffa2a9271259b79cc616e2389ed1a4d93842fac5a1f923ffd/argon2_cffi_bindings-25.1.0-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:c87b72589133f0346a1cb8d5ecca4b933e3c9b64656c9d175270a000e73b288d", size = 80343, upload-time = "2025-07-30T10:01:56.007Z" }, + { url = "https://files.pythonhosted.org/packages/78/9a/4e5157d893ffc712b74dbd868c7f62365618266982b64accab26bab01edc/argon2_cffi_bindings-25.1.0-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:1db89609c06afa1a214a69a462ea741cf735b29a57530478c06eb81dd403de99", size = 86777, upload-time = "2025-07-30T10:01:56.943Z" }, + { url = "https://files.pythonhosted.org/packages/74/cd/15777dfde1c29d96de7f18edf4cc94c385646852e7c7b0320aa91ccca583/argon2_cffi_bindings-25.1.0-cp39-abi3-win32.whl", hash = "sha256:473bcb5f82924b1becbb637b63303ec8d10e84c8d241119419897a26116515d2", size = 27180, upload-time = "2025-07-30T10:01:57.759Z" }, + { url = "https://files.pythonhosted.org/packages/e2/c6/a759ece8f1829d1f162261226fbfd2c6832b3ff7657384045286d2afa384/argon2_cffi_bindings-25.1.0-cp39-abi3-win_amd64.whl", hash = "sha256:a98cd7d17e9f7ce244c0803cad3c23a7d379c301ba618a5fa76a67d116618b98", size = 31715, upload-time = "2025-07-30T10:01:58.56Z" }, + { url = "https://files.pythonhosted.org/packages/42/b9/f8d6fa329ab25128b7e98fd83a3cb34d9db5b059a9847eddb840a0af45dd/argon2_cffi_bindings-25.1.0-cp39-abi3-win_arm64.whl", hash = "sha256:b0fdbcf513833809c882823f98dc2f931cf659d9a1429616ac3adebb49f5db94", size = 27149, upload-time = "2025-07-30T10:01:59.329Z" }, +] + +[[package]] +name = "arrow" +version = "1.4.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "python-dateutil" }, + { name = "tzdata" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/b9/33/032cdc44182491aa708d06a68b62434140d8c50820a087fac7af37703357/arrow-1.4.0.tar.gz", hash = "sha256:ed0cc050e98001b8779e84d461b0098c4ac597e88704a655582b21d116e526d7", size = 152931, upload-time = "2025-10-18T17:46:46.761Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ed/c9/d7977eaacb9df673210491da99e6a247e93df98c715fc43fd136ce1d3d33/arrow-1.4.0-py3-none-any.whl", hash = "sha256:749f0769958ebdc79c173ff0b0670d59051a535fa26e8eba02953dc19eb43205", size = 68797, upload-time = "2025-10-18T17:46:45.663Z" }, +] + +[[package]] +name = "asttokens" +version = "3.0.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/be/a5/8e3f9b6771b0b408517c82d97aed8f2036509bc247d46114925e32fe33f0/asttokens-3.0.1.tar.gz", hash = "sha256:71a4ee5de0bde6a31d64f6b13f2293ac190344478f081c3d1bccfcf5eacb0cb7", size = 62308, upload-time = "2025-11-15T16:43:48.578Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/d2/39/e7eaf1799466a4aef85b6a4fe7bd175ad2b1c6345066aa33f1f58d4b18d0/asttokens-3.0.1-py3-none-any.whl", hash = "sha256:15a3ebc0f43c2d0a50eeafea25e19046c68398e487b9f1f5b517f7c0f40f976a", size = 27047, upload-time = "2025-11-15T16:43:16.109Z" }, +] + +[[package]] +name = "async-lru" +version = "2.3.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/e8/1f/989ecfef8e64109a489fff357450cb73fa73a865a92bd8c272170a6922c2/async_lru-2.3.0.tar.gz", hash = "sha256:89bdb258a0140d7313cf8f4031d816a042202faa61d0ab310a0a538baa1c24b6", size = 16332, upload-time = "2026-03-19T01:04:32.413Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e5/e2/c2e3abf398f80732e58b03be77bde9022550d221dd8781bf586bd4d97cc1/async_lru-2.3.0-py3-none-any.whl", hash = "sha256:eea27b01841909316f2cc739807acea1c623df2be8c5cfad7583286397bb8315", size = 8403, upload-time = "2026-03-19T01:04:30.883Z" }, +] + +[[package]] +name = "attrs" +version = "26.1.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/9a/8e/82a0fe20a541c03148528be8cac2408564a6c9a0cc7e9171802bc1d26985/attrs-26.1.0.tar.gz", hash = "sha256:d03ceb89cb322a8fd706d4fb91940737b6642aa36998fe130a9bc96c985eff32", size = 952055, upload-time = "2026-03-19T14:22:25.026Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/64/b4/17d4b0b2a2dc85a6df63d1157e028ed19f90d4cd97c36717afef2bc2f395/attrs-26.1.0-py3-none-any.whl", hash = "sha256:c647aa4a12dfbad9333ca4e71fe62ddc36f4e63b2d260a37a8b83d2f043ac309", size = 67548, upload-time = "2026-03-19T14:22:23.645Z" }, +] + +[[package]] +name = "babel" +version = "2.18.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/7d/b2/51899539b6ceeeb420d40ed3cd4b7a40519404f9baf3d4ac99dc413a834b/babel-2.18.0.tar.gz", hash = "sha256:b80b99a14bd085fcacfa15c9165f651fbb3406e66cc603abf11c5750937c992d", size = 9959554, upload-time = "2026-02-01T12:30:56.078Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/77/f5/21d2de20e8b8b0408f0681956ca2c69f1320a3848ac50e6e7f39c6159675/babel-2.18.0-py3-none-any.whl", hash = "sha256:e2b422b277c2b9a9630c1d7903c2a00d0830c409c59ac8cae9081c92f1aeba35", size = 10196845, upload-time = "2026-02-01T12:30:53.445Z" }, +] + +[[package]] +name = "beautifulsoup4" +version = "4.14.3" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "soupsieve" }, + { name = "typing-extensions" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/c3/b0/1c6a16426d389813b48d95e26898aff79abbde42ad353958ad95cc8c9b21/beautifulsoup4-4.14.3.tar.gz", hash = "sha256:6292b1c5186d356bba669ef9f7f051757099565ad9ada5dd630bd9de5fa7fb86", size = 627737, upload-time = "2025-11-30T15:08:26.084Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/1a/39/47f9197bdd44df24d67ac8893641e16f386c984a0619ef2ee4c51fbbc019/beautifulsoup4-4.14.3-py3-none-any.whl", hash = "sha256:0918bfe44902e6ad8d57732ba310582e98da931428d231a5ecb9e7c703a735bb", size = 107721, upload-time = "2025-11-30T15:08:24.087Z" }, +] + +[[package]] +name = "bleach" +version = "6.3.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "webencodings" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/07/18/3c8523962314be6bf4c8989c79ad9531c825210dd13a8669f6b84336e8bd/bleach-6.3.0.tar.gz", hash = "sha256:6f3b91b1c0a02bb9a78b5a454c92506aa0fdf197e1d5e114d2e00c6f64306d22", size = 203533, upload-time = "2025-10-27T17:57:39.211Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/cd/3a/577b549de0cc09d95f11087ee63c739bba856cd3952697eec4c4bb91350a/bleach-6.3.0-py3-none-any.whl", hash = "sha256:fe10ec77c93ddf3d13a73b035abaac7a9f5e436513864ccdad516693213c65d6", size = 164437, upload-time = "2025-10-27T17:57:37.538Z" }, +] + +[package.optional-dependencies] +css = [ + { name = "tinycss2" }, +] + +[[package]] +name = "certifi" +version = "2026.4.22" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/25/ee/6caf7a40c36a1220410afe15a1cc64993a1f864871f698c0f93acb72842a/certifi-2026.4.22.tar.gz", hash = "sha256:8d455352a37b71bf76a79caa83a3d6c25afee4a385d632127b6afb3963f1c580", size = 137077, upload-time = "2026-04-22T11:26:11.191Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/22/30/7cd8fdcdfbc5b869528b079bfb76dcdf6056b1a2097a662e5e8c04f42965/certifi-2026.4.22-py3-none-any.whl", hash = "sha256:3cb2210c8f88ba2318d29b0388d1023c8492ff72ecdde4ebdaddbb13a31b1c4a", size = 135707, upload-time = "2026-04-22T11:26:09.372Z" }, +] + +[[package]] +name = "cffi" +version = "2.0.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "pycparser", marker = "implementation_name != 'PyPy'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/eb/56/b1ba7935a17738ae8453301356628e8147c79dbb825bcbc73dc7401f9846/cffi-2.0.0.tar.gz", hash = "sha256:44d1b5909021139fe36001ae048dbdde8214afa20200eda0f64c068cac5d5529", size = 523588, upload-time = "2025-09-08T23:24:04.541Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/4b/8d/a0a47a0c9e413a658623d014e91e74a50cdd2c423f7ccfd44086ef767f90/cffi-2.0.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:00bdf7acc5f795150faa6957054fbbca2439db2f775ce831222b66f192f03beb", size = 185230, upload-time = "2025-09-08T23:23:00.879Z" }, + { url = "https://files.pythonhosted.org/packages/4a/d2/a6c0296814556c68ee32009d9c2ad4f85f2707cdecfd7727951ec228005d/cffi-2.0.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:45d5e886156860dc35862657e1494b9bae8dfa63bf56796f2fb56e1679fc0bca", size = 181043, upload-time = "2025-09-08T23:23:02.231Z" }, + { url = "https://files.pythonhosted.org/packages/b0/1e/d22cc63332bd59b06481ceaac49d6c507598642e2230f201649058a7e704/cffi-2.0.0-cp313-cp313-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:07b271772c100085dd28b74fa0cd81c8fb1a3ba18b21e03d7c27f3436a10606b", size = 212446, upload-time = "2025-09-08T23:23:03.472Z" }, + { url = "https://files.pythonhosted.org/packages/a9/f5/a2c23eb03b61a0b8747f211eb716446c826ad66818ddc7810cc2cc19b3f2/cffi-2.0.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d48a880098c96020b02d5a1f7d9251308510ce8858940e6fa99ece33f610838b", size = 220101, upload-time = "2025-09-08T23:23:04.792Z" }, + { url = "https://files.pythonhosted.org/packages/f2/7f/e6647792fc5850d634695bc0e6ab4111ae88e89981d35ac269956605feba/cffi-2.0.0-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:f93fd8e5c8c0a4aa1f424d6173f14a892044054871c771f8566e4008eaa359d2", size = 207948, upload-time = "2025-09-08T23:23:06.127Z" }, + { url = "https://files.pythonhosted.org/packages/cb/1e/a5a1bd6f1fb30f22573f76533de12a00bf274abcdc55c8edab639078abb6/cffi-2.0.0-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:dd4f05f54a52fb558f1ba9f528228066954fee3ebe629fc1660d874d040ae5a3", size = 206422, upload-time = "2025-09-08T23:23:07.753Z" }, + { url = "https://files.pythonhosted.org/packages/98/df/0a1755e750013a2081e863e7cd37e0cdd02664372c754e5560099eb7aa44/cffi-2.0.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:c8d3b5532fc71b7a77c09192b4a5a200ea992702734a2e9279a37f2478236f26", size = 219499, upload-time = "2025-09-08T23:23:09.648Z" }, + { url = "https://files.pythonhosted.org/packages/50/e1/a969e687fcf9ea58e6e2a928ad5e2dd88cc12f6f0ab477e9971f2309b57c/cffi-2.0.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:d9b29c1f0ae438d5ee9acb31cadee00a58c46cc9c0b2f9038c6b0b3470877a8c", size = 222928, upload-time = "2025-09-08T23:23:10.928Z" }, + { url = "https://files.pythonhosted.org/packages/36/54/0362578dd2c9e557a28ac77698ed67323ed5b9775ca9d3fe73fe191bb5d8/cffi-2.0.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:6d50360be4546678fc1b79ffe7a66265e28667840010348dd69a314145807a1b", size = 221302, upload-time = "2025-09-08T23:23:12.42Z" }, + { url = "https://files.pythonhosted.org/packages/eb/6d/bf9bda840d5f1dfdbf0feca87fbdb64a918a69bca42cfa0ba7b137c48cb8/cffi-2.0.0-cp313-cp313-win32.whl", hash = "sha256:74a03b9698e198d47562765773b4a8309919089150a0bb17d829ad7b44b60d27", size = 172909, upload-time = "2025-09-08T23:23:14.32Z" }, + { url = "https://files.pythonhosted.org/packages/37/18/6519e1ee6f5a1e579e04b9ddb6f1676c17368a7aba48299c3759bbc3c8b3/cffi-2.0.0-cp313-cp313-win_amd64.whl", hash = "sha256:19f705ada2530c1167abacb171925dd886168931e0a7b78f5bffcae5c6b5be75", size = 183402, upload-time = "2025-09-08T23:23:15.535Z" }, + { url = "https://files.pythonhosted.org/packages/cb/0e/02ceeec9a7d6ee63bb596121c2c8e9b3a9e150936f4fbef6ca1943e6137c/cffi-2.0.0-cp313-cp313-win_arm64.whl", hash = "sha256:256f80b80ca3853f90c21b23ee78cd008713787b1b1e93eae9f3d6a7134abd91", size = 177780, upload-time = "2025-09-08T23:23:16.761Z" }, + { url = "https://files.pythonhosted.org/packages/92/c4/3ce07396253a83250ee98564f8d7e9789fab8e58858f35d07a9a2c78de9f/cffi-2.0.0-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:fc33c5141b55ed366cfaad382df24fe7dcbc686de5be719b207bb248e3053dc5", size = 185320, upload-time = "2025-09-08T23:23:18.087Z" }, + { url = "https://files.pythonhosted.org/packages/59/dd/27e9fa567a23931c838c6b02d0764611c62290062a6d4e8ff7863daf9730/cffi-2.0.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:c654de545946e0db659b3400168c9ad31b5d29593291482c43e3564effbcee13", size = 181487, upload-time = "2025-09-08T23:23:19.622Z" }, + { url = "https://files.pythonhosted.org/packages/d6/43/0e822876f87ea8a4ef95442c3d766a06a51fc5298823f884ef87aaad168c/cffi-2.0.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:24b6f81f1983e6df8db3adc38562c83f7d4a0c36162885ec7f7b77c7dcbec97b", size = 220049, upload-time = "2025-09-08T23:23:20.853Z" }, + { url = "https://files.pythonhosted.org/packages/b4/89/76799151d9c2d2d1ead63c2429da9ea9d7aac304603de0c6e8764e6e8e70/cffi-2.0.0-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:12873ca6cb9b0f0d3a0da705d6086fe911591737a59f28b7936bdfed27c0d47c", size = 207793, upload-time = "2025-09-08T23:23:22.08Z" }, + { url = "https://files.pythonhosted.org/packages/bb/dd/3465b14bb9e24ee24cb88c9e3730f6de63111fffe513492bf8c808a3547e/cffi-2.0.0-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:d9b97165e8aed9272a6bb17c01e3cc5871a594a446ebedc996e2397a1c1ea8ef", size = 206300, upload-time = "2025-09-08T23:23:23.314Z" }, + { url = "https://files.pythonhosted.org/packages/47/d9/d83e293854571c877a92da46fdec39158f8d7e68da75bf73581225d28e90/cffi-2.0.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:afb8db5439b81cf9c9d0c80404b60c3cc9c3add93e114dcae767f1477cb53775", size = 219244, upload-time = "2025-09-08T23:23:24.541Z" }, + { url = "https://files.pythonhosted.org/packages/2b/0f/1f177e3683aead2bb00f7679a16451d302c436b5cbf2505f0ea8146ef59e/cffi-2.0.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:737fe7d37e1a1bffe70bd5754ea763a62a066dc5913ca57e957824b72a85e205", size = 222828, upload-time = "2025-09-08T23:23:26.143Z" }, + { url = "https://files.pythonhosted.org/packages/c6/0f/cafacebd4b040e3119dcb32fed8bdef8dfe94da653155f9d0b9dc660166e/cffi-2.0.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:38100abb9d1b1435bc4cc340bb4489635dc2f0da7456590877030c9b3d40b0c1", size = 220926, upload-time = "2025-09-08T23:23:27.873Z" }, + { url = "https://files.pythonhosted.org/packages/3e/aa/df335faa45b395396fcbc03de2dfcab242cd61a9900e914fe682a59170b1/cffi-2.0.0-cp314-cp314-win32.whl", hash = "sha256:087067fa8953339c723661eda6b54bc98c5625757ea62e95eb4898ad5e776e9f", size = 175328, upload-time = "2025-09-08T23:23:44.61Z" }, + { url = "https://files.pythonhosted.org/packages/bb/92/882c2d30831744296ce713f0feb4c1cd30f346ef747b530b5318715cc367/cffi-2.0.0-cp314-cp314-win_amd64.whl", hash = "sha256:203a48d1fb583fc7d78a4c6655692963b860a417c0528492a6bc21f1aaefab25", size = 185650, upload-time = "2025-09-08T23:23:45.848Z" }, + { url = "https://files.pythonhosted.org/packages/9f/2c/98ece204b9d35a7366b5b2c6539c350313ca13932143e79dc133ba757104/cffi-2.0.0-cp314-cp314-win_arm64.whl", hash = "sha256:dbd5c7a25a7cb98f5ca55d258b103a2054f859a46ae11aaf23134f9cc0d356ad", size = 180687, upload-time = "2025-09-08T23:23:47.105Z" }, + { url = "https://files.pythonhosted.org/packages/3e/61/c768e4d548bfa607abcda77423448df8c471f25dbe64fb2ef6d555eae006/cffi-2.0.0-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:9a67fc9e8eb39039280526379fb3a70023d77caec1852002b4da7e8b270c4dd9", size = 188773, upload-time = "2025-09-08T23:23:29.347Z" }, + { url = "https://files.pythonhosted.org/packages/2c/ea/5f76bce7cf6fcd0ab1a1058b5af899bfbef198bea4d5686da88471ea0336/cffi-2.0.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:7a66c7204d8869299919db4d5069a82f1561581af12b11b3c9f48c584eb8743d", size = 185013, upload-time = "2025-09-08T23:23:30.63Z" }, + { url = "https://files.pythonhosted.org/packages/be/b4/c56878d0d1755cf9caa54ba71e5d049479c52f9e4afc230f06822162ab2f/cffi-2.0.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:7cc09976e8b56f8cebd752f7113ad07752461f48a58cbba644139015ac24954c", size = 221593, upload-time = "2025-09-08T23:23:31.91Z" }, + { url = "https://files.pythonhosted.org/packages/e0/0d/eb704606dfe8033e7128df5e90fee946bbcb64a04fcdaa97321309004000/cffi-2.0.0-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:92b68146a71df78564e4ef48af17551a5ddd142e5190cdf2c5624d0c3ff5b2e8", size = 209354, upload-time = "2025-09-08T23:23:33.214Z" }, + { url = "https://files.pythonhosted.org/packages/d8/19/3c435d727b368ca475fb8742ab97c9cb13a0de600ce86f62eab7fa3eea60/cffi-2.0.0-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:b1e74d11748e7e98e2f426ab176d4ed720a64412b6a15054378afdb71e0f37dc", size = 208480, upload-time = "2025-09-08T23:23:34.495Z" }, + { url = "https://files.pythonhosted.org/packages/d0/44/681604464ed9541673e486521497406fadcc15b5217c3e326b061696899a/cffi-2.0.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:28a3a209b96630bca57cce802da70c266eb08c6e97e5afd61a75611ee6c64592", size = 221584, upload-time = "2025-09-08T23:23:36.096Z" }, + { url = "https://files.pythonhosted.org/packages/25/8e/342a504ff018a2825d395d44d63a767dd8ebc927ebda557fecdaca3ac33a/cffi-2.0.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:7553fb2090d71822f02c629afe6042c299edf91ba1bf94951165613553984512", size = 224443, upload-time = "2025-09-08T23:23:37.328Z" }, + { url = "https://files.pythonhosted.org/packages/e1/5e/b666bacbbc60fbf415ba9988324a132c9a7a0448a9a8f125074671c0f2c3/cffi-2.0.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:6c6c373cfc5c83a975506110d17457138c8c63016b563cc9ed6e056a82f13ce4", size = 223437, upload-time = "2025-09-08T23:23:38.945Z" }, + { url = "https://files.pythonhosted.org/packages/a0/1d/ec1a60bd1a10daa292d3cd6bb0b359a81607154fb8165f3ec95fe003b85c/cffi-2.0.0-cp314-cp314t-win32.whl", hash = "sha256:1fc9ea04857caf665289b7a75923f2c6ed559b8298a1b8c49e59f7dd95c8481e", size = 180487, upload-time = "2025-09-08T23:23:40.423Z" }, + { url = "https://files.pythonhosted.org/packages/bf/41/4c1168c74fac325c0c8156f04b6749c8b6a8f405bbf91413ba088359f60d/cffi-2.0.0-cp314-cp314t-win_amd64.whl", hash = "sha256:d68b6cef7827e8641e8ef16f4494edda8b36104d79773a334beaa1e3521430f6", size = 191726, upload-time = "2025-09-08T23:23:41.742Z" }, + { url = "https://files.pythonhosted.org/packages/ae/3a/dbeec9d1ee0844c679f6bb5d6ad4e9f198b1224f4e7a32825f47f6192b0c/cffi-2.0.0-cp314-cp314t-win_arm64.whl", hash = "sha256:0a1527a803f0a659de1af2e1fd700213caba79377e27e4693648c2923da066f9", size = 184195, upload-time = "2025-09-08T23:23:43.004Z" }, +] + +[[package]] +name = "charset-normalizer" +version = "3.4.7" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/e7/a1/67fe25fac3c7642725500a3f6cfe5821ad557c3abb11c9d20d12c7008d3e/charset_normalizer-3.4.7.tar.gz", hash = "sha256:ae89db9e5f98a11a4bf50407d4363e7b09b31e55bc117b4f7d80aab97ba009e5", size = 144271, upload-time = "2026-04-02T09:28:39.342Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/c1/3b/66777e39d3ae1ddc77ee606be4ec6d8cbd4c801f65e5a1b6f2b11b8346dd/charset_normalizer-3.4.7-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:f496c9c3cc02230093d8330875c4c3cdfc3b73612a5fd921c65d39cbcef08063", size = 309627, upload-time = "2026-04-02T09:26:45.198Z" }, + { url = "https://files.pythonhosted.org/packages/2e/4e/b7f84e617b4854ade48a1b7915c8ccfadeba444d2a18c291f696e37f0d3b/charset_normalizer-3.4.7-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0ea948db76d31190bf08bd371623927ee1339d5f2a0b4b1b4a4439a65298703c", size = 207008, upload-time = "2026-04-02T09:26:46.824Z" }, + { url = "https://files.pythonhosted.org/packages/c4/bb/ec73c0257c9e11b268f018f068f5d00aa0ef8c8b09f7753ebd5f2880e248/charset_normalizer-3.4.7-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a277ab8928b9f299723bc1a2dabb1265911b1a76341f90a510368ca44ad9ab66", size = 228303, upload-time = "2026-04-02T09:26:48.397Z" }, + { url = "https://files.pythonhosted.org/packages/85/fb/32d1f5033484494619f701e719429c69b766bfc4dbc61aa9e9c8c166528b/charset_normalizer-3.4.7-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:3bec022aec2c514d9cf199522a802bd007cd588ab17ab2525f20f9c34d067c18", size = 224282, upload-time = "2026-04-02T09:26:49.684Z" }, + { url = "https://files.pythonhosted.org/packages/fa/07/330e3a0dda4c404d6da83b327270906e9654a24f6c546dc886a0eb0ffb23/charset_normalizer-3.4.7-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e044c39e41b92c845bc815e5ae4230804e8e7bc29e399b0437d64222d92809dd", size = 215595, upload-time = "2026-04-02T09:26:50.915Z" }, + { url = "https://files.pythonhosted.org/packages/e3/7c/fc890655786e423f02556e0216d4b8c6bcb6bdfa890160dc66bf52dee468/charset_normalizer-3.4.7-cp313-cp313-manylinux_2_31_armv7l.whl", hash = "sha256:f495a1652cf3fbab2eb0639776dad966c2fb874d79d87ca07f9d5f059b8bd215", size = 201986, upload-time = "2026-04-02T09:26:52.197Z" }, + { url = "https://files.pythonhosted.org/packages/d8/97/bfb18b3db2aed3b90cf54dc292ad79fdd5ad65c4eae454099475cbeadd0d/charset_normalizer-3.4.7-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:e712b419df8ba5e42b226c510472b37bd57b38e897d3eca5e8cfd410a29fa859", size = 211711, upload-time = "2026-04-02T09:26:53.49Z" }, + { url = "https://files.pythonhosted.org/packages/6f/a5/a581c13798546a7fd557c82614a5c65a13df2157e9ad6373166d2a3e645d/charset_normalizer-3.4.7-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:7804338df6fcc08105c7745f1502ba68d900f45fd770d5bdd5288ddccb8a42d8", size = 210036, upload-time = "2026-04-02T09:26:54.975Z" }, + { url = "https://files.pythonhosted.org/packages/8c/bf/b3ab5bcb478e4193d517644b0fb2bf5497fbceeaa7a1bc0f4d5b50953861/charset_normalizer-3.4.7-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:481551899c856c704d58119b5025793fa6730adda3571971af568f66d2424bb5", size = 202998, upload-time = "2026-04-02T09:26:56.303Z" }, + { url = "https://files.pythonhosted.org/packages/e7/4e/23efd79b65d314fa320ec6017b4b5834d5c12a58ba4610aa353af2e2f577/charset_normalizer-3.4.7-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:f59099f9b66f0d7145115e6f80dd8b1d847176df89b234a5a6b3f00437aa0832", size = 230056, upload-time = "2026-04-02T09:26:57.554Z" }, + { url = "https://files.pythonhosted.org/packages/b9/9f/1e1941bc3f0e01df116e68dc37a55c4d249df5e6fa77f008841aef68264f/charset_normalizer-3.4.7-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:f59ad4c0e8f6bba240a9bb85504faa1ab438237199d4cce5f622761507b8f6a6", size = 211537, upload-time = "2026-04-02T09:26:58.843Z" }, + { url = "https://files.pythonhosted.org/packages/80/0f/088cbb3020d44428964a6c97fe1edfb1b9550396bf6d278330281e8b709c/charset_normalizer-3.4.7-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:3dedcc22d73ec993f42055eff4fcfed9318d1eeb9a6606c55892a26964964e48", size = 226176, upload-time = "2026-04-02T09:27:00.437Z" }, + { url = "https://files.pythonhosted.org/packages/6a/9f/130394f9bbe06f4f63e22641d32fc9b202b7e251c9aef4db044324dac493/charset_normalizer-3.4.7-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:64f02c6841d7d83f832cd97ccf8eb8a906d06eb95d5276069175c696b024b60a", size = 217723, upload-time = "2026-04-02T09:27:02.021Z" }, + { url = "https://files.pythonhosted.org/packages/73/55/c469897448a06e49f8fa03f6caae97074fde823f432a98f979cc42b90e69/charset_normalizer-3.4.7-cp313-cp313-win32.whl", hash = "sha256:4042d5c8f957e15221d423ba781e85d553722fc4113f523f2feb7b188cc34c5e", size = 148085, upload-time = "2026-04-02T09:27:03.192Z" }, + { url = "https://files.pythonhosted.org/packages/5d/78/1b74c5bbb3f99b77a1715c91b3e0b5bdb6fe302d95ace4f5b1bec37b0167/charset_normalizer-3.4.7-cp313-cp313-win_amd64.whl", hash = "sha256:3946fa46a0cf3e4c8cb1cc52f56bb536310d34f25f01ca9b6c16afa767dab110", size = 158819, upload-time = "2026-04-02T09:27:04.454Z" }, + { url = "https://files.pythonhosted.org/packages/68/86/46bd42279d323deb8687c4a5a811fd548cb7d1de10cf6535d099877a9a9f/charset_normalizer-3.4.7-cp313-cp313-win_arm64.whl", hash = "sha256:80d04837f55fc81da168b98de4f4b797ef007fc8a79ab71c6ec9bc4dd662b15b", size = 147915, upload-time = "2026-04-02T09:27:05.971Z" }, + { url = "https://files.pythonhosted.org/packages/97/c8/c67cb8c70e19ef1960b97b22ed2a1567711de46c4ddf19799923adc836c2/charset_normalizer-3.4.7-cp314-cp314-macosx_10_15_universal2.whl", hash = "sha256:c36c333c39be2dbca264d7803333c896ab8fa7d4d6f0ab7edb7dfd7aea6e98c0", size = 309234, upload-time = "2026-04-02T09:27:07.194Z" }, + { url = "https://files.pythonhosted.org/packages/99/85/c091fdee33f20de70d6c8b522743b6f831a2f1cd3ff86de4c6a827c48a76/charset_normalizer-3.4.7-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1c2aed2e5e41f24ea8ef1590b8e848a79b56f3a5564a65ceec43c9d692dc7d8a", size = 208042, upload-time = "2026-04-02T09:27:08.749Z" }, + { url = "https://files.pythonhosted.org/packages/87/1c/ab2ce611b984d2fd5d86a5a8a19c1ae26acac6bad967da4967562c75114d/charset_normalizer-3.4.7-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:54523e136b8948060c0fa0bc7b1b50c32c186f2fceee897a495406bb6e311d2b", size = 228706, upload-time = "2026-04-02T09:27:09.951Z" }, + { url = "https://files.pythonhosted.org/packages/a8/29/2b1d2cb00bf085f59d29eb773ce58ec2d325430f8c216804a0a5cd83cbca/charset_normalizer-3.4.7-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:715479b9a2802ecac752a3b0efa2b0b60285cf962ee38414211abdfccc233b41", size = 224727, upload-time = "2026-04-02T09:27:11.175Z" }, + { url = "https://files.pythonhosted.org/packages/47/5c/032c2d5a07fe4d4855fea851209cca2b6f03ebeb6d4e3afdb3358386a684/charset_normalizer-3.4.7-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bd6c2a1c7573c64738d716488d2cdd3c00e340e4835707d8fdb8dc1a66ef164e", size = 215882, upload-time = "2026-04-02T09:27:12.446Z" }, + { url = "https://files.pythonhosted.org/packages/2c/c2/356065d5a8b78ed04499cae5f339f091946a6a74f91e03476c33f0ab7100/charset_normalizer-3.4.7-cp314-cp314-manylinux_2_31_armv7l.whl", hash = "sha256:c45e9440fb78f8ddabcf714b68f936737a121355bf59f3907f4e17721b9d1aae", size = 200860, upload-time = "2026-04-02T09:27:13.721Z" }, + { url = "https://files.pythonhosted.org/packages/0c/cd/a32a84217ced5039f53b29f460962abb2d4420def55afabe45b1c3c7483d/charset_normalizer-3.4.7-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:3534e7dcbdcf757da6b85a0bbf5b6868786d5982dd959b065e65481644817a18", size = 211564, upload-time = "2026-04-02T09:27:15.272Z" }, + { url = "https://files.pythonhosted.org/packages/44/86/58e6f13ce26cc3b8f4a36b94a0f22ae2f00a72534520f4ae6857c4b81f89/charset_normalizer-3.4.7-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:e8ac484bf18ce6975760921bb6148041faa8fef0547200386ea0b52b5d27bf7b", size = 211276, upload-time = "2026-04-02T09:27:16.834Z" }, + { url = "https://files.pythonhosted.org/packages/8f/fe/d17c32dc72e17e155e06883efa84514ca375f8a528ba2546bee73fc4df81/charset_normalizer-3.4.7-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:a5fe03b42827c13cdccd08e6c0247b6a6d4b5e3cdc53fd1749f5896adcdc2356", size = 201238, upload-time = "2026-04-02T09:27:18.229Z" }, + { url = "https://files.pythonhosted.org/packages/6a/29/f33daa50b06525a237451cdb6c69da366c381a3dadcd833fa5676bc468b3/charset_normalizer-3.4.7-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:2d6eb928e13016cea4f1f21d1e10c1cebd5a421bc57ddf5b1142ae3f86824fab", size = 230189, upload-time = "2026-04-02T09:27:19.445Z" }, + { url = "https://files.pythonhosted.org/packages/b6/6e/52c84015394a6a0bdcd435210a7e944c5f94ea1055f5cc5d56c5fe368e7b/charset_normalizer-3.4.7-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:e74327fb75de8986940def6e8dee4f127cc9752bee7355bb323cc5b2659b6d46", size = 211352, upload-time = "2026-04-02T09:27:20.79Z" }, + { url = "https://files.pythonhosted.org/packages/8c/d7/4353be581b373033fb9198bf1da3cf8f09c1082561e8e922aa7b39bf9fe8/charset_normalizer-3.4.7-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:d6038d37043bced98a66e68d3aa2b6a35505dc01328cd65217cefe82f25def44", size = 227024, upload-time = "2026-04-02T09:27:22.063Z" }, + { url = "https://files.pythonhosted.org/packages/30/45/99d18aa925bd1740098ccd3060e238e21115fffbfdcb8f3ece837d0ace6c/charset_normalizer-3.4.7-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:7579e913a5339fb8fa133f6bbcfd8e6749696206cf05acdbdca71a1b436d8e72", size = 217869, upload-time = "2026-04-02T09:27:23.486Z" }, + { url = "https://files.pythonhosted.org/packages/5c/05/5ee478aa53f4bb7996482153d4bfe1b89e0f087f0ab6b294fcf92d595873/charset_normalizer-3.4.7-cp314-cp314-win32.whl", hash = "sha256:5b77459df20e08151cd6f8b9ef8ef1f961ef73d85c21a555c7eed5b79410ec10", size = 148541, upload-time = "2026-04-02T09:27:25.146Z" }, + { url = "https://files.pythonhosted.org/packages/48/77/72dcb0921b2ce86420b2d79d454c7022bf5be40202a2a07906b9f2a35c97/charset_normalizer-3.4.7-cp314-cp314-win_amd64.whl", hash = "sha256:92a0a01ead5e668468e952e4238cccd7c537364eb7d851ab144ab6627dbbe12f", size = 159634, upload-time = "2026-04-02T09:27:26.642Z" }, + { url = "https://files.pythonhosted.org/packages/c6/a3/c2369911cd72f02386e4e340770f6e158c7980267da16af8f668217abaa0/charset_normalizer-3.4.7-cp314-cp314-win_arm64.whl", hash = "sha256:67f6279d125ca0046a7fd386d01b311c6363844deac3e5b069b514ba3e63c246", size = 148384, upload-time = "2026-04-02T09:27:28.271Z" }, + { url = "https://files.pythonhosted.org/packages/94/09/7e8a7f73d24dba1f0035fbbf014d2c36828fc1bf9c88f84093e57d315935/charset_normalizer-3.4.7-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:effc3f449787117233702311a1b7d8f59cba9ced946ba727bdc329ec69028e24", size = 330133, upload-time = "2026-04-02T09:27:29.474Z" }, + { url = "https://files.pythonhosted.org/packages/8d/da/96975ddb11f8e977f706f45cddd8540fd8242f71ecdb5d18a80723dcf62c/charset_normalizer-3.4.7-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:fbccdc05410c9ee21bbf16a35f4c1d16123dcdeb8a1d38f33654fa21d0234f79", size = 216257, upload-time = "2026-04-02T09:27:30.793Z" }, + { url = "https://files.pythonhosted.org/packages/e5/e8/1d63bf8ef2d388e95c64b2098f45f84758f6d102a087552da1485912637b/charset_normalizer-3.4.7-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:733784b6d6def852c814bce5f318d25da2ee65dd4839a0718641c696e09a2960", size = 234851, upload-time = "2026-04-02T09:27:32.44Z" }, + { url = "https://files.pythonhosted.org/packages/9b/40/e5ff04233e70da2681fa43969ad6f66ca5611d7e669be0246c4c7aaf6dc8/charset_normalizer-3.4.7-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a89c23ef8d2c6b27fd200a42aa4ac72786e7c60d40efdc76e6011260b6e949c4", size = 233393, upload-time = "2026-04-02T09:27:34.03Z" }, + { url = "https://files.pythonhosted.org/packages/be/c1/06c6c49d5a5450f76899992f1ee40b41d076aee9279b49cf9974d2f313d5/charset_normalizer-3.4.7-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6c114670c45346afedc0d947faf3c7f701051d2518b943679c8ff88befe14f8e", size = 223251, upload-time = "2026-04-02T09:27:35.369Z" }, + { url = "https://files.pythonhosted.org/packages/2b/9f/f2ff16fb050946169e3e1f82134d107e5d4ae72647ec8a1b1446c148480f/charset_normalizer-3.4.7-cp314-cp314t-manylinux_2_31_armv7l.whl", hash = "sha256:a180c5e59792af262bf263b21a3c49353f25945d8d9f70628e73de370d55e1e1", size = 206609, upload-time = "2026-04-02T09:27:36.661Z" }, + { url = "https://files.pythonhosted.org/packages/69/d5/a527c0cd8d64d2eab7459784fb4169a0ac76e5a6fc5237337982fd61347e/charset_normalizer-3.4.7-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:3c9a494bc5ec77d43cea229c4f6db1e4d8fe7e1bbffa8b6f0f0032430ff8ab44", size = 220014, upload-time = "2026-04-02T09:27:38.019Z" }, + { url = "https://files.pythonhosted.org/packages/7e/80/8a7b8104a3e203074dc9aa2c613d4b726c0e136bad1cc734594b02867972/charset_normalizer-3.4.7-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:8d828b6667a32a728a1ad1d93957cdf37489c57b97ae6c4de2860fa749b8fc1e", size = 218979, upload-time = "2026-04-02T09:27:39.37Z" }, + { url = "https://files.pythonhosted.org/packages/02/9a/b759b503d507f375b2b5c153e4d2ee0a75aa215b7f2489cf314f4541f2c0/charset_normalizer-3.4.7-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:cf1493cd8607bec4d8a7b9b004e699fcf8f9103a9284cc94962cb73d20f9d4a3", size = 209238, upload-time = "2026-04-02T09:27:40.722Z" }, + { url = "https://files.pythonhosted.org/packages/c2/4e/0f3f5d47b86bdb79256e7290b26ac847a2832d9a4033f7eb2cd4bcf4bb5b/charset_normalizer-3.4.7-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:0c96c3b819b5c3e9e165495db84d41914d6894d55181d2d108cc1a69bfc9cce0", size = 236110, upload-time = "2026-04-02T09:27:42.33Z" }, + { url = "https://files.pythonhosted.org/packages/96/23/bce28734eb3ed2c91dcf93abeb8a5cf393a7b2749725030bb630e554fdd8/charset_normalizer-3.4.7-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:752a45dc4a6934060b3b0dab47e04edc3326575f82be64bc4fc293914566503e", size = 219824, upload-time = "2026-04-02T09:27:43.924Z" }, + { url = "https://files.pythonhosted.org/packages/2c/6f/6e897c6984cc4d41af319b077f2f600fc8214eb2fe2d6bcb79141b882400/charset_normalizer-3.4.7-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:8778f0c7a52e56f75d12dae53ae320fae900a8b9b4164b981b9c5ce059cd1fcb", size = 233103, upload-time = "2026-04-02T09:27:45.348Z" }, + { url = "https://files.pythonhosted.org/packages/76/22/ef7bd0fe480a0ae9b656189ec00744b60933f68b4f42a7bb06589f6f576a/charset_normalizer-3.4.7-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:ce3412fbe1e31eb81ea42f4169ed94861c56e643189e1e75f0041f3fe7020abe", size = 225194, upload-time = "2026-04-02T09:27:46.706Z" }, + { url = "https://files.pythonhosted.org/packages/c5/a7/0e0ab3e0b5bc1219bd80a6a0d4d72ca74d9250cb2382b7c699c147e06017/charset_normalizer-3.4.7-cp314-cp314t-win32.whl", hash = "sha256:c03a41a8784091e67a39648f70c5f97b5b6a37f216896d44d2cdcb82615339a0", size = 159827, upload-time = "2026-04-02T09:27:48.053Z" }, + { url = "https://files.pythonhosted.org/packages/7a/1d/29d32e0fb40864b1f878c7f5a0b343ae676c6e2b271a2d55cc3a152391da/charset_normalizer-3.4.7-cp314-cp314t-win_amd64.whl", hash = "sha256:03853ed82eeebbce3c2abfdbc98c96dc205f32a79627688ac9a27370ea61a49c", size = 174168, upload-time = "2026-04-02T09:27:49.795Z" }, + { url = "https://files.pythonhosted.org/packages/de/32/d92444ad05c7a6e41fb2036749777c163baf7a0301a040cb672d6b2b1ae9/charset_normalizer-3.4.7-cp314-cp314t-win_arm64.whl", hash = "sha256:c35abb8bfff0185efac5878da64c45dafd2b37fb0383add1be155a763c1f083d", size = 153018, upload-time = "2026-04-02T09:27:51.116Z" }, + { url = "https://files.pythonhosted.org/packages/db/8f/61959034484a4a7c527811f4721e75d02d653a35afb0b6054474d8185d4c/charset_normalizer-3.4.7-py3-none-any.whl", hash = "sha256:3dce51d0f5e7951f8bb4900c257dad282f49190fdbebecd4ba99bcc41fef404d", size = 61958, upload-time = "2026-04-02T09:28:37.794Z" }, +] + +[[package]] +name = "click" +version = "8.3.3" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "colorama", marker = "sys_platform == 'win32'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/bb/63/f9e1ea081ce35720d8b92acde70daaedace594dc93b693c869e0d5910718/click-8.3.3.tar.gz", hash = "sha256:398329ad4837b2ff7cbe1dd166a4c0f8900c3ca3a218de04466f38f6497f18a2", size = 328061, upload-time = "2026-04-22T15:11:27.506Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ae/44/c1221527f6a71a01ec6fbad7fa78f1d50dfa02217385cf0fa3eec7087d59/click-8.3.3-py3-none-any.whl", hash = "sha256:a2bf429bb3033c89fa4936ffb35d5cb471e3719e1f3c8a7c3fff0b8314305613", size = 110502, upload-time = "2026-04-22T15:11:25.044Z" }, +] + +[[package]] +name = "colorama" +version = "0.4.6" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/d8/53/6f443c9a4a8358a93a6792e2acffb9d9d5cb0a5cfd8802644b7b1c9a02e4/colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44", size = 27697, upload-time = "2022-10-25T02:36:22.414Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" }, +] + +[[package]] +name = "comm" +version = "0.2.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/4c/13/7d740c5849255756bc17888787313b61fd38a0a8304fc4f073dfc46122aa/comm-0.2.3.tar.gz", hash = "sha256:2dc8048c10962d55d7ad693be1e7045d891b7ce8d999c97963a5e3e99c055971", size = 6319, upload-time = "2025-07-25T14:02:04.452Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/60/97/891a0971e1e4a8c5d2b20bbe0e524dc04548d2307fee33cdeba148fd4fc7/comm-0.2.3-py3-none-any.whl", hash = "sha256:c615d91d75f7f04f095b30d1c1711babd43bdc6419c1be9886a85f2f4e489417", size = 7294, upload-time = "2025-07-25T14:02:02.896Z" }, +] + +[[package]] +name = "contourpy" +version = "1.3.3" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "numpy" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/58/01/1253e6698a07380cd31a736d248a3f2a50a7c88779a1813da27503cadc2a/contourpy-1.3.3.tar.gz", hash = "sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880", size = 13466174, upload-time = "2025-07-26T12:03:12.549Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/68/35/0167aad910bbdb9599272bd96d01a9ec6852f36b9455cf2ca67bd4cc2d23/contourpy-1.3.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:177fb367556747a686509d6fef71d221a4b198a3905fe824430e5ea0fda54eb5", size = 293257, upload-time = "2025-07-26T12:01:39.367Z" }, + { url = "https://files.pythonhosted.org/packages/96/e4/7adcd9c8362745b2210728f209bfbcf7d91ba868a2c5f40d8b58f54c509b/contourpy-1.3.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:d002b6f00d73d69333dac9d0b8d5e84d9724ff9ef044fd63c5986e62b7c9e1b1", size = 274034, upload-time = "2025-07-26T12:01:40.645Z" }, + { url = "https://files.pythonhosted.org/packages/73/23/90e31ceeed1de63058a02cb04b12f2de4b40e3bef5e082a7c18d9c8ae281/contourpy-1.3.3-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:348ac1f5d4f1d66d3322420f01d42e43122f43616e0f194fc1c9f5d830c5b286", size = 334672, upload-time = "2025-07-26T12:01:41.942Z" }, + { url = "https://files.pythonhosted.org/packages/ed/93/b43d8acbe67392e659e1d984700e79eb67e2acb2bd7f62012b583a7f1b55/contourpy-1.3.3-cp313-cp313-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:655456777ff65c2c548b7c454af9c6f33f16c8884f11083244b5819cc214f1b5", size = 381234, upload-time = "2025-07-26T12:01:43.499Z" }, + { url = "https://files.pythonhosted.org/packages/46/3b/bec82a3ea06f66711520f75a40c8fc0b113b2a75edb36aa633eb11c4f50f/contourpy-1.3.3-cp313-cp313-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:644a6853d15b2512d67881586bd03f462c7ab755db95f16f14d7e238f2852c67", size = 385169, upload-time = "2025-07-26T12:01:45.219Z" }, + { url = "https://files.pythonhosted.org/packages/4b/32/e0f13a1c5b0f8572d0ec6ae2f6c677b7991fafd95da523159c19eff0696a/contourpy-1.3.3-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4debd64f124ca62069f313a9cb86656ff087786016d76927ae2cf37846b006c9", size = 362859, upload-time = "2025-07-26T12:01:46.519Z" }, + { url = "https://files.pythonhosted.org/packages/33/71/e2a7945b7de4e58af42d708a219f3b2f4cff7386e6b6ab0a0fa0033c49a9/contourpy-1.3.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a15459b0f4615b00bbd1e91f1b9e19b7e63aea7483d03d804186f278c0af2659", size = 1332062, upload-time = "2025-07-26T12:01:48.964Z" }, + { url = "https://files.pythonhosted.org/packages/12/fc/4e87ac754220ccc0e807284f88e943d6d43b43843614f0a8afa469801db0/contourpy-1.3.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:ca0fdcd73925568ca027e0b17ab07aad764be4706d0a925b89227e447d9737b7", size = 1403932, upload-time = "2025-07-26T12:01:51.979Z" }, + { url = "https://files.pythonhosted.org/packages/a6/2e/adc197a37443f934594112222ac1aa7dc9a98faf9c3842884df9a9d8751d/contourpy-1.3.3-cp313-cp313-win32.whl", hash = "sha256:b20c7c9a3bf701366556e1b1984ed2d0cedf999903c51311417cf5f591d8c78d", size = 185024, upload-time = "2025-07-26T12:01:53.245Z" }, + { url = "https://files.pythonhosted.org/packages/18/0b/0098c214843213759692cc638fce7de5c289200a830e5035d1791d7a2338/contourpy-1.3.3-cp313-cp313-win_amd64.whl", hash = "sha256:1cadd8b8969f060ba45ed7c1b714fe69185812ab43bd6b86a9123fe8f99c3263", size = 226578, upload-time = "2025-07-26T12:01:54.422Z" }, + { url = "https://files.pythonhosted.org/packages/8a/9a/2f6024a0c5995243cd63afdeb3651c984f0d2bc727fd98066d40e141ad73/contourpy-1.3.3-cp313-cp313-win_arm64.whl", hash = "sha256:fd914713266421b7536de2bfa8181aa8c699432b6763a0ea64195ebe28bff6a9", size = 193524, upload-time = "2025-07-26T12:01:55.73Z" }, + { url = "https://files.pythonhosted.org/packages/c0/b3/f8a1a86bd3298513f500e5b1f5fd92b69896449f6cab6a146a5d52715479/contourpy-1.3.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:88df9880d507169449d434c293467418b9f6cbe82edd19284aa0409e7fdb933d", size = 306730, upload-time = "2025-07-26T12:01:57.051Z" }, + { url = "https://files.pythonhosted.org/packages/3f/11/4780db94ae62fc0c2053909b65dc3246bd7cecfc4f8a20d957ad43aa4ad8/contourpy-1.3.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:d06bb1f751ba5d417047db62bca3c8fde202b8c11fb50742ab3ab962c81e8216", size = 287897, upload-time = "2025-07-26T12:01:58.663Z" }, + { url = "https://files.pythonhosted.org/packages/ae/15/e59f5f3ffdd6f3d4daa3e47114c53daabcb18574a26c21f03dc9e4e42ff0/contourpy-1.3.3-cp313-cp313t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e4e6b05a45525357e382909a4c1600444e2a45b4795163d3b22669285591c1ae", size = 326751, upload-time = "2025-07-26T12:02:00.343Z" }, + { url = "https://files.pythonhosted.org/packages/0f/81/03b45cfad088e4770b1dcf72ea78d3802d04200009fb364d18a493857210/contourpy-1.3.3-cp313-cp313t-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ab3074b48c4e2cf1a960e6bbeb7f04566bf36b1861d5c9d4d8ac04b82e38ba20", size = 375486, upload-time = "2025-07-26T12:02:02.128Z" }, + { url = "https://files.pythonhosted.org/packages/0c/ba/49923366492ffbdd4486e970d421b289a670ae8cf539c1ea9a09822b371a/contourpy-1.3.3-cp313-cp313t-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:6c3d53c796f8647d6deb1abe867daeb66dcc8a97e8455efa729516b997b8ed99", size = 388106, upload-time = "2025-07-26T12:02:03.615Z" }, + { url = "https://files.pythonhosted.org/packages/9f/52/5b00ea89525f8f143651f9f03a0df371d3cbd2fccd21ca9b768c7a6500c2/contourpy-1.3.3-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:50ed930df7289ff2a8d7afeb9603f8289e5704755c7e5c3bbd929c90c817164b", size = 352548, upload-time = "2025-07-26T12:02:05.165Z" }, + { url = "https://files.pythonhosted.org/packages/32/1d/a209ec1a3a3452d490f6b14dd92e72280c99ae3d1e73da74f8277d4ee08f/contourpy-1.3.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:4feffb6537d64b84877da813a5c30f1422ea5739566abf0bd18065ac040e120a", size = 1322297, upload-time = "2025-07-26T12:02:07.379Z" }, + { url = "https://files.pythonhosted.org/packages/bc/9e/46f0e8ebdd884ca0e8877e46a3f4e633f6c9c8c4f3f6e72be3fe075994aa/contourpy-1.3.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:2b7e9480ffe2b0cd2e787e4df64270e3a0440d9db8dc823312e2c940c167df7e", size = 1391023, upload-time = "2025-07-26T12:02:10.171Z" }, + { url = "https://files.pythonhosted.org/packages/b9/70/f308384a3ae9cd2209e0849f33c913f658d3326900d0ff5d378d6a1422d2/contourpy-1.3.3-cp313-cp313t-win32.whl", hash = "sha256:283edd842a01e3dcd435b1c5116798d661378d83d36d337b8dde1d16a5fc9ba3", size = 196157, upload-time = "2025-07-26T12:02:11.488Z" }, + { url = "https://files.pythonhosted.org/packages/b2/dd/880f890a6663b84d9e34a6f88cded89d78f0091e0045a284427cb6b18521/contourpy-1.3.3-cp313-cp313t-win_amd64.whl", hash = "sha256:87acf5963fc2b34825e5b6b048f40e3635dd547f590b04d2ab317c2619ef7ae8", size = 240570, upload-time = "2025-07-26T12:02:12.754Z" }, + { url = "https://files.pythonhosted.org/packages/80/99/2adc7d8ffead633234817ef8e9a87115c8a11927a94478f6bb3d3f4d4f7d/contourpy-1.3.3-cp313-cp313t-win_arm64.whl", hash = "sha256:3c30273eb2a55024ff31ba7d052dde990d7d8e5450f4bbb6e913558b3d6c2301", size = 199713, upload-time = "2025-07-26T12:02:14.4Z" }, + { url = "https://files.pythonhosted.org/packages/72/8b/4546f3ab60f78c514ffb7d01a0bd743f90de36f0019d1be84d0a708a580a/contourpy-1.3.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:fde6c716d51c04b1c25d0b90364d0be954624a0ee9d60e23e850e8d48353d07a", size = 292189, upload-time = "2025-07-26T12:02:16.095Z" }, + { url = "https://files.pythonhosted.org/packages/fd/e1/3542a9cb596cadd76fcef413f19c79216e002623158befe6daa03dbfa88c/contourpy-1.3.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:cbedb772ed74ff5be440fa8eee9bd49f64f6e3fc09436d9c7d8f1c287b121d77", size = 273251, upload-time = "2025-07-26T12:02:17.524Z" }, + { url = "https://files.pythonhosted.org/packages/b1/71/f93e1e9471d189f79d0ce2497007731c1e6bf9ef6d1d61b911430c3db4e5/contourpy-1.3.3-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:22e9b1bd7a9b1d652cd77388465dc358dafcd2e217d35552424aa4f996f524f5", size = 335810, upload-time = "2025-07-26T12:02:18.9Z" }, + { url = "https://files.pythonhosted.org/packages/91/f9/e35f4c1c93f9275d4e38681a80506b5510e9327350c51f8d4a5a724d178c/contourpy-1.3.3-cp314-cp314-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a22738912262aa3e254e4f3cb079a95a67132fc5a063890e224393596902f5a4", size = 382871, upload-time = "2025-07-26T12:02:20.418Z" }, + { url = "https://files.pythonhosted.org/packages/b5/71/47b512f936f66a0a900d81c396a7e60d73419868fba959c61efed7a8ab46/contourpy-1.3.3-cp314-cp314-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:afe5a512f31ee6bd7d0dda52ec9864c984ca3d66664444f2d72e0dc4eb832e36", size = 386264, upload-time = "2025-07-26T12:02:21.916Z" }, + { url = "https://files.pythonhosted.org/packages/04/5f/9ff93450ba96b09c7c2b3f81c94de31c89f92292f1380261bd7195bea4ea/contourpy-1.3.3-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f64836de09927cba6f79dcd00fdd7d5329f3fccc633468507079c829ca4db4e3", size = 363819, upload-time = "2025-07-26T12:02:23.759Z" }, + { url = "https://files.pythonhosted.org/packages/3e/a6/0b185d4cc480ee494945cde102cb0149ae830b5fa17bf855b95f2e70ad13/contourpy-1.3.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:1fd43c3be4c8e5fd6e4f2baeae35ae18176cf2e5cced681cca908addf1cdd53b", size = 1333650, upload-time = "2025-07-26T12:02:26.181Z" }, + { url = "https://files.pythonhosted.org/packages/43/d7/afdc95580ca56f30fbcd3060250f66cedbde69b4547028863abd8aa3b47e/contourpy-1.3.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:6afc576f7b33cf00996e5c1102dc2a8f7cc89e39c0b55df93a0b78c1bd992b36", size = 1404833, upload-time = "2025-07-26T12:02:28.782Z" }, + { url = "https://files.pythonhosted.org/packages/e2/e2/366af18a6d386f41132a48f033cbd2102e9b0cf6345d35ff0826cd984566/contourpy-1.3.3-cp314-cp314-win32.whl", hash = "sha256:66c8a43a4f7b8df8b71ee1840e4211a3c8d93b214b213f590e18a1beca458f7d", size = 189692, upload-time = "2025-07-26T12:02:30.128Z" }, + { url = "https://files.pythonhosted.org/packages/7d/c2/57f54b03d0f22d4044b8afb9ca0e184f8b1afd57b4f735c2fa70883dc601/contourpy-1.3.3-cp314-cp314-win_amd64.whl", hash = "sha256:cf9022ef053f2694e31d630feaacb21ea24224be1c3ad0520b13d844274614fd", size = 232424, upload-time = "2025-07-26T12:02:31.395Z" }, + { url = "https://files.pythonhosted.org/packages/18/79/a9416650df9b525737ab521aa181ccc42d56016d2123ddcb7b58e926a42c/contourpy-1.3.3-cp314-cp314-win_arm64.whl", hash = "sha256:95b181891b4c71de4bb404c6621e7e2390745f887f2a026b2d99e92c17892339", size = 198300, upload-time = "2025-07-26T12:02:32.956Z" }, + { url = "https://files.pythonhosted.org/packages/1f/42/38c159a7d0f2b7b9c04c64ab317042bb6952b713ba875c1681529a2932fe/contourpy-1.3.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:33c82d0138c0a062380332c861387650c82e4cf1747aaa6938b9b6516762e772", size = 306769, upload-time = "2025-07-26T12:02:34.2Z" }, + { url = "https://files.pythonhosted.org/packages/c3/6c/26a8205f24bca10974e77460de68d3d7c63e282e23782f1239f226fcae6f/contourpy-1.3.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:ea37e7b45949df430fe649e5de8351c423430046a2af20b1c1961cae3afcda77", size = 287892, upload-time = "2025-07-26T12:02:35.807Z" }, + { url = "https://files.pythonhosted.org/packages/66/06/8a475c8ab718ebfd7925661747dbb3c3ee9c82ac834ccb3570be49d129f4/contourpy-1.3.3-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d304906ecc71672e9c89e87c4675dc5c2645e1f4269a5063b99b0bb29f232d13", size = 326748, upload-time = "2025-07-26T12:02:37.193Z" }, + { url = "https://files.pythonhosted.org/packages/b4/a3/c5ca9f010a44c223f098fccd8b158bb1cb287378a31ac141f04730dc49be/contourpy-1.3.3-cp314-cp314t-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ca658cd1a680a5c9ea96dc61cdbae1e85c8f25849843aa799dfd3cb370ad4fbe", size = 375554, upload-time = "2025-07-26T12:02:38.894Z" }, + { url = "https://files.pythonhosted.org/packages/80/5b/68bd33ae63fac658a4145088c1e894405e07584a316738710b636c6d0333/contourpy-1.3.3-cp314-cp314t-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:ab2fd90904c503739a75b7c8c5c01160130ba67944a7b77bbf36ef8054576e7f", size = 388118, upload-time = "2025-07-26T12:02:40.642Z" }, + { url = "https://files.pythonhosted.org/packages/40/52/4c285a6435940ae25d7410a6c36bda5145839bc3f0beb20c707cda18b9d2/contourpy-1.3.3-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b7301b89040075c30e5768810bc96a8e8d78085b47d8be6e4c3f5a0b4ed478a0", size = 352555, upload-time = "2025-07-26T12:02:42.25Z" }, + { url = "https://files.pythonhosted.org/packages/24/ee/3e81e1dd174f5c7fefe50e85d0892de05ca4e26ef1c9a59c2a57e43b865a/contourpy-1.3.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:2a2a8b627d5cc6b7c41a4beff6c5ad5eb848c88255fda4a8745f7e901b32d8e4", size = 1322295, upload-time = "2025-07-26T12:02:44.668Z" }, + { url = "https://files.pythonhosted.org/packages/3c/b2/6d913d4d04e14379de429057cd169e5e00f6c2af3bb13e1710bcbdb5da12/contourpy-1.3.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:fd6ec6be509c787f1caf6b247f0b1ca598bef13f4ddeaa126b7658215529ba0f", size = 1391027, upload-time = "2025-07-26T12:02:47.09Z" }, + { url = "https://files.pythonhosted.org/packages/93/8a/68a4ec5c55a2971213d29a9374913f7e9f18581945a7a31d1a39b5d2dfe5/contourpy-1.3.3-cp314-cp314t-win32.whl", hash = "sha256:e74a9a0f5e3fff48fb5a7f2fd2b9b70a3fe014a67522f79b7cca4c0c7e43c9ae", size = 202428, upload-time = "2025-07-26T12:02:48.691Z" }, + { url = "https://files.pythonhosted.org/packages/fa/96/fd9f641ffedc4fa3ace923af73b9d07e869496c9cc7a459103e6e978992f/contourpy-1.3.3-cp314-cp314t-win_amd64.whl", hash = "sha256:13b68d6a62db8eafaebb8039218921399baf6e47bf85006fd8529f2a08ef33fc", size = 250331, upload-time = "2025-07-26T12:02:50.137Z" }, + { url = "https://files.pythonhosted.org/packages/ae/8c/469afb6465b853afff216f9528ffda78a915ff880ed58813ba4faf4ba0b6/contourpy-1.3.3-cp314-cp314t-win_arm64.whl", hash = "sha256:b7448cb5a725bb1e35ce88771b86fba35ef418952474492cf7c764059933ff8b", size = 203831, upload-time = "2025-07-26T12:02:51.449Z" }, +] + +[[package]] +name = "cryptography" +version = "47.0.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "cffi", marker = "platform_python_implementation != 'PyPy'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/ef/b2/7ffa7fe8207a8c42147ffe70c3e360b228160c1d85dc3faff16aaa3244c0/cryptography-47.0.0.tar.gz", hash = "sha256:9f8e55fe4e63613a5e1cc5819030f27b97742d720203a087802ce4ce9ceb52bb", size = 830863, upload-time = "2026-04-24T19:54:57.056Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/a4/98/40dfe932134bdcae4f6ab5927c87488754bf9eb79297d7e0070b78dd58e9/cryptography-47.0.0-cp311-abi3-macosx_10_9_universal2.whl", hash = "sha256:160ad728f128972d362e714054f6ba0067cab7fb350c5202a9ae8ae4ce3ef1a0", size = 7912214, upload-time = "2026-04-24T19:53:03.864Z" }, + { url = "https://files.pythonhosted.org/packages/34/c6/2733531243fba725f58611b918056b277692f1033373dcc8bd01af1c05d4/cryptography-47.0.0-cp311-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:b9a8943e359b7615db1a3ba587994618e094ff3d6fa5a390c73d079ce18b3973", size = 4644617, upload-time = "2026-04-24T19:53:06.909Z" }, + { url = "https://files.pythonhosted.org/packages/00/e3/b27be1a670a9b87f855d211cf0e1174a5d721216b7616bd52d8581d912ed/cryptography-47.0.0-cp311-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:f5c15764f261394b22aef6b00252f5195f46f2ca300bec57149474e2538b31f8", size = 4668186, upload-time = "2026-04-24T19:53:09.053Z" }, + { url = "https://files.pythonhosted.org/packages/81/b9/8443cfe5d17d482d348cee7048acf502bb89a51b6382f06240fd290d4ca3/cryptography-47.0.0-cp311-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:9c59ab0e0fa3a180a5a9c59f3a5abe3ef90d474bc56d7fadfbe80359491b615b", size = 4651244, upload-time = "2026-04-24T19:53:11.217Z" }, + { url = "https://files.pythonhosted.org/packages/5d/5e/13ed0cdd0eb88ba159d6dd5ebfece8cb901dbcf1ae5ac4072e28b55d3153/cryptography-47.0.0-cp311-abi3-manylinux_2_28_ppc64le.whl", hash = "sha256:34b4358b925a5ea3e14384ca781a2c0ef7ac219b57bb9eacc4457078e2b19f92", size = 5252906, upload-time = "2026-04-24T19:53:13.532Z" }, + { url = "https://files.pythonhosted.org/packages/64/16/ed058e1df0f33d440217cd120d41d5dda9dd215a80b8187f68483185af82/cryptography-47.0.0-cp311-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:0024b87d47ae2399165a6bfb20d24888881eeab83ae2566d62467c5ff0030ce7", size = 4701842, upload-time = "2026-04-24T19:53:15.618Z" }, + { url = "https://files.pythonhosted.org/packages/02/e0/3d30986b30fdbd9e969abbdf8ba00ed0618615144341faeb57f395a084fe/cryptography-47.0.0-cp311-abi3-manylinux_2_31_armv7l.whl", hash = "sha256:1e47422b5557bb82d3fff997e8d92cff4e28b9789576984f08c248d2b3535d93", size = 4289313, upload-time = "2026-04-24T19:53:17.755Z" }, + { url = "https://files.pythonhosted.org/packages/df/fd/32db38e3ad0cb331f0691cb4c7a8a6f176f679124dee746b3af6633db4d9/cryptography-47.0.0-cp311-abi3-manylinux_2_34_aarch64.whl", hash = "sha256:6f29f36582e6151d9686235e586dd35bb67491f024767d10b842e520dc6a07ac", size = 4650964, upload-time = "2026-04-24T19:53:20.062Z" }, + { url = "https://files.pythonhosted.org/packages/86/53/5395d944dfd48cb1f67917f533c609c34347185ef15eb4308024c876f274/cryptography-47.0.0-cp311-abi3-manylinux_2_34_ppc64le.whl", hash = "sha256:a9b761f012a943b7de0e828843c5688d0de94a0578d44d6c85a1bae32f87791f", size = 5207817, upload-time = "2026-04-24T19:53:22.498Z" }, + { url = "https://files.pythonhosted.org/packages/34/4f/e5711b28e1901f7d480a2b1b688b645aa4c77c73f10731ed17e7f7db3f0d/cryptography-47.0.0-cp311-abi3-manylinux_2_34_x86_64.whl", hash = "sha256:4e1de79e047e25d6e9f8cea71c86b4a53aced64134f0f003bbcbf3655fd172c8", size = 4701544, upload-time = "2026-04-24T19:53:24.356Z" }, + { url = "https://files.pythonhosted.org/packages/22/22/c8ddc25de3010fc8da447648f5a092c40e7a8fadf01dd6d255d9c0b9373d/cryptography-47.0.0-cp311-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:ef6b3634087f18d2155b1e8ce264e5345a753da2c5fa9815e7d41315c90f8318", size = 4783536, upload-time = "2026-04-24T19:53:26.665Z" }, + { url = "https://files.pythonhosted.org/packages/66/b6/d4a68f4ea999c6d89e8498579cba1c5fcba4276284de7773b17e4fa69293/cryptography-47.0.0-cp311-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:11dbb9f50a0f1bb9757b3d8c27c1101780efb8f0bdecfb12439c22a74d64c001", size = 4926106, upload-time = "2026-04-24T19:53:28.686Z" }, + { url = "https://files.pythonhosted.org/packages/54/ed/5f524db1fade9c013aa618e1c99c6ed05e8ffc9ceee6cda22fed22dda3f4/cryptography-47.0.0-cp311-abi3-win32.whl", hash = "sha256:7fda2f02c9015db3f42bb8a22324a454516ed10a8c29ca6ece6cdbb5efe2a203", size = 3258581, upload-time = "2026-04-24T19:53:31.058Z" }, + { url = "https://files.pythonhosted.org/packages/b2/dc/1b901990b174786569029f67542b3edf72ac068b6c3c8683c17e6a2f5363/cryptography-47.0.0-cp311-abi3-win_amd64.whl", hash = "sha256:f5c3296dab66202f1b18a91fa266be93d6aa0c2806ea3d67762c69f60adc71aa", size = 3775309, upload-time = "2026-04-24T19:53:33.054Z" }, + { url = "https://files.pythonhosted.org/packages/14/88/7aa18ad9c11bc87689affa5ce4368d884b517502d75739d475fc6f4a03c7/cryptography-47.0.0-cp314-cp314t-macosx_10_9_universal2.whl", hash = "sha256:be12cb6a204f77ed968bcefe68086eb061695b540a3dd05edac507a3111b25f0", size = 7904299, upload-time = "2026-04-24T19:53:35.003Z" }, + { url = "https://files.pythonhosted.org/packages/07/55/c18f75724544872f234678fdedc871391722cb34a2aee19faa9f63100bb2/cryptography-47.0.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:2ebd84adf0728c039a3be2700289378e1c164afc6748df1a5ed456767bef9ba7", size = 4631180, upload-time = "2026-04-24T19:53:37.517Z" }, + { url = "https://files.pythonhosted.org/packages/ee/65/31a5cc0eaca99cec5bafffe155d407115d96136bb161e8b49e0ef73f09a7/cryptography-47.0.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:7f68d6fbc7fbbcfb0939fea72c3b96a9f9a6edfc0e1b1d29778a2066030418b1", size = 4653529, upload-time = "2026-04-24T19:53:39.775Z" }, + { url = "https://files.pythonhosted.org/packages/e5/bc/641c0519a495f3bfd0421b48d7cd325c4336578523ccd76ea322b6c29c7a/cryptography-47.0.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:6651d32eff255423503aa276739da98c30f26c40cbeffcc6048e0d54ef704c0c", size = 4638570, upload-time = "2026-04-24T19:53:42.129Z" }, + { url = "https://files.pythonhosted.org/packages/2b/f2/300327b0a47f6dc94dd8b71b57052aefe178bb51745073d73d80604f11ab/cryptography-47.0.0-cp314-cp314t-manylinux_2_28_ppc64le.whl", hash = "sha256:3fb8fa48075fad7193f2e5496135c6a76ac4b2aa5a38433df0a539296b377829", size = 5238019, upload-time = "2026-04-24T19:53:44.577Z" }, + { url = "https://files.pythonhosted.org/packages/e9/5a/5b5cf994391d4bf9d9c7efd4c66aabe4d95227256627f8fea6cff7dfadbd/cryptography-47.0.0-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:11438c7518132d95f354fa01a4aa2f806d172a061a7bed18cf18cbdacdb204d7", size = 4686832, upload-time = "2026-04-24T19:53:47.015Z" }, + { url = "https://files.pythonhosted.org/packages/dc/2c/ae950e28fd6475c852fc21a44db3e6b5bcc1261d1e370f2b6e42fa800fef/cryptography-47.0.0-cp314-cp314t-manylinux_2_31_armv7l.whl", hash = "sha256:8c1a736bbb3288005796c3f7ccb9453360d7fed483b13b9f468aea5171432923", size = 4269301, upload-time = "2026-04-24T19:53:48.97Z" }, + { url = "https://files.pythonhosted.org/packages/67/fb/6a39782e150ffe5cc1b0018cb6ddc48bf7ca62b498d7539ffc8a758e977d/cryptography-47.0.0-cp314-cp314t-manylinux_2_34_aarch64.whl", hash = "sha256:f1557695e5c2b86e204f6ce9470497848634100787935ab7adc5397c54abd7ab", size = 4638110, upload-time = "2026-04-24T19:53:51.011Z" }, + { url = "https://files.pythonhosted.org/packages/8e/d7/0b3c71090a76e5c203164a47688b697635ece006dcd2499ab3a4dbd3f0bd/cryptography-47.0.0-cp314-cp314t-manylinux_2_34_ppc64le.whl", hash = "sha256:f9a034b642b960767fb343766ae5ba6ad653f2e890ddd82955aef288ffea8736", size = 5194988, upload-time = "2026-04-24T19:53:52.962Z" }, + { url = "https://files.pythonhosted.org/packages/63/33/63a961498a9df51721ab578c5a2622661411fc520e00bd83b0cc64eb20c4/cryptography-47.0.0-cp314-cp314t-manylinux_2_34_x86_64.whl", hash = "sha256:b1c76fca783aa7698eb21eb14f9c4aa09452248ee54a627d125025a43f83e7a7", size = 4686563, upload-time = "2026-04-24T19:53:55.274Z" }, + { url = "https://files.pythonhosted.org/packages/b7/bf/5ee5b145248f92250de86145d1c1d6edebbd57a7fe7caa4dedb5d4cf06a1/cryptography-47.0.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:4f7722c97826770bab8ae92959a2e7b20a5e9e9bf4deae68fd86c3ca457bab52", size = 4770094, upload-time = "2026-04-24T19:53:57.753Z" }, + { url = "https://files.pythonhosted.org/packages/92/43/21d220b2da5d517773894dacdcdb5c682c28d3fffce65548cb06e87d5501/cryptography-47.0.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:09f6d7bf6724f8db8b32f11eccf23efc8e759924bc5603800335cf8859a3ddbd", size = 4913811, upload-time = "2026-04-24T19:54:00.236Z" }, + { url = "https://files.pythonhosted.org/packages/31/98/dc4ad376ac5f1a1a7d4a83f7b0c6f2bcad36b5d2d8f30aeb482d3a7d9582/cryptography-47.0.0-cp314-cp314t-win32.whl", hash = "sha256:6eebcaf0df1d21ce1f90605c9b432dd2c4f4ab665ac29a40d5e3fc68f51b5e63", size = 3237158, upload-time = "2026-04-24T19:54:02.606Z" }, + { url = "https://files.pythonhosted.org/packages/bc/da/97f62d18306b5133468bc3f8cc73a3111e8cdc8cf8d3e69474d6e5fd2d1b/cryptography-47.0.0-cp314-cp314t-win_amd64.whl", hash = "sha256:51c9313e90bd1690ec5a75ed047c27c0b8e6c570029712943d6116ef9a90620b", size = 3758706, upload-time = "2026-04-24T19:54:04.433Z" }, + { url = "https://files.pythonhosted.org/packages/e0/34/a4fae8ae7c3bc227460c9ae43f56abf1b911da0ec29e0ebac53bb0a4b6b7/cryptography-47.0.0-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:14432c8a9bcb37009784f9594a62fae211a2ae9543e96c92b2a8e4c3cd5cd0c4", size = 7904072, upload-time = "2026-04-24T19:54:06.411Z" }, + { url = "https://files.pythonhosted.org/packages/01/64/d7b1e54fdb69f22d24a64bb3e88dc718b31c7fb10ef0b9691a3cf7eeea6e/cryptography-47.0.0-cp38-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:07efe86201817e7d3c18781ca9770bc0db04e1e48c994be384e4602bc38f8f27", size = 4635767, upload-time = "2026-04-24T19:54:08.519Z" }, + { url = "https://files.pythonhosted.org/packages/8b/7b/cca826391fb2a94efdcdfe4631eb69306ee1cff0b22f664a412c90713877/cryptography-47.0.0-cp38-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:2b45761c6ec22b7c726d6a829558777e32d0f1c8be7c3f3480f9c912d5ee8a10", size = 4654350, upload-time = "2026-04-24T19:54:10.795Z" }, + { url = "https://files.pythonhosted.org/packages/4c/65/4b57bcc823f42a991627c51c2f68c9fd6eb1393c1756aac876cba2accae2/cryptography-47.0.0-cp38-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:edd4da498015da5b9f26d38d3bfc2e90257bfa9cbed1f6767c282a0025ae649b", size = 4643394, upload-time = "2026-04-24T19:54:13.275Z" }, + { url = "https://files.pythonhosted.org/packages/f4/c4/2c5fbeea70adbbca2bbae865e1d605d6a4a7f8dbd9d33eaf69645087f06c/cryptography-47.0.0-cp38-abi3-manylinux_2_28_ppc64le.whl", hash = "sha256:9af828c0d5a65c70ec729cd7495a4bf1a67ecb66417b8f02ff125ab8a6326a74", size = 5225777, upload-time = "2026-04-24T19:54:15.18Z" }, + { url = "https://files.pythonhosted.org/packages/7e/b8/ac57107ef32749d2b244e36069bb688792a363aaaa3acc9e3cf84c130315/cryptography-47.0.0-cp38-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:256d07c78a04d6b276f5df935a9923275f53bd1522f214447fdf365494e2d515", size = 4688771, upload-time = "2026-04-24T19:54:17.835Z" }, + { url = "https://files.pythonhosted.org/packages/56/fc/9f1de22ff8be99d991f240a46863c52d475404c408886c5a38d2b5c3bb26/cryptography-47.0.0-cp38-abi3-manylinux_2_31_armv7l.whl", hash = "sha256:5d0e362ff51041b0c0d219cc7d6924d7b8996f57ce5712bdcef71eb3c65a59cc", size = 4270753, upload-time = "2026-04-24T19:54:19.963Z" }, + { url = "https://files.pythonhosted.org/packages/00/68/d70c852797aa68e8e48d12e5a87170c43f67bb4a59403627259dd57d15de/cryptography-47.0.0-cp38-abi3-manylinux_2_34_aarch64.whl", hash = "sha256:1581aef4219f7ca2849d0250edaa3866212fb74bf5667284f46aa92f9e65c1ca", size = 4642911, upload-time = "2026-04-24T19:54:21.818Z" }, + { url = "https://files.pythonhosted.org/packages/a5/51/661cbee74f594c5d97ff82d34f10d5551c085ca4668645f4606ebd22bd5d/cryptography-47.0.0-cp38-abi3-manylinux_2_34_ppc64le.whl", hash = "sha256:a49a3eb5341b9503fa3000a9a0db033161db90d47285291f53c2a9d2cd1b7f76", size = 5181411, upload-time = "2026-04-24T19:54:24.376Z" }, + { url = "https://files.pythonhosted.org/packages/94/87/f2b6c374a82cf076cfa1416992ac8e8ec94d79facc37aec87c1a5cb72352/cryptography-47.0.0-cp38-abi3-manylinux_2_34_x86_64.whl", hash = "sha256:2207a498b03275d0051589e326b79d4cf59985c99031b05bb292ac52631c37fe", size = 4688262, upload-time = "2026-04-24T19:54:26.946Z" }, + { url = "https://files.pythonhosted.org/packages/14/e2/8b7462f4acf21ec509616f0245018bb197194ab0b65c2ea21a0bdd53c0eb/cryptography-47.0.0-cp38-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:7a02675e2fabd0c0fc04c868b8781863cbf1967691543c22f5470500ff840b31", size = 4775506, upload-time = "2026-04-24T19:54:28.926Z" }, + { url = "https://files.pythonhosted.org/packages/70/75/158e494e4c08dc05e039da5bb48553826bd26c23930cf8d3cd5f21fa8921/cryptography-47.0.0-cp38-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:80887c5cbd1774683cb126f0ab4184567f080071d5acf62205acb354b4b753b7", size = 4912060, upload-time = "2026-04-24T19:54:30.869Z" }, + { url = "https://files.pythonhosted.org/packages/06/bd/0a9d3edbf5eadbac926d7b9b3cd0c4be584eeeae4a003d24d9eda4affbbd/cryptography-47.0.0-cp38-abi3-win32.whl", hash = "sha256:ed67ea4e0cfb5faa5bc7ecb6e2b8838f3807a03758eec239d6c21c8769355310", size = 3248487, upload-time = "2026-04-24T19:54:33.494Z" }, + { url = "https://files.pythonhosted.org/packages/60/80/5681af756d0da3a599b7bdb586fac5a1540f1bcefd2717a20e611ddade45/cryptography-47.0.0-cp38-abi3-win_amd64.whl", hash = "sha256:835d2d7f47cdc53b3224e90810fb1d36ca94ea29cc1801fb4c1bc43876735769", size = 3755737, upload-time = "2026-04-24T19:54:35.408Z" }, +] + +[[package]] +name = "cuda-bindings" +version = "13.2.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "cuda-pathfinder", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" }, +] +wheels = [ + { url = "https://files.pythonhosted.org/packages/df/93/eef988860a3ca985f82c4f3174fc0cdd94e07331ba9a92e8e064c260337f/cuda_bindings-13.2.0-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6629ca2df6f795b784752409bcaedbd22a7a651b74b56a165ebc0c9dcbd504d0", size = 5614610, upload-time = "2026-03-11T00:12:50.337Z" }, + { url = "https://files.pythonhosted.org/packages/18/23/6db3aba46864aee357ab2415135b3fe3da7e9f1fa0221fa2a86a5968099c/cuda_bindings-13.2.0-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7dca0da053d3b4cc4869eff49c61c03f3c5dbaa0bcd712317a358d5b8f3f385d", size = 6149914, upload-time = "2026-03-11T00:12:52.374Z" }, + { url = "https://files.pythonhosted.org/packages/c0/87/87a014f045b77c6de5c8527b0757fe644417b184e5367db977236a141602/cuda_bindings-13.2.0-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a6464b30f46692d6c7f65d4a0e0450d81dd29de3afc1bb515653973d01c2cd6e", size = 5685673, upload-time = "2026-03-11T00:12:56.371Z" }, + { url = "https://files.pythonhosted.org/packages/ee/5e/c0fe77a73aaefd3fff25ffaccaac69c5a63eafdf8b9a4c476626ef0ac703/cuda_bindings-13.2.0-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f4af9f3e1be603fa12d5ad6cfca7844c9d230befa9792b5abdf7dd79979c3626", size = 6191386, upload-time = "2026-03-11T00:12:58.965Z" }, + { url = "https://files.pythonhosted.org/packages/5f/58/ed2c3b39c8dd5f96aa7a4abef0d47a73932c7a988e30f5fa428f00ed0da1/cuda_bindings-13.2.0-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:df850a1ff8ce1b3385257b08e47b70e959932f5f432d0a4e46a355962b4e4771", size = 5507469, upload-time = "2026-03-11T00:13:04.063Z" }, + { url = "https://files.pythonhosted.org/packages/1f/01/0c941b112ceeb21439b05895eace78ca1aa2eaaf695c8521a068fd9b4c00/cuda_bindings-13.2.0-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e8a16384c6494e5485f39314b0b4afb04bee48d49edb16d5d8593fd35bbd231b", size = 6059693, upload-time = "2026-03-11T00:13:06.003Z" }, +] + +[[package]] +name = "cuda-pathfinder" +version = "1.5.4" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/11/d0/c177e29701cf1d3008d7d2b16b5fc626592ce13bd535f8795c5f57187e0e/cuda_pathfinder-1.5.4-py3-none-any.whl", hash = "sha256:9563d3175ce1828531acf4b94e1c1c7d67208c347ca002493e2654878b26f4b7", size = 51657, upload-time = "2026-04-27T22:42:07.712Z" }, +] + +[[package]] +name = "cuda-toolkit" +version = "13.0.2" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/57/b2/453099f5f3b698d7d0eab38916aac44c7f76229f451709e2eb9db6615dcd/cuda_toolkit-13.0.2-py2.py3-none-any.whl", hash = "sha256:b198824cf2f54003f50d64ada3a0f184b42ca0846c1c94192fa269ecd97a66eb", size = 2364, upload-time = "2025-12-19T23:24:07.328Z" }, +] + +[package.optional-dependencies] +cublas = [ + { name = "nvidia-cublas", marker = "sys_platform == 'linux'" }, +] +cudart = [ + { name = "nvidia-cuda-runtime", marker = "sys_platform == 'linux'" }, +] +cufft = [ + { name = "nvidia-cufft", marker = "sys_platform == 'linux'" }, +] +cufile = [ + { name = "nvidia-cufile", marker = "sys_platform == 'linux'" }, +] +cupti = [ + { name = "nvidia-cuda-cupti", marker = "sys_platform == 'linux'" }, +] +curand = [ + { name = "nvidia-curand", marker = "sys_platform == 'linux'" }, +] +cusolver = [ + { name = "nvidia-cusolver", marker = "sys_platform == 'linux'" }, +] +cusparse = [ + { name = "nvidia-cusparse", marker = "sys_platform == 'linux'" }, +] +nvjitlink = [ + { name = "nvidia-nvjitlink", marker = "sys_platform == 'linux'" }, +] +nvrtc = [ + { name = "nvidia-cuda-nvrtc", marker = "sys_platform == 'linux'" }, +] +nvtx = [ + { name = "nvidia-nvtx", marker = "sys_platform == 'linux'" }, +] + +[[package]] +name = "cycler" +version = "0.12.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/a9/95/a3dbbb5028f35eafb79008e7522a75244477d2838f38cbb722248dabc2a8/cycler-0.12.1.tar.gz", hash = "sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c", size = 7615, upload-time = "2023-10-07T05:32:18.335Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30", size = 8321, upload-time = "2023-10-07T05:32:16.783Z" }, +] + +[[package]] +name = "datasets" +version = "4.8.5" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "dill" }, + { name = "filelock" }, + { name = "fsspec", extra = ["http"] }, + { name = "httpx" }, + { name = "huggingface-hub" }, + { name = "multiprocess" }, + { name = "numpy" }, + { name = "packaging" }, + { name = "pandas" }, + { name = "pyarrow" }, + { name = "pyyaml" }, + { name = "requests" }, + { name = "tqdm" }, + { name = "xxhash" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/66/34/14cd8e76f907f7d4dca2334cfeec9f81d30fd15c25a015f99aaea694eaed/datasets-4.8.5.tar.gz", hash = "sha256:0f0c1c3d56ffff2c93b2f4c63c95bac94f3d7e8621aea2a2a576275233bba772", size = 605649, upload-time = "2026-04-27T15:43:57.384Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/65/99/00f3196036501b53032c4b1ab8337a0b978dee832ed276dae3815df4e8b5/datasets-4.8.5-py3-none-any.whl", hash = "sha256:5079900781719c0e063a8efdd2cd95a31ad0c63209178669cd23cf1b926149ff", size = 528973, upload-time = "2026-04-27T15:43:53.702Z" }, +] + +[[package]] +name = "debugpy" +version = "1.8.20" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/e0/b7/cd8080344452e4874aae67c40d8940e2b4d47b01601a8fd9f44786c757c7/debugpy-1.8.20.tar.gz", hash = "sha256:55bc8701714969f1ab89a6d5f2f3d40c36f91b2cbe2f65d98bf8196f6a6a2c33", size = 1645207, upload-time = "2026-01-29T23:03:28.199Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/15/e2/fc500524cc6f104a9d049abc85a0a8b3f0d14c0a39b9c140511c61e5b40b/debugpy-1.8.20-cp313-cp313-macosx_15_0_universal2.whl", hash = "sha256:5dff4bb27027821fdfcc9e8f87309a28988231165147c31730128b1c983e282a", size = 2539560, upload-time = "2026-01-29T23:03:48.738Z" }, + { url = "https://files.pythonhosted.org/packages/90/83/fb33dcea789ed6018f8da20c5a9bc9d82adc65c0c990faed43f7c955da46/debugpy-1.8.20-cp313-cp313-manylinux_2_34_x86_64.whl", hash = "sha256:84562982dd7cf5ebebfdea667ca20a064e096099997b175fe204e86817f64eaf", size = 4293272, upload-time = "2026-01-29T23:03:50.169Z" }, + { url = "https://files.pythonhosted.org/packages/a6/25/b1e4a01bfb824d79a6af24b99ef291e24189080c93576dfd9b1a2815cd0f/debugpy-1.8.20-cp313-cp313-win32.whl", hash = "sha256:da11dea6447b2cadbf8ce2bec59ecea87cc18d2c574980f643f2d2dfe4862393", size = 5331208, upload-time = "2026-01-29T23:03:51.547Z" }, + { url = "https://files.pythonhosted.org/packages/13/f7/a0b368ce54ffff9e9028c098bd2d28cfc5b54f9f6c186929083d4c60ba58/debugpy-1.8.20-cp313-cp313-win_amd64.whl", hash = "sha256:eb506e45943cab2efb7c6eafdd65b842f3ae779f020c82221f55aca9de135ed7", size = 5372930, upload-time = "2026-01-29T23:03:53.585Z" }, + { url = "https://files.pythonhosted.org/packages/33/2e/f6cb9a8a13f5058f0a20fe09711a7b726232cd5a78c6a7c05b2ec726cff9/debugpy-1.8.20-cp314-cp314-macosx_15_0_universal2.whl", hash = "sha256:9c74df62fc064cd5e5eaca1353a3ef5a5d50da5eb8058fcef63106f7bebe6173", size = 2538066, upload-time = "2026-01-29T23:03:54.999Z" }, + { url = "https://files.pythonhosted.org/packages/c5/56/6ddca50b53624e1ca3ce1d1e49ff22db46c47ea5fb4c0cc5c9b90a616364/debugpy-1.8.20-cp314-cp314-manylinux_2_34_x86_64.whl", hash = "sha256:077a7447589ee9bc1ff0cdf443566d0ecf540ac8aa7333b775ebcb8ce9f4ecad", size = 4269425, upload-time = "2026-01-29T23:03:56.518Z" }, + { url = "https://files.pythonhosted.org/packages/c5/d9/d64199c14a0d4c476df46c82470a3ce45c8d183a6796cfb5e66533b3663c/debugpy-1.8.20-cp314-cp314-win32.whl", hash = "sha256:352036a99dd35053b37b7803f748efc456076f929c6a895556932eaf2d23b07f", size = 5331407, upload-time = "2026-01-29T23:03:58.481Z" }, + { url = "https://files.pythonhosted.org/packages/e0/d9/1f07395b54413432624d61524dfd98c1a7c7827d2abfdb8829ac92638205/debugpy-1.8.20-cp314-cp314-win_amd64.whl", hash = "sha256:a98eec61135465b062846112e5ecf2eebb855305acc1dfbae43b72903b8ab5be", size = 5372521, upload-time = "2026-01-29T23:03:59.864Z" }, + { url = "https://files.pythonhosted.org/packages/e0/c3/7f67dea8ccf8fdcb9c99033bbe3e90b9e7395415843accb81428c441be2d/debugpy-1.8.20-py2.py3-none-any.whl", hash = "sha256:5be9bed9ae3be00665a06acaa48f8329d2b9632f15fd09f6a9a8c8d9907e54d7", size = 5337658, upload-time = "2026-01-29T23:04:17.404Z" }, +] + +[[package]] +name = "decorator" +version = "5.2.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/43/fa/6d96a0978d19e17b68d634497769987b16c8f4cd0a7a05048bec693caa6b/decorator-5.2.1.tar.gz", hash = "sha256:65f266143752f734b0a7cc83c46f4618af75b8c5911b00ccb61d0ac9b6da0360", size = 56711, upload-time = "2025-02-24T04:41:34.073Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/4e/8c/f3147f5c4b73e7550fe5f9352eaa956ae838d5c51eb58e7a25b9f3e2643b/decorator-5.2.1-py3-none-any.whl", hash = "sha256:d316bb415a2d9e2d2b3abcc4084c6502fc09240e292cd76a76afc106a1c8e04a", size = 9190, upload-time = "2025-02-24T04:41:32.565Z" }, +] + +[[package]] +name = "defusedxml" +version = "0.7.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/0f/d5/c66da9b79e5bdb124974bfe172b4daf3c984ebd9c2a06e2b8a4dc7331c72/defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69", size = 75520, upload-time = "2021-03-08T10:59:26.269Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/07/6c/aa3f2f849e01cb6a001cd8554a88d4c77c5c1a31c95bdf1cf9301e6d9ef4/defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61", size = 25604, upload-time = "2021-03-08T10:59:24.45Z" }, +] + +[[package]] +name = "dill" +version = "0.4.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/81/e1/56027a71e31b02ddc53c7d65b01e68edf64dea2932122fe7746a516f75d5/dill-0.4.1.tar.gz", hash = "sha256:423092df4182177d4d8ba8290c8a5b640c66ab35ec7da59ccfa00f6fa3eea5fa", size = 187315, upload-time = "2026-01-19T02:36:56.85Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/1e/77/dc8c558f7593132cf8fefec57c4f60c83b16941c574ac5f619abb3ae7933/dill-0.4.1-py3-none-any.whl", hash = "sha256:1e1ce33e978ae97fcfcff5638477032b801c46c7c65cf717f95fbc2248f79a9d", size = 120019, upload-time = "2026-01-19T02:36:55.663Z" }, +] + +[[package]] +name = "executing" +version = "2.2.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/cc/28/c14e053b6762b1044f34a13aab6859bbf40456d37d23aa286ac24cfd9a5d/executing-2.2.1.tar.gz", hash = "sha256:3632cc370565f6648cc328b32435bd120a1e4ebb20c77e3fdde9a13cd1e533c4", size = 1129488, upload-time = "2025-09-01T09:48:10.866Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/c1/ea/53f2148663b321f21b5a606bd5f191517cf40b7072c0497d3c92c4a13b1e/executing-2.2.1-py2.py3-none-any.whl", hash = "sha256:760643d3452b4d777d295bb167ccc74c64a81df23fb5e08eff250c425a4b2017", size = 28317, upload-time = "2025-09-01T09:48:08.5Z" }, +] + +[[package]] +name = "fastapi" +version = "0.136.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "annotated-doc" }, + { name = "pydantic" }, + { name = "starlette" }, + { name = "typing-extensions" }, + { name = "typing-inspection" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/5d/45/c130091c2dfa061bbfe3150f2a5091ef1adf149f2a8d2ae769ecaf6e99a2/fastapi-0.136.1.tar.gz", hash = "sha256:7af665ad7acfa0a3baf8983d393b6b471b9da10ede59c60045f49fbc89a0fa7f", size = 397448, upload-time = "2026-04-23T16:49:44.046Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/5a/ff/2e4eca3ade2c22fe1dea7043b8ee9dabe47753349eb1b56a202de8af6349/fastapi-0.136.1-py3-none-any.whl", hash = "sha256:a6e9d7eeada96c93a4d69cb03836b44fa34e2854accb7244a1ece36cd4781c3f", size = 117683, upload-time = "2026-04-23T16:49:42.437Z" }, +] + +[[package]] +name = "fastjsonschema" +version = "2.21.2" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/20/b5/23b216d9d985a956623b6bd12d4086b60f0059b27799f23016af04a74ea1/fastjsonschema-2.21.2.tar.gz", hash = "sha256:b1eb43748041c880796cd077f1a07c3d94e93ae84bba5ed36800a33554ae05de", size = 374130, upload-time = "2025-08-14T18:49:36.666Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/cb/a8/20d0723294217e47de6d9e2e40fd4a9d2f7c4b6ef974babd482a59743694/fastjsonschema-2.21.2-py3-none-any.whl", hash = "sha256:1c797122d0a86c5cace2e54bf4e819c36223b552017172f32c5c024a6b77e463", size = 24024, upload-time = "2025-08-14T18:49:34.776Z" }, +] + +[[package]] +name = "filelock" +version = "3.29.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/b5/fe/997687a931ab51049acce6fa1f23e8f01216374ea81374ddee763c493db5/filelock-3.29.0.tar.gz", hash = "sha256:69974355e960702e789734cb4871f884ea6fe50bd8404051a3530bc07809cf90", size = 57571, upload-time = "2026-04-19T15:39:10.068Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/81/47/dd9a212ef6e343a6857485ffe25bba537304f1913bdbed446a23f7f592e1/filelock-3.29.0-py3-none-any.whl", hash = "sha256:96f5f6344709aa1572bbf631c640e4ebeeb519e08da902c39a001882f30ac258", size = 39812, upload-time = "2026-04-19T15:39:08.752Z" }, +] + +[[package]] +name = "flatbuffers" +version = "25.12.19" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e8/2d/d2a548598be01649e2d46231d151a6c56d10b964d94043a335ae56ea2d92/flatbuffers-25.12.19-py2.py3-none-any.whl", hash = "sha256:7634f50c427838bb021c2d66a3d1168e9d199b0607e6329399f04846d42e20b4", size = 26661, upload-time = "2025-12-19T23:16:13.622Z" }, +] + +[[package]] +name = "fonttools" +version = "4.62.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/9a/08/7012b00a9a5874311b639c3920270c36ee0c445b69d9989a85e5c92ebcb0/fonttools-4.62.1.tar.gz", hash = "sha256:e54c75fd6041f1122476776880f7c3c3295ffa31962dc6ebe2543c00dca58b5d", size = 3580737, upload-time = "2026-03-13T13:54:25.52Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/3b/56/6f389de21c49555553d6a5aeed5ac9767631497ac836c4f076273d15bd72/fonttools-4.62.1-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:c22b1014017111c401469e3acc5433e6acf6ebcc6aa9efb538a533c800971c79", size = 2865155, upload-time = "2026-03-13T13:53:16.132Z" }, + { url = "https://files.pythonhosted.org/packages/03/c5/0e3966edd5ec668d41dfe418787726752bc07e2f5fd8c8f208615e61fa89/fonttools-4.62.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:68959f5fc58ed4599b44aad161c2837477d7f35f5f79402d97439974faebfebe", size = 2412802, upload-time = "2026-03-13T13:53:18.878Z" }, + { url = "https://files.pythonhosted.org/packages/52/94/e6ac4b44026de7786fe46e3bfa0c87e51d5d70a841054065d49cd62bb909/fonttools-4.62.1-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ef46db46c9447103b8f3ff91e8ba009d5fe181b1920a83757a5762551e32bb68", size = 5013926, upload-time = "2026-03-13T13:53:21.379Z" }, + { url = "https://files.pythonhosted.org/packages/e2/98/8b1e801939839d405f1f122e7d175cebe9aeb4e114f95bfc45e3152af9a7/fonttools-4.62.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:6706d1cb1d5e6251a97ad3c1b9347505c5615c112e66047abbef0f8545fa30d1", size = 4964575, upload-time = "2026-03-13T13:53:23.857Z" }, + { url = "https://files.pythonhosted.org/packages/46/76/7d051671e938b1881670528fec69cc4044315edd71a229c7fd712eaa5119/fonttools-4.62.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:2e7abd2b1e11736f58c1de27819e1955a53267c21732e78243fa2fa2e5c1e069", size = 4953693, upload-time = "2026-03-13T13:53:26.569Z" }, + { url = "https://files.pythonhosted.org/packages/1f/ae/b41f8628ec0be3c1b934fc12b84f4576a5c646119db4d3bdd76a217c90b5/fonttools-4.62.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:403d28ce06ebfc547fbcb0cb8b7f7cc2f7a2d3e1a67ba9a34b14632df9e080f9", size = 5094920, upload-time = "2026-03-13T13:53:29.329Z" }, + { url = "https://files.pythonhosted.org/packages/f2/f6/53a1e9469331a23dcc400970a27a4caa3d9f6edbf5baab0260285238b884/fonttools-4.62.1-cp313-cp313-win32.whl", hash = "sha256:93c316e0f5301b2adbe6a5f658634307c096fd5aae60a5b3412e4f3e1728ab24", size = 2279928, upload-time = "2026-03-13T13:53:32.352Z" }, + { url = "https://files.pythonhosted.org/packages/38/60/35186529de1db3c01f5ad625bde07c1f576305eab6d86bbda4c58445f721/fonttools-4.62.1-cp313-cp313-win_amd64.whl", hash = "sha256:7aa21ff53e28a9c2157acbc44e5b401149d3c9178107130e82d74ceb500e5056", size = 2330514, upload-time = "2026-03-13T13:53:34.991Z" }, + { url = "https://files.pythonhosted.org/packages/36/f0/2888cdac391807d68d90dcb16ef858ddc1b5309bfc6966195a459dd326e2/fonttools-4.62.1-cp314-cp314-macosx_10_15_universal2.whl", hash = "sha256:fa1d16210b6b10a826d71bed68dd9ec24a9e218d5a5e2797f37c573e7ec215ca", size = 2864442, upload-time = "2026-03-13T13:53:37.509Z" }, + { url = "https://files.pythonhosted.org/packages/4b/b2/e521803081f8dc35990816b82da6360fa668a21b44da4b53fc9e77efcd62/fonttools-4.62.1-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:aa69d10ed420d8121118e628ad47d86e4caa79ba37f968597b958f6cceab7eca", size = 2410901, upload-time = "2026-03-13T13:53:40.55Z" }, + { url = "https://files.pythonhosted.org/packages/00/a4/8c3511ff06e53110039358dbbdc1a65d72157a054638387aa2ada300a8b8/fonttools-4.62.1-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:bd13b7999d59c5eb1c2b442eb2d0c427cb517a0b7a1f5798fc5c9e003f5ff782", size = 4999608, upload-time = "2026-03-13T13:53:42.798Z" }, + { url = "https://files.pythonhosted.org/packages/28/63/cd0c3b26afe60995a5295f37c246a93d454023726c3261cfbb3559969bb9/fonttools-4.62.1-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:8d337fdd49a79b0d51c4da87bc38169d21c3abbf0c1aa9367eff5c6656fb6dae", size = 4912726, upload-time = "2026-03-13T13:53:45.405Z" }, + { url = "https://files.pythonhosted.org/packages/70/b9/ac677cb07c24c685cf34f64e140617d58789d67a3dd524164b63648c6114/fonttools-4.62.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:d241cdc4a67b5431c6d7f115fdf63335222414995e3a1df1a41e1182acd4bcc7", size = 4951422, upload-time = "2026-03-13T13:53:48.326Z" }, + { url = "https://files.pythonhosted.org/packages/e6/10/11c08419a14b85b7ca9a9faca321accccc8842dd9e0b1c8a72908de05945/fonttools-4.62.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:c05557a78f8fa514da0f869556eeda40887a8abc77c76ee3f74cf241778afd5a", size = 5060979, upload-time = "2026-03-13T13:53:51.366Z" }, + { url = "https://files.pythonhosted.org/packages/4e/3c/12eea4a4cf054e7ab058ed5ceada43b46809fce2bf319017c4d63ae55bb4/fonttools-4.62.1-cp314-cp314-win32.whl", hash = "sha256:49a445d2f544ce4a69338694cad575ba97b9a75fff02720da0882d1a73f12800", size = 2283733, upload-time = "2026-03-13T13:53:53.606Z" }, + { url = "https://files.pythonhosted.org/packages/6b/67/74b070029043186b5dd13462c958cb7c7f811be0d2e634309d9a1ffb1505/fonttools-4.62.1-cp314-cp314-win_amd64.whl", hash = "sha256:1eecc128c86c552fb963fe846ca4e011b1be053728f798185a1687502f6d398e", size = 2335663, upload-time = "2026-03-13T13:53:56.23Z" }, + { url = "https://files.pythonhosted.org/packages/42/c5/4d2ed3ca6e33617fc5624467da353337f06e7f637707478903c785bd8e20/fonttools-4.62.1-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:1596aeaddf7f78e21e68293c011316a25267b3effdaccaf4d59bc9159d681b82", size = 2947288, upload-time = "2026-03-13T13:53:59.397Z" }, + { url = "https://files.pythonhosted.org/packages/1f/e9/7ab11ddfda48ed0f89b13380e5595ba572619c27077be0b2c447a63ff351/fonttools-4.62.1-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:8f8fca95d3bb3208f59626a4b0ea6e526ee51f5a8ad5d91821c165903e8d9260", size = 2449023, upload-time = "2026-03-13T13:54:01.642Z" }, + { url = "https://files.pythonhosted.org/packages/b2/10/a800fa090b5e8819942e54e19b55fc7c21fe14a08757c3aa3ca8db358939/fonttools-4.62.1-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ee91628c08e76f77b533d65feb3fbe6d9dad699f95be51cf0d022db94089cdc4", size = 5137599, upload-time = "2026-03-13T13:54:04.495Z" }, + { url = "https://files.pythonhosted.org/packages/37/dc/8ccd45033fffd74deb6912fa1ca524643f584b94c87a16036855b498a1ed/fonttools-4.62.1-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:5f37df1cac61d906e7b836abe356bc2f34c99d4477467755c216b72aa3dc748b", size = 4920933, upload-time = "2026-03-13T13:54:07.557Z" }, + { url = "https://files.pythonhosted.org/packages/99/eb/e618adefb839598d25ac8136cd577925d6c513dc0d931d93b8af956210f0/fonttools-4.62.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:92bb00a947e666169c99b43753c4305fc95a890a60ef3aeb2a6963e07902cc87", size = 5016232, upload-time = "2026-03-13T13:54:10.611Z" }, + { url = "https://files.pythonhosted.org/packages/d9/5f/9b5c9bfaa8ec82def8d8168c4f13615990d6ce5996fe52bd49bfb5e05134/fonttools-4.62.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:bdfe592802ef939a0e33106ea4a318eeb17822c7ee168c290273cbd5fabd746c", size = 5042987, upload-time = "2026-03-13T13:54:13.569Z" }, + { url = "https://files.pythonhosted.org/packages/90/aa/dfbbe24c6a6afc5c203d90cc0343e24bcbb09e76d67c4d6eef8c2558d7ba/fonttools-4.62.1-cp314-cp314t-win32.whl", hash = "sha256:b820fcb92d4655513d8402d5b219f94481c4443d825b4372c75a2072aa4b357a", size = 2348021, upload-time = "2026-03-13T13:54:16.98Z" }, + { url = "https://files.pythonhosted.org/packages/13/6f/ae9c4e4dd417948407b680855c2c7790efb52add6009aaecff1e3bc50e8e/fonttools-4.62.1-cp314-cp314t-win_amd64.whl", hash = "sha256:59b372b4f0e113d3746b88985f1c796e7bf830dd54b28374cd85c2b8acd7583e", size = 2414147, upload-time = "2026-03-13T13:54:19.416Z" }, + { url = "https://files.pythonhosted.org/packages/fd/ba/56147c165442cc5ba7e82ecf301c9a68353cede498185869e6e02b4c264f/fonttools-4.62.1-py3-none-any.whl", hash = "sha256:7487782e2113861f4ddcc07c3436450659e3caa5e470b27dc2177cade2d8e7fd", size = 1152647, upload-time = "2026-03-13T13:54:22.735Z" }, +] + +[[package]] +name = "fqdn" +version = "1.5.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/30/3e/a80a8c077fd798951169626cde3e239adeba7dab75deb3555716415bd9b0/fqdn-1.5.1.tar.gz", hash = "sha256:105ed3677e767fb5ca086a0c1f4bb66ebc3c100be518f0e0d755d9eae164d89f", size = 6015, upload-time = "2021-03-11T07:16:29.08Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/cf/58/8acf1b3e91c58313ce5cb67df61001fc9dcd21be4fadb76c1a2d540e09ed/fqdn-1.5.1-py3-none-any.whl", hash = "sha256:3a179af3761e4df6eb2e026ff9e1a3033d3587bf980a0b1b2e1e5d08d7358014", size = 9121, upload-time = "2021-03-11T07:16:28.351Z" }, +] + +[[package]] +name = "frozenlist" +version = "1.8.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/2d/f5/c831fac6cc817d26fd54c7eaccd04ef7e0288806943f7cc5bbf69f3ac1f0/frozenlist-1.8.0.tar.gz", hash = "sha256:3ede829ed8d842f6cd48fc7081d7a41001a56f1f38603f9d49bf3020d59a31ad", size = 45875, upload-time = "2025-10-06T05:38:17.865Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/2d/40/0832c31a37d60f60ed79e9dfb5a92e1e2af4f40a16a29abcc7992af9edff/frozenlist-1.8.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:8d92f1a84bb12d9e56f818b3a746f3efba93c1b63c8387a73dde655e1e42282a", size = 85717, upload-time = "2025-10-06T05:36:27.341Z" }, + { url = "https://files.pythonhosted.org/packages/30/ba/b0b3de23f40bc55a7057bd38434e25c34fa48e17f20ee273bbde5e0650f3/frozenlist-1.8.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:96153e77a591c8adc2ee805756c61f59fef4cf4073a9275ee86fe8cba41241f7", size = 49651, upload-time = "2025-10-06T05:36:28.855Z" }, + { url = "https://files.pythonhosted.org/packages/0c/ab/6e5080ee374f875296c4243c381bbdef97a9ac39c6e3ce1d5f7d42cb78d6/frozenlist-1.8.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:f21f00a91358803399890ab167098c131ec2ddd5f8f5fd5fe9c9f2c6fcd91e40", size = 49417, upload-time = "2025-10-06T05:36:29.877Z" }, + { url = "https://files.pythonhosted.org/packages/d5/4e/e4691508f9477ce67da2015d8c00acd751e6287739123113a9fca6f1604e/frozenlist-1.8.0-cp313-cp313-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:fb30f9626572a76dfe4293c7194a09fb1fe93ba94c7d4f720dfae3b646b45027", size = 234391, upload-time = "2025-10-06T05:36:31.301Z" }, + { url = "https://files.pythonhosted.org/packages/40/76/c202df58e3acdf12969a7895fd6f3bc016c642e6726aa63bd3025e0fc71c/frozenlist-1.8.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:eaa352d7047a31d87dafcacbabe89df0aa506abb5b1b85a2fb91bc3faa02d822", size = 233048, upload-time = "2025-10-06T05:36:32.531Z" }, + { url = "https://files.pythonhosted.org/packages/f9/c0/8746afb90f17b73ca5979c7a3958116e105ff796e718575175319b5bb4ce/frozenlist-1.8.0-cp313-cp313-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:03ae967b4e297f58f8c774c7eabcce57fe3c2434817d4385c50661845a058121", size = 226549, upload-time = "2025-10-06T05:36:33.706Z" }, + { url = "https://files.pythonhosted.org/packages/7e/eb/4c7eefc718ff72f9b6c4893291abaae5fbc0c82226a32dcd8ef4f7a5dbef/frozenlist-1.8.0-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:f6292f1de555ffcc675941d65fffffb0a5bcd992905015f85d0592201793e0e5", size = 239833, upload-time = "2025-10-06T05:36:34.947Z" }, + { url = "https://files.pythonhosted.org/packages/c2/4e/e5c02187cf704224f8b21bee886f3d713ca379535f16893233b9d672ea71/frozenlist-1.8.0-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:29548f9b5b5e3460ce7378144c3010363d8035cea44bc0bf02d57f5a685e084e", size = 245363, upload-time = "2025-10-06T05:36:36.534Z" }, + { url = "https://files.pythonhosted.org/packages/1f/96/cb85ec608464472e82ad37a17f844889c36100eed57bea094518bf270692/frozenlist-1.8.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:ec3cc8c5d4084591b4237c0a272cc4f50a5b03396a47d9caaf76f5d7b38a4f11", size = 229314, upload-time = "2025-10-06T05:36:38.582Z" }, + { url = "https://files.pythonhosted.org/packages/5d/6f/4ae69c550e4cee66b57887daeebe006fe985917c01d0fff9caab9883f6d0/frozenlist-1.8.0-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:517279f58009d0b1f2e7c1b130b377a349405da3f7621ed6bfae50b10adf20c1", size = 243365, upload-time = "2025-10-06T05:36:40.152Z" }, + { url = "https://files.pythonhosted.org/packages/7a/58/afd56de246cf11780a40a2c28dc7cbabbf06337cc8ddb1c780a2d97e88d8/frozenlist-1.8.0-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:db1e72ede2d0d7ccb213f218df6a078a9c09a7de257c2fe8fcef16d5925230b1", size = 237763, upload-time = "2025-10-06T05:36:41.355Z" }, + { url = "https://files.pythonhosted.org/packages/cb/36/cdfaf6ed42e2644740d4a10452d8e97fa1c062e2a8006e4b09f1b5fd7d63/frozenlist-1.8.0-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:b4dec9482a65c54a5044486847b8a66bf10c9cb4926d42927ec4e8fd5db7fed8", size = 240110, upload-time = "2025-10-06T05:36:42.716Z" }, + { url = "https://files.pythonhosted.org/packages/03/a8/9ea226fbefad669f11b52e864c55f0bd57d3c8d7eb07e9f2e9a0b39502e1/frozenlist-1.8.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:21900c48ae04d13d416f0e1e0c4d81f7931f73a9dfa0b7a8746fb2fe7dd970ed", size = 233717, upload-time = "2025-10-06T05:36:44.251Z" }, + { url = "https://files.pythonhosted.org/packages/1e/0b/1b5531611e83ba7d13ccc9988967ea1b51186af64c42b7a7af465dcc9568/frozenlist-1.8.0-cp313-cp313-win32.whl", hash = "sha256:8b7b94a067d1c504ee0b16def57ad5738701e4ba10cec90529f13fa03c833496", size = 39628, upload-time = "2025-10-06T05:36:45.423Z" }, + { url = "https://files.pythonhosted.org/packages/d8/cf/174c91dbc9cc49bc7b7aab74d8b734e974d1faa8f191c74af9b7e80848e6/frozenlist-1.8.0-cp313-cp313-win_amd64.whl", hash = "sha256:878be833caa6a3821caf85eb39c5ba92d28e85df26d57afb06b35b2efd937231", size = 43882, upload-time = "2025-10-06T05:36:46.796Z" }, + { url = "https://files.pythonhosted.org/packages/c1/17/502cd212cbfa96eb1388614fe39a3fc9ab87dbbe042b66f97acb57474834/frozenlist-1.8.0-cp313-cp313-win_arm64.whl", hash = "sha256:44389d135b3ff43ba8cc89ff7f51f5a0bb6b63d829c8300f79a2fe4fe61bcc62", size = 39676, upload-time = "2025-10-06T05:36:47.8Z" }, + { url = "https://files.pythonhosted.org/packages/d2/5c/3bbfaa920dfab09e76946a5d2833a7cbdf7b9b4a91c714666ac4855b88b4/frozenlist-1.8.0-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:e25ac20a2ef37e91c1b39938b591457666a0fa835c7783c3a8f33ea42870db94", size = 89235, upload-time = "2025-10-06T05:36:48.78Z" }, + { url = "https://files.pythonhosted.org/packages/d2/d6/f03961ef72166cec1687e84e8925838442b615bd0b8854b54923ce5b7b8a/frozenlist-1.8.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:07cdca25a91a4386d2e76ad992916a85038a9b97561bf7a3fd12d5d9ce31870c", size = 50742, upload-time = "2025-10-06T05:36:49.837Z" }, + { url = "https://files.pythonhosted.org/packages/1e/bb/a6d12b7ba4c3337667d0e421f7181c82dda448ce4e7ad7ecd249a16fa806/frozenlist-1.8.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:4e0c11f2cc6717e0a741f84a527c52616140741cd812a50422f83dc31749fb52", size = 51725, upload-time = "2025-10-06T05:36:50.851Z" }, + { url = "https://files.pythonhosted.org/packages/bc/71/d1fed0ffe2c2ccd70b43714c6cab0f4188f09f8a67a7914a6b46ee30f274/frozenlist-1.8.0-cp313-cp313t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:b3210649ee28062ea6099cfda39e147fa1bc039583c8ee4481cb7811e2448c51", size = 284533, upload-time = "2025-10-06T05:36:51.898Z" }, + { url = "https://files.pythonhosted.org/packages/c9/1f/fb1685a7b009d89f9bf78a42d94461bc06581f6e718c39344754a5d9bada/frozenlist-1.8.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:581ef5194c48035a7de2aefc72ac6539823bb71508189e5de01d60c9dcd5fa65", size = 292506, upload-time = "2025-10-06T05:36:53.101Z" }, + { url = "https://files.pythonhosted.org/packages/e6/3b/b991fe1612703f7e0d05c0cf734c1b77aaf7c7d321df4572e8d36e7048c8/frozenlist-1.8.0-cp313-cp313t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:3ef2d026f16a2b1866e1d86fc4e1291e1ed8a387b2c333809419a2f8b3a77b82", size = 274161, upload-time = "2025-10-06T05:36:54.309Z" }, + { url = "https://files.pythonhosted.org/packages/ca/ec/c5c618767bcdf66e88945ec0157d7f6c4a1322f1473392319b7a2501ded7/frozenlist-1.8.0-cp313-cp313t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:5500ef82073f599ac84d888e3a8c1f77ac831183244bfd7f11eaa0289fb30714", size = 294676, upload-time = "2025-10-06T05:36:55.566Z" }, + { url = "https://files.pythonhosted.org/packages/7c/ce/3934758637d8f8a88d11f0585d6495ef54b2044ed6ec84492a91fa3b27aa/frozenlist-1.8.0-cp313-cp313t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:50066c3997d0091c411a66e710f4e11752251e6d2d73d70d8d5d4c76442a199d", size = 300638, upload-time = "2025-10-06T05:36:56.758Z" }, + { url = "https://files.pythonhosted.org/packages/fc/4f/a7e4d0d467298f42de4b41cbc7ddaf19d3cfeabaf9ff97c20c6c7ee409f9/frozenlist-1.8.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:5c1c8e78426e59b3f8005e9b19f6ff46e5845895adbde20ece9218319eca6506", size = 283067, upload-time = "2025-10-06T05:36:57.965Z" }, + { url = "https://files.pythonhosted.org/packages/dc/48/c7b163063d55a83772b268e6d1affb960771b0e203b632cfe09522d67ea5/frozenlist-1.8.0-cp313-cp313t-musllinux_1_2_armv7l.whl", hash = "sha256:eefdba20de0d938cec6a89bd4d70f346a03108a19b9df4248d3cf0d88f1b0f51", size = 292101, upload-time = "2025-10-06T05:36:59.237Z" }, + { url = "https://files.pythonhosted.org/packages/9f/d0/2366d3c4ecdc2fd391e0afa6e11500bfba0ea772764d631bbf82f0136c9d/frozenlist-1.8.0-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:cf253e0e1c3ceb4aaff6df637ce033ff6535fb8c70a764a8f46aafd3d6ab798e", size = 289901, upload-time = "2025-10-06T05:37:00.811Z" }, + { url = "https://files.pythonhosted.org/packages/b8/94/daff920e82c1b70e3618a2ac39fbc01ae3e2ff6124e80739ce5d71c9b920/frozenlist-1.8.0-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:032efa2674356903cd0261c4317a561a6850f3ac864a63fc1583147fb05a79b0", size = 289395, upload-time = "2025-10-06T05:37:02.115Z" }, + { url = "https://files.pythonhosted.org/packages/e3/20/bba307ab4235a09fdcd3cc5508dbabd17c4634a1af4b96e0f69bfe551ebd/frozenlist-1.8.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:6da155091429aeba16851ecb10a9104a108bcd32f6c1642867eadaee401c1c41", size = 283659, upload-time = "2025-10-06T05:37:03.711Z" }, + { url = "https://files.pythonhosted.org/packages/fd/00/04ca1c3a7a124b6de4f8a9a17cc2fcad138b4608e7a3fc5877804b8715d7/frozenlist-1.8.0-cp313-cp313t-win32.whl", hash = "sha256:0f96534f8bfebc1a394209427d0f8a63d343c9779cda6fc25e8e121b5fd8555b", size = 43492, upload-time = "2025-10-06T05:37:04.915Z" }, + { url = "https://files.pythonhosted.org/packages/59/5e/c69f733a86a94ab10f68e496dc6b7e8bc078ebb415281d5698313e3af3a1/frozenlist-1.8.0-cp313-cp313t-win_amd64.whl", hash = "sha256:5d63a068f978fc69421fb0e6eb91a9603187527c86b7cd3f534a5b77a592b888", size = 48034, upload-time = "2025-10-06T05:37:06.343Z" }, + { url = "https://files.pythonhosted.org/packages/16/6c/be9d79775d8abe79b05fa6d23da99ad6e7763a1d080fbae7290b286093fd/frozenlist-1.8.0-cp313-cp313t-win_arm64.whl", hash = "sha256:bf0a7e10b077bf5fb9380ad3ae8ce20ef919a6ad93b4552896419ac7e1d8e042", size = 41749, upload-time = "2025-10-06T05:37:07.431Z" }, + { url = "https://files.pythonhosted.org/packages/f1/c8/85da824b7e7b9b6e7f7705b2ecaf9591ba6f79c1177f324c2735e41d36a2/frozenlist-1.8.0-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:cee686f1f4cadeb2136007ddedd0aaf928ab95216e7691c63e50a8ec066336d0", size = 86127, upload-time = "2025-10-06T05:37:08.438Z" }, + { url = "https://files.pythonhosted.org/packages/8e/e8/a1185e236ec66c20afd72399522f142c3724c785789255202d27ae992818/frozenlist-1.8.0-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:119fb2a1bd47307e899c2fac7f28e85b9a543864df47aa7ec9d3c1b4545f096f", size = 49698, upload-time = "2025-10-06T05:37:09.48Z" }, + { url = "https://files.pythonhosted.org/packages/a1/93/72b1736d68f03fda5fdf0f2180fb6caaae3894f1b854d006ac61ecc727ee/frozenlist-1.8.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:4970ece02dbc8c3a92fcc5228e36a3e933a01a999f7094ff7c23fbd2beeaa67c", size = 49749, upload-time = "2025-10-06T05:37:10.569Z" }, + { url = "https://files.pythonhosted.org/packages/a7/b2/fabede9fafd976b991e9f1b9c8c873ed86f202889b864756f240ce6dd855/frozenlist-1.8.0-cp314-cp314-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:cba69cb73723c3f329622e34bdbf5ce1f80c21c290ff04256cff1cd3c2036ed2", size = 231298, upload-time = "2025-10-06T05:37:11.993Z" }, + { url = "https://files.pythonhosted.org/packages/3a/3b/d9b1e0b0eed36e70477ffb8360c49c85c8ca8ef9700a4e6711f39a6e8b45/frozenlist-1.8.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:778a11b15673f6f1df23d9586f83c4846c471a8af693a22e066508b77d201ec8", size = 232015, upload-time = "2025-10-06T05:37:13.194Z" }, + { url = "https://files.pythonhosted.org/packages/dc/94/be719d2766c1138148564a3960fc2c06eb688da592bdc25adcf856101be7/frozenlist-1.8.0-cp314-cp314-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:0325024fe97f94c41c08872db482cf8ac4800d80e79222c6b0b7b162d5b13686", size = 225038, upload-time = "2025-10-06T05:37:14.577Z" }, + { url = "https://files.pythonhosted.org/packages/e4/09/6712b6c5465f083f52f50cf74167b92d4ea2f50e46a9eea0523d658454ae/frozenlist-1.8.0-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:97260ff46b207a82a7567b581ab4190bd4dfa09f4db8a8b49d1a958f6aa4940e", size = 240130, upload-time = "2025-10-06T05:37:15.781Z" }, + { url = "https://files.pythonhosted.org/packages/f8/d4/cd065cdcf21550b54f3ce6a22e143ac9e4836ca42a0de1022da8498eac89/frozenlist-1.8.0-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:54b2077180eb7f83dd52c40b2750d0a9f175e06a42e3213ce047219de902717a", size = 242845, upload-time = "2025-10-06T05:37:17.037Z" }, + { url = "https://files.pythonhosted.org/packages/62/c3/f57a5c8c70cd1ead3d5d5f776f89d33110b1addae0ab010ad774d9a44fb9/frozenlist-1.8.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:2f05983daecab868a31e1da44462873306d3cbfd76d1f0b5b69c473d21dbb128", size = 229131, upload-time = "2025-10-06T05:37:18.221Z" }, + { url = "https://files.pythonhosted.org/packages/6c/52/232476fe9cb64f0742f3fde2b7d26c1dac18b6d62071c74d4ded55e0ef94/frozenlist-1.8.0-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:33f48f51a446114bc5d251fb2954ab0164d5be02ad3382abcbfe07e2531d650f", size = 240542, upload-time = "2025-10-06T05:37:19.771Z" }, + { url = "https://files.pythonhosted.org/packages/5f/85/07bf3f5d0fb5414aee5f47d33c6f5c77bfe49aac680bfece33d4fdf6a246/frozenlist-1.8.0-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:154e55ec0655291b5dd1b8731c637ecdb50975a2ae70c606d100750a540082f7", size = 237308, upload-time = "2025-10-06T05:37:20.969Z" }, + { url = "https://files.pythonhosted.org/packages/11/99/ae3a33d5befd41ac0ca2cc7fd3aa707c9c324de2e89db0e0f45db9a64c26/frozenlist-1.8.0-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:4314debad13beb564b708b4a496020e5306c7333fa9a3ab90374169a20ffab30", size = 238210, upload-time = "2025-10-06T05:37:22.252Z" }, + { url = "https://files.pythonhosted.org/packages/b2/60/b1d2da22f4970e7a155f0adde9b1435712ece01b3cd45ba63702aea33938/frozenlist-1.8.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:073f8bf8becba60aa931eb3bc420b217bb7d5b8f4750e6f8b3be7f3da85d38b7", size = 231972, upload-time = "2025-10-06T05:37:23.5Z" }, + { url = "https://files.pythonhosted.org/packages/3f/ab/945b2f32de889993b9c9133216c068b7fcf257d8595a0ac420ac8677cab0/frozenlist-1.8.0-cp314-cp314-win32.whl", hash = "sha256:bac9c42ba2ac65ddc115d930c78d24ab8d4f465fd3fc473cdedfccadb9429806", size = 40536, upload-time = "2025-10-06T05:37:25.581Z" }, + { url = "https://files.pythonhosted.org/packages/59/ad/9caa9b9c836d9ad6f067157a531ac48b7d36499f5036d4141ce78c230b1b/frozenlist-1.8.0-cp314-cp314-win_amd64.whl", hash = "sha256:3e0761f4d1a44f1d1a47996511752cf3dcec5bbdd9cc2b4fe595caf97754b7a0", size = 44330, upload-time = "2025-10-06T05:37:26.928Z" }, + { url = "https://files.pythonhosted.org/packages/82/13/e6950121764f2676f43534c555249f57030150260aee9dcf7d64efda11dd/frozenlist-1.8.0-cp314-cp314-win_arm64.whl", hash = "sha256:d1eaff1d00c7751b7c6662e9c5ba6eb2c17a2306ba5e2a37f24ddf3cc953402b", size = 40627, upload-time = "2025-10-06T05:37:28.075Z" }, + { url = "https://files.pythonhosted.org/packages/c0/c7/43200656ecc4e02d3f8bc248df68256cd9572b3f0017f0a0c4e93440ae23/frozenlist-1.8.0-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:d3bb933317c52d7ea5004a1c442eef86f426886fba134ef8cf4226ea6ee1821d", size = 89238, upload-time = "2025-10-06T05:37:29.373Z" }, + { url = "https://files.pythonhosted.org/packages/d1/29/55c5f0689b9c0fb765055629f472c0de484dcaf0acee2f7707266ae3583c/frozenlist-1.8.0-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:8009897cdef112072f93a0efdce29cd819e717fd2f649ee3016efd3cd885a7ed", size = 50738, upload-time = "2025-10-06T05:37:30.792Z" }, + { url = "https://files.pythonhosted.org/packages/ba/7d/b7282a445956506fa11da8c2db7d276adcbf2b17d8bb8407a47685263f90/frozenlist-1.8.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:2c5dcbbc55383e5883246d11fd179782a9d07a986c40f49abe89ddf865913930", size = 51739, upload-time = "2025-10-06T05:37:32.127Z" }, + { url = "https://files.pythonhosted.org/packages/62/1c/3d8622e60d0b767a5510d1d3cf21065b9db874696a51ea6d7a43180a259c/frozenlist-1.8.0-cp314-cp314t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:39ecbc32f1390387d2aa4f5a995e465e9e2f79ba3adcac92d68e3e0afae6657c", size = 284186, upload-time = "2025-10-06T05:37:33.21Z" }, + { url = "https://files.pythonhosted.org/packages/2d/14/aa36d5f85a89679a85a1d44cd7a6657e0b1c75f61e7cad987b203d2daca8/frozenlist-1.8.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:92db2bf818d5cc8d9c1f1fc56b897662e24ea5adb36ad1f1d82875bd64e03c24", size = 292196, upload-time = "2025-10-06T05:37:36.107Z" }, + { url = "https://files.pythonhosted.org/packages/05/23/6bde59eb55abd407d34f77d39a5126fb7b4f109a3f611d3929f14b700c66/frozenlist-1.8.0-cp314-cp314t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:2dc43a022e555de94c3b68a4ef0b11c4f747d12c024a520c7101709a2144fb37", size = 273830, upload-time = "2025-10-06T05:37:37.663Z" }, + { url = "https://files.pythonhosted.org/packages/d2/3f/22cff331bfad7a8afa616289000ba793347fcd7bc275f3b28ecea2a27909/frozenlist-1.8.0-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:cb89a7f2de3602cfed448095bab3f178399646ab7c61454315089787df07733a", size = 294289, upload-time = "2025-10-06T05:37:39.261Z" }, + { url = "https://files.pythonhosted.org/packages/a4/89/5b057c799de4838b6c69aa82b79705f2027615e01be996d2486a69ca99c4/frozenlist-1.8.0-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:33139dc858c580ea50e7e60a1b0ea003efa1fd42e6ec7fdbad78fff65fad2fd2", size = 300318, upload-time = "2025-10-06T05:37:43.213Z" }, + { url = "https://files.pythonhosted.org/packages/30/de/2c22ab3eb2a8af6d69dc799e48455813bab3690c760de58e1bf43b36da3e/frozenlist-1.8.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:168c0969a329b416119507ba30b9ea13688fafffac1b7822802537569a1cb0ef", size = 282814, upload-time = "2025-10-06T05:37:45.337Z" }, + { url = "https://files.pythonhosted.org/packages/59/f7/970141a6a8dbd7f556d94977858cfb36fa9b66e0892c6dd780d2219d8cd8/frozenlist-1.8.0-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:28bd570e8e189d7f7b001966435f9dac6718324b5be2990ac496cf1ea9ddb7fe", size = 291762, upload-time = "2025-10-06T05:37:46.657Z" }, + { url = "https://files.pythonhosted.org/packages/c1/15/ca1adae83a719f82df9116d66f5bb28bb95557b3951903d39135620ef157/frozenlist-1.8.0-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:b2a095d45c5d46e5e79ba1e5b9cb787f541a8dee0433836cea4b96a2c439dcd8", size = 289470, upload-time = "2025-10-06T05:37:47.946Z" }, + { url = "https://files.pythonhosted.org/packages/ac/83/dca6dc53bf657d371fbc88ddeb21b79891e747189c5de990b9dfff2ccba1/frozenlist-1.8.0-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:eab8145831a0d56ec9c4139b6c3e594c7a83c2c8be25d5bcf2d86136a532287a", size = 289042, upload-time = "2025-10-06T05:37:49.499Z" }, + { url = "https://files.pythonhosted.org/packages/96/52/abddd34ca99be142f354398700536c5bd315880ed0a213812bc491cff5e4/frozenlist-1.8.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:974b28cf63cc99dfb2188d8d222bc6843656188164848c4f679e63dae4b0708e", size = 283148, upload-time = "2025-10-06T05:37:50.745Z" }, + { url = "https://files.pythonhosted.org/packages/af/d3/76bd4ed4317e7119c2b7f57c3f6934aba26d277acc6309f873341640e21f/frozenlist-1.8.0-cp314-cp314t-win32.whl", hash = "sha256:342c97bf697ac5480c0a7ec73cd700ecfa5a8a40ac923bd035484616efecc2df", size = 44676, upload-time = "2025-10-06T05:37:52.222Z" }, + { url = "https://files.pythonhosted.org/packages/89/76/c615883b7b521ead2944bb3480398cbb07e12b7b4e4d073d3752eb721558/frozenlist-1.8.0-cp314-cp314t-win_amd64.whl", hash = "sha256:06be8f67f39c8b1dc671f5d83aaefd3358ae5cdcf8314552c57e7ed3e6475bdd", size = 49451, upload-time = "2025-10-06T05:37:53.425Z" }, + { url = "https://files.pythonhosted.org/packages/e0/a3/5982da14e113d07b325230f95060e2169f5311b1017ea8af2a29b374c289/frozenlist-1.8.0-cp314-cp314t-win_arm64.whl", hash = "sha256:102e6314ca4da683dca92e3b1355490fed5f313b768500084fbe6371fddfdb79", size = 42507, upload-time = "2025-10-06T05:37:54.513Z" }, + { url = "https://files.pythonhosted.org/packages/9a/9a/e35b4a917281c0b8419d4207f4334c8e8c5dbf4f3f5f9ada73958d937dcc/frozenlist-1.8.0-py3-none-any.whl", hash = "sha256:0c18a16eab41e82c295618a77502e17b195883241c563b00f0aa5106fc4eaa0d", size = 13409, upload-time = "2025-10-06T05:38:16.721Z" }, +] + +[[package]] +name = "fsspec" +version = "2026.2.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/51/7c/f60c259dcbf4f0c47cc4ddb8f7720d2dcdc8888c8e5ad84c73ea4531cc5b/fsspec-2026.2.0.tar.gz", hash = "sha256:6544e34b16869f5aacd5b90bdf1a71acb37792ea3ddf6125ee69a22a53fb8bff", size = 313441, upload-time = "2026-02-05T21:50:53.743Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e6/ab/fb21f4c939bb440104cc2b396d3be1d9b7a9fd3c6c2a53d98c45b3d7c954/fsspec-2026.2.0-py3-none-any.whl", hash = "sha256:98de475b5cb3bd66bedd5c4679e87b4fdfe1a3bf4d707b151b3c07e58c9a2437", size = 202505, upload-time = "2026-02-05T21:50:51.819Z" }, +] + +[package.optional-dependencies] +http = [ + { name = "aiohttp" }, +] + +[[package]] +name = "gliner" +version = "0.2.26" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "huggingface-hub" }, + { name = "onnxruntime" }, + { name = "sentencepiece" }, + { name = "torch" }, + { name = "tqdm" }, + { name = "transformers" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/49/18/e199cb97147c4a9260c75e4caf51e17be6ff969b0604a029c9c62810cbe0/gliner-0.2.26.tar.gz", hash = "sha256:6783be92b4b81caa878dcc4269ba37800207c37118d8ff9be028b93bddd6813d", size = 181224, upload-time = "2026-03-19T15:07:22.707Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/7c/6e/d54d3d2867e29b68a22b144f570c8204209647fccc7879cec5218d6ed5fb/gliner-0.2.26-py3-none-any.whl", hash = "sha256:b9baa47641efb90b9d069add0528ed2464d137991ff097f42b0cab37a91ba991", size = 170429, upload-time = "2026-03-19T15:07:19.914Z" }, +] + +[[package]] +name = "gliner-glirel-tuning" +version = "0.1.0" +source = { virtual = "." } +dependencies = [ + { name = "gliner" }, + { name = "glirel" }, + { name = "huggingface-hub" }, + { name = "jupyter" }, + { name = "jupyter-collaboration" }, + { name = "jupyter-mcp-server" }, + { name = "jupyterlab" }, + { name = "loguru" }, + { name = "matplotlib" }, + { name = "numpy" }, + { name = "pandas" }, + { name = "torch" }, + { name = "transformers" }, +] + +[package.metadata] +requires-dist = [ + { name = "gliner", specifier = ">=0.2.26" }, + { name = "glirel", specifier = ">=1.2.1" }, + { name = "huggingface-hub", specifier = ">=1.13.0" }, + { name = "jupyter", specifier = ">=1.1.1" }, + { name = "jupyter-collaboration", specifier = ">=4.3.0" }, + { name = "jupyter-mcp-server", specifier = ">=1.0.2" }, + { name = "jupyterlab", specifier = ">=4.5.7" }, + { name = "loguru", specifier = ">=0.7.3" }, + { name = "matplotlib", specifier = ">=3.10.9" }, + { name = "numpy", specifier = ">=2.4.4" }, + { name = "pandas", specifier = ">=3.0.2" }, + { name = "torch", specifier = ">=2.11.0" }, + { name = "transformers", specifier = ">=5.1.0" }, +] + +[[package]] +name = "glirel" +version = "1.2.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "datasets" }, + { name = "huggingface-hub" }, + { name = "seqeval" }, + { name = "torch" }, + { name = "tqdm" }, + { name = "transformers" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/aa/17/366a8bcdf3631af9f11b989c3387b952cf3fd3b786df95952e826d64374a/glirel-1.2.1.tar.gz", hash = "sha256:96e0636810c6f020707a515d4f08aa36b7950c7d06030b5fe149b86d716e00e4", size = 49623, upload-time = "2025-04-11T10:10:58.396Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/2f/62/fce91d0f41f493d1fb69db3df828aa6c2a76390a4a38db5db6a40b3b2150/glirel-1.2.1-py3-none-any.whl", hash = "sha256:ecbe2f955d8f2a406931022a10753e889761de7a736ec4090187830e49689aaf", size = 54298, upload-time = "2025-04-11T10:10:57.277Z" }, +] + +[[package]] +name = "h11" +version = "0.16.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/01/ee/02a2c011bdab74c6fb3c75474d40b3052059d95df7e73351460c8588d963/h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1", size = 101250, upload-time = "2025-04-24T03:35:25.427Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/04/4b/29cac41a4d98d144bf5f6d33995617b185d14b22401f75ca86f384e87ff1/h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86", size = 37515, upload-time = "2025-04-24T03:35:24.344Z" }, +] + +[[package]] +name = "hf-xet" +version = "1.4.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/53/92/ec9ad04d0b5728dca387a45af7bc98fbb0d73b2118759f5f6038b61a57e8/hf_xet-1.4.3.tar.gz", hash = "sha256:8ddedb73c8c08928c793df2f3401ec26f95be7f7e516a7bee2fbb546f6676113", size = 670477, upload-time = "2026-03-31T22:40:07.874Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/72/43/724d307b34e353da0abd476e02f72f735cdd2bc86082dee1b32ea0bfee1d/hf_xet-1.4.3-cp313-cp313t-macosx_10_12_x86_64.whl", hash = "sha256:7551659ba4f1e1074e9623996f28c3873682530aee0a846b7f2f066239228144", size = 3800935, upload-time = "2026-03-31T22:39:49.618Z" }, + { url = "https://files.pythonhosted.org/packages/2b/d2/8bee5996b699262edb87dbb54118d287c0e1b2fc78af7cdc41857ba5e3c4/hf_xet-1.4.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:bee693ada985e7045997f05f081d0e12c4c08bd7626dc397f8a7c487e6c04f7f", size = 3558942, upload-time = "2026-03-31T22:39:47.938Z" }, + { url = "https://files.pythonhosted.org/packages/c3/a1/e993d09cbe251196fb60812b09a58901c468127b7259d2bf0f68bf6088eb/hf_xet-1.4.3-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:21644b404bb0100fe3857892f752c4d09642586fd988e61501c95bbf44b393a3", size = 4207657, upload-time = "2026-03-31T22:39:39.69Z" }, + { url = "https://files.pythonhosted.org/packages/64/44/9eb6d21e5c34c63e5e399803a6932fa983cabdf47c0ecbcfe7ea97684b8c/hf_xet-1.4.3-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:987f09cfe418237812896a6736b81b1af02a3a6dcb4b4944425c4c4fca7a7cf8", size = 3986765, upload-time = "2026-03-31T22:39:37.936Z" }, + { url = "https://files.pythonhosted.org/packages/ea/7b/8ad6f16fdb82f5f7284a34b5ec48645bd575bdcd2f6f0d1644775909c486/hf_xet-1.4.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:60cf7fc43a99da0a853345cf86d23738c03983ee5249613a6305d3e57a5dca74", size = 4188162, upload-time = "2026-03-31T22:39:58.382Z" }, + { url = "https://files.pythonhosted.org/packages/1b/c4/39d6e136cbeea9ca5a23aad4b33024319222adbdc059ebcda5fc7d9d5ff4/hf_xet-1.4.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:2815a49a7a59f3e2edf0cf113ae88e8cb2ca2a221bf353fb60c609584f4884d4", size = 4424525, upload-time = "2026-03-31T22:40:00.225Z" }, + { url = "https://files.pythonhosted.org/packages/46/f2/adc32dae6bdbc367853118b9878139ac869419a4ae7ba07185dc31251b76/hf_xet-1.4.3-cp313-cp313t-win_amd64.whl", hash = "sha256:42ee323265f1e6a81b0e11094564fb7f7e0ec75b5105ffd91ae63f403a11931b", size = 3671610, upload-time = "2026-03-31T22:40:10.42Z" }, + { url = "https://files.pythonhosted.org/packages/e2/19/25d897dcc3f81953e0c2cde9ec186c7a0fee413eb0c9a7a9130d87d94d3a/hf_xet-1.4.3-cp313-cp313t-win_arm64.whl", hash = "sha256:27c976ba60079fb8217f485b9c5c7fcd21c90b0367753805f87cb9f3cdc4418a", size = 3528529, upload-time = "2026-03-31T22:40:09.106Z" }, + { url = "https://files.pythonhosted.org/packages/ec/36/3e8f85ca9fe09b8de2b2e10c63b3b3353d7dda88a0b3d426dffbe7b8313b/hf_xet-1.4.3-cp314-cp314t-macosx_10_12_x86_64.whl", hash = "sha256:5251d5ece3a81815bae9abab41cf7ddb7bcb8f56411bce0827f4a3071c92fdc6", size = 3801019, upload-time = "2026-03-31T22:39:56.651Z" }, + { url = "https://files.pythonhosted.org/packages/b5/9c/defb6cb1de28bccb7bd8d95f6e60f72a3d3fa4cb3d0329c26fb9a488bfe7/hf_xet-1.4.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:1feb0f3abeacee143367c326a128a2e2b60868ec12a36c225afb1d6c5a05e6d2", size = 3558746, upload-time = "2026-03-31T22:39:54.766Z" }, + { url = "https://files.pythonhosted.org/packages/c1/bd/8d001191893178ff8e826e46ad5299446e62b93cd164e17b0ffea08832ec/hf_xet-1.4.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:8b301fc150290ca90b4fccd079829b84bb4786747584ae08b94b4577d82fb791", size = 4207692, upload-time = "2026-03-31T22:39:46.246Z" }, + { url = "https://files.pythonhosted.org/packages/ce/48/6790b402803250e9936435613d3a78b9aaeee7973439f0918848dde58309/hf_xet-1.4.3-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:d972fbe95ddc0d3c0fc49b31a8a69f47db35c1e3699bf316421705741aab6653", size = 3986281, upload-time = "2026-03-31T22:39:44.648Z" }, + { url = "https://files.pythonhosted.org/packages/51/56/ea62552fe53db652a9099eda600b032d75554d0e86c12a73824bfedef88b/hf_xet-1.4.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:c5b48db1ee344a805a1b9bd2cda9b6b65fe77ed3787bd6e87ad5521141d317cd", size = 4187414, upload-time = "2026-03-31T22:40:04.951Z" }, + { url = "https://files.pythonhosted.org/packages/7d/f5/bc1456d4638061bea997e6d2db60a1a613d7b200e0755965ec312dc1ef79/hf_xet-1.4.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:22bdc1f5fb8b15bf2831440b91d1c9bbceeb7e10c81a12e8d75889996a5c9da8", size = 4424368, upload-time = "2026-03-31T22:40:06.347Z" }, + { url = "https://files.pythonhosted.org/packages/e4/76/ab597bae87e1f06d18d3ecb8ed7f0d3c9a37037fc32ce76233d369273c64/hf_xet-1.4.3-cp314-cp314t-win_amd64.whl", hash = "sha256:0392c79b7cf48418cd61478c1a925246cf10639f4cd9d94368d8ca1e8df9ea07", size = 3672280, upload-time = "2026-03-31T22:40:16.401Z" }, + { url = "https://files.pythonhosted.org/packages/62/05/2e462d34e23a09a74d73785dbed71cc5dbad82a72eee2ad60a72a554155d/hf_xet-1.4.3-cp314-cp314t-win_arm64.whl", hash = "sha256:681c92a07796325778a79d76c67011764ecc9042a8c3579332b61b63ae512075", size = 3528945, upload-time = "2026-03-31T22:40:14.995Z" }, + { url = "https://files.pythonhosted.org/packages/ac/9f/9c23e4a447b8f83120798f9279d0297a4d1360bdbf59ef49ebec78fe2545/hf_xet-1.4.3-cp37-abi3-macosx_10_12_x86_64.whl", hash = "sha256:d0da85329eaf196e03e90b84c2d0aca53bd4573d097a75f99609e80775f98025", size = 3805048, upload-time = "2026-03-31T22:39:53.105Z" }, + { url = "https://files.pythonhosted.org/packages/0b/f8/7aacb8e5f4a7899d39c787b5984e912e6c18b11be136ef13947d7a66d265/hf_xet-1.4.3-cp37-abi3-macosx_11_0_arm64.whl", hash = "sha256:e23717ce4186b265f69afa66e6f0069fe7efbf331546f5c313d00e123dc84583", size = 3562178, upload-time = "2026-03-31T22:39:51.295Z" }, + { url = "https://files.pythonhosted.org/packages/df/9a/a24b26dc8a65f0ecc0fe5be981a19e61e7ca963b85e062c083f3a9100529/hf_xet-1.4.3-cp37-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:fc360b70c815bf340ed56c7b8c63aacf11762a4b099b2fe2c9bd6d6068668c08", size = 4212320, upload-time = "2026-03-31T22:39:42.922Z" }, + { url = "https://files.pythonhosted.org/packages/53/60/46d493db155d2ee2801b71fb1b0fd67696359047fdd8caee2c914cc50c79/hf_xet-1.4.3-cp37-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:39f2d2e9654cd9b4319885733993807aab6de9dfbd34c42f0b78338d6617421f", size = 3991546, upload-time = "2026-03-31T22:39:41.335Z" }, + { url = "https://files.pythonhosted.org/packages/bc/f5/067363e1c96c6b17256910830d1b54099d06287e10f4ec6ec4e7e08371fc/hf_xet-1.4.3-cp37-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:49ad8a8cead2b56051aa84d7fce3e1335efe68df3cf6c058f22a65513885baac", size = 4193200, upload-time = "2026-03-31T22:40:01.936Z" }, + { url = "https://files.pythonhosted.org/packages/42/4b/53951592882d9c23080c7644542fda34a3813104e9e11fa1a7d82d419cb8/hf_xet-1.4.3-cp37-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:7716d62015477a70ea272d2d68cd7cad140f61c52ee452e133e139abfe2c17ba", size = 4429392, upload-time = "2026-03-31T22:40:03.492Z" }, + { url = "https://files.pythonhosted.org/packages/8a/21/75a6c175b4e79662ad8e62f46a40ce341d8d6b206b06b4320d07d55b188c/hf_xet-1.4.3-cp37-abi3-win_amd64.whl", hash = "sha256:6b591fcad34e272a5b02607485e4f2a1334aebf1bc6d16ce8eb1eb8978ac2021", size = 3677359, upload-time = "2026-03-31T22:40:13.619Z" }, + { url = "https://files.pythonhosted.org/packages/8a/7c/44314ecd0e89f8b2b51c9d9e5e7a60a9c1c82024ac471d415860557d3cd8/hf_xet-1.4.3-cp37-abi3-win_arm64.whl", hash = "sha256:7c2c7e20bcfcc946dc67187c203463f5e932e395845d098cc2a93f5b67ca0b47", size = 3533664, upload-time = "2026-03-31T22:40:12.152Z" }, +] + +[[package]] +name = "httpcore" +version = "1.0.9" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "certifi" }, + { name = "h11" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/06/94/82699a10bca87a5556c9c59b5963f2d039dbd239f25bc2a63907a05a14cb/httpcore-1.0.9.tar.gz", hash = "sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8", size = 85484, upload-time = "2025-04-24T22:06:22.219Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/7e/f5/f66802a942d491edb555dd61e3a9961140fd64c90bce1eafd741609d334d/httpcore-1.0.9-py3-none-any.whl", hash = "sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55", size = 78784, upload-time = "2025-04-24T22:06:20.566Z" }, +] + +[[package]] +name = "httpx" +version = "0.28.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "anyio" }, + { name = "certifi" }, + { name = "httpcore" }, + { name = "idna" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/b1/df/48c586a5fe32a0f01324ee087459e112ebb7224f646c0b5023f5e79e9956/httpx-0.28.1.tar.gz", hash = "sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc", size = 141406, upload-time = "2024-12-06T15:37:23.222Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/2a/39/e50c7c3a983047577ee07d2a9e53faf5a69493943ec3f6a384bdc792deb2/httpx-0.28.1-py3-none-any.whl", hash = "sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad", size = 73517, upload-time = "2024-12-06T15:37:21.509Z" }, +] + +[[package]] +name = "httpx-sse" +version = "0.4.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/0f/4c/751061ffa58615a32c31b2d82e8482be8dd4a89154f003147acee90f2be9/httpx_sse-0.4.3.tar.gz", hash = "sha256:9b1ed0127459a66014aec3c56bebd93da3c1bc8bb6618c8082039a44889a755d", size = 15943, upload-time = "2025-10-10T21:48:22.271Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/d2/fd/6668e5aec43ab844de6fc74927e155a3b37bf40d7c3790e49fc0406b6578/httpx_sse-0.4.3-py3-none-any.whl", hash = "sha256:0ac1c9fe3c0afad2e0ebb25a934a59f4c7823b60792691f779fad2c5568830fc", size = 8960, upload-time = "2025-10-10T21:48:21.158Z" }, +] + +[[package]] +name = "huggingface-hub" +version = "1.13.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "filelock" }, + { name = "fsspec" }, + { name = "hf-xet", marker = "platform_machine == 'AMD64' or platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'arm64' or platform_machine == 'x86_64'" }, + { name = "httpx" }, + { name = "packaging" }, + { name = "pyyaml" }, + { name = "tqdm" }, + { name = "typer" }, + { name = "typing-extensions" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/89/ff/ec7ed2eb43bd7ce8bb2233d109cc235c3e807ffe5e469dc09db261fac05e/huggingface_hub-1.13.0.tar.gz", hash = "sha256:f6df2dac5abe82ce2fe05873d10d5ff47bc677d616a2f521f4ee26db9415d9d0", size = 781788, upload-time = "2026-04-30T11:57:33.858Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/93/db/4b1cdae9460ae1f3ca020cd767f013430ce23eb1d9c890ae3a0609b38d26/huggingface_hub-1.13.0-py3-none-any.whl", hash = "sha256:e942cb50d6a08dd5306688b1ac05bda157fd2fcc88b63dae405f7bd0d3234005", size = 660643, upload-time = "2026-04-30T11:57:31.802Z" }, +] + +[[package]] +name = "idna" +version = "3.13" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/ce/cc/762dfb036166873f0059f3b7de4565e1b5bc3d6f28a414c13da27e442f99/idna-3.13.tar.gz", hash = "sha256:585ea8fe5d69b9181ec1afba340451fba6ba764af97026f92a91d4eef164a242", size = 194210, upload-time = "2026-04-22T16:42:42.314Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/5d/13/ad7d7ca3808a898b4612b6fe93cde56b53f3034dcde235acb1f0e1df24c6/idna-3.13-py3-none-any.whl", hash = "sha256:892ea0cde124a99ce773decba204c5552b69c3c67ffd5f232eb7696135bc8bb3", size = 68629, upload-time = "2026-04-22T16:42:40.909Z" }, +] + +[[package]] +name = "importlib-metadata" +version = "8.7.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "zipp" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/f3/49/3b30cad09e7771a4982d9975a8cbf64f00d4a1ececb53297f1d9a7be1b10/importlib_metadata-8.7.1.tar.gz", hash = "sha256:49fef1ae6440c182052f407c8d34a68f72efc36db9ca90dc0113398f2fdde8bb", size = 57107, upload-time = "2025-12-21T10:00:19.278Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/fa/5e/f8e9a1d23b9c20a551a8a02ea3637b4642e22c2626e3a13a9a29cdea99eb/importlib_metadata-8.7.1-py3-none-any.whl", hash = "sha256:5a1f80bf1daa489495071efbb095d75a634cf28a8bc299581244063b53176151", size = 27865, upload-time = "2025-12-21T10:00:18.329Z" }, +] + +[[package]] +name = "ipykernel" +version = "7.2.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "appnope", marker = "sys_platform == 'darwin'" }, + { name = "comm" }, + { name = "debugpy" }, + { name = "ipython" }, + { name = "jupyter-client" }, + { name = "jupyter-core" }, + { name = "matplotlib-inline" }, + { name = "nest-asyncio" }, + { name = "packaging" }, + { name = "psutil" }, + { name = "pyzmq" }, + { name = "tornado" }, + { name = "traitlets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/ca/8d/b68b728e2d06b9e0051019640a40a9eb7a88fcd82c2e1b5ce70bef5ff044/ipykernel-7.2.0.tar.gz", hash = "sha256:18ed160b6dee2cbb16e5f3575858bc19d8f1fe6046a9a680c708494ce31d909e", size = 176046, upload-time = "2026-02-06T16:43:27.403Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/82/b9/e73d5d9f405cba7706c539aa8b311b49d4c2f3d698d9c12f815231169c71/ipykernel-7.2.0-py3-none-any.whl", hash = "sha256:3bbd4420d2b3cc105cbdf3756bfc04500b1e52f090a90716851f3916c62e1661", size = 118788, upload-time = "2026-02-06T16:43:25.149Z" }, +] + +[[package]] +name = "ipython" +version = "9.13.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "colorama", marker = "sys_platform == 'win32'" }, + { name = "decorator" }, + { name = "ipython-pygments-lexers" }, + { name = "jedi" }, + { name = "matplotlib-inline" }, + { name = "pexpect", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" }, + { name = "prompt-toolkit" }, + { name = "psutil" }, + { name = "pygments" }, + { name = "stack-data" }, + { name = "traitlets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/cd/c4/87cda5842cf5c31837c06ddb588e11c3c35d8ece89b7a0108c06b8c9b00a/ipython-9.13.0.tar.gz", hash = "sha256:7e834b6afc99f020e3f05966ced34792f40267d64cb1ea9043886dab0dde5967", size = 4430549, upload-time = "2026-04-24T12:24:55.221Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/b9/86/3060e8029b7cc505cce9a0137431dda81d0a3fde93a8f0f50ee0bf37a795/ipython-9.13.0-py3-none-any.whl", hash = "sha256:57f9d4639e20818d328d287c7b549af3d05f12486ea8f2e7f73e52a36ec4d201", size = 627274, upload-time = "2026-04-24T12:24:53.038Z" }, +] + +[[package]] +name = "ipython-pygments-lexers" +version = "1.1.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "pygments" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/ef/4c/5dd1d8af08107f88c7f741ead7a40854b8ac24ddf9ae850afbcf698aa552/ipython_pygments_lexers-1.1.1.tar.gz", hash = "sha256:09c0138009e56b6854f9535736f4171d855c8c08a563a0dcd8022f78355c7e81", size = 8393, upload-time = "2025-01-17T11:24:34.505Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/d9/33/1f075bf72b0b747cb3288d011319aaf64083cf2efef8354174e3ed4540e2/ipython_pygments_lexers-1.1.1-py3-none-any.whl", hash = "sha256:a9462224a505ade19a605f71f8fa63c2048833ce50abc86768a0d81d876dc81c", size = 8074, upload-time = "2025-01-17T11:24:33.271Z" }, +] + +[[package]] +name = "ipywidgets" +version = "8.1.8" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "comm" }, + { name = "ipython" }, + { name = "jupyterlab-widgets" }, + { name = "traitlets" }, + { name = "widgetsnbextension" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/4c/ae/c5ce1edc1afe042eadb445e95b0671b03cee61895264357956e61c0d2ac0/ipywidgets-8.1.8.tar.gz", hash = "sha256:61f969306b95f85fba6b6986b7fe45d73124d1d9e3023a8068710d47a22ea668", size = 116739, upload-time = "2025-11-01T21:18:12.393Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/56/6d/0d9848617b9f753b87f214f1c682592f7ca42de085f564352f10f0843026/ipywidgets-8.1.8-py3-none-any.whl", hash = "sha256:ecaca67aed704a338f88f67b1181b58f821ab5dc89c1f0f5ef99db43c1c2921e", size = 139808, upload-time = "2025-11-01T21:18:10.956Z" }, +] + +[[package]] +name = "isoduration" +version = "20.11.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "arrow" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/7c/1a/3c8edc664e06e6bd06cce40c6b22da5f1429aa4224d0c590f3be21c91ead/isoduration-20.11.0.tar.gz", hash = "sha256:ac2f9015137935279eac671f94f89eb00584f940f5dc49462a0c4ee692ba1bd9", size = 11649, upload-time = "2020-11-01T11:00:00.312Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/7b/55/e5326141505c5d5e34c5e0935d2908a74e4561eca44108fbfb9c13d2911a/isoduration-20.11.0-py3-none-any.whl", hash = "sha256:b2904c2a4228c3d44f409c8ae8e2370eb21a26f7ac2ec5446df141dde3452042", size = 11321, upload-time = "2020-11-01T10:59:58.02Z" }, +] + +[[package]] +name = "jedi" +version = "0.20.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "parso" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/46/b7/a3635f6a2d7cf5b5dd98064fc1d5fbbafcb25477bcea204a3a92145d158b/jedi-0.20.0.tar.gz", hash = "sha256:c3f4ccbd276696f4b19c54618d4fb18f9fc24b0aef02acf704b23f487daa1011", size = 3119416, upload-time = "2026-05-01T23:38:47.814Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/9a/93/242e2eab5fe682ffcb8b0084bde703a41d51e17ee0f3a31ff0d9d813620a/jedi-0.20.0-py2.py3-none-any.whl", hash = "sha256:7bdd9c2634f56713299976f4cbd59cb3fa92165cc5e05ea811fb253480728b67", size = 4884812, upload-time = "2026-05-01T23:38:43.919Z" }, +] + +[[package]] +name = "jinja2" +version = "3.1.6" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "markupsafe" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/df/bf/f7da0350254c0ed7c72f3e33cef02e048281fec7ecec5f032d4aac52226b/jinja2-3.1.6.tar.gz", hash = "sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d", size = 245115, upload-time = "2025-03-05T20:05:02.478Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/62/a1/3d680cbfd5f4b8f15abc1d571870c5fc3e594bb582bc3b64ea099db13e56/jinja2-3.1.6-py3-none-any.whl", hash = "sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67", size = 134899, upload-time = "2025-03-05T20:05:00.369Z" }, +] + +[[package]] +name = "joblib" +version = "1.5.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/41/f2/d34e8b3a08a9cc79a50b2208a93dce981fe615b64d5a4d4abee421d898df/joblib-1.5.3.tar.gz", hash = "sha256:8561a3269e6801106863fd0d6d84bb737be9e7631e33aaed3fb9ce5953688da3", size = 331603, upload-time = "2025-12-15T08:41:46.427Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/7b/91/984aca2ec129e2757d1e4e3c81c3fcda9d0f85b74670a094cc443d9ee949/joblib-1.5.3-py3-none-any.whl", hash = "sha256:5fc3c5039fc5ca8c0276333a188bbd59d6b7ab37fe6632daa76bc7f9ec18e713", size = 309071, upload-time = "2025-12-15T08:41:44.973Z" }, +] + +[[package]] +name = "json5" +version = "0.14.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/9c/4b/6f8906aaf67d501e259b0adab4d312945bb7211e8b8d4dcc77c92320edaa/json5-0.14.0.tar.gz", hash = "sha256:b3f492fad9f6cdbced8b7d40b28b9b1c9701c5f561bef0d33b81c2ff433fefcb", size = 52656, upload-time = "2026-03-27T22:50:48.108Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/b8/42/cf027b4ac873b076189d935b135397675dac80cb29acb13e1ab86ad6c631/json5-0.14.0-py3-none-any.whl", hash = "sha256:56cf861bab076b1178eb8c92e1311d273a9b9acea2ccc82c276abf839ebaef3a", size = 36271, upload-time = "2026-03-27T22:50:47.073Z" }, +] + +[[package]] +name = "jsonpointer" +version = "3.1.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/18/c7/af399a2e7a67fd18d63c40c5e62d3af4e67b836a2107468b6a5ea24c4304/jsonpointer-3.1.1.tar.gz", hash = "sha256:0b801c7db33a904024f6004d526dcc53bbb8a4a0f4e32bfd10beadf60adf1900", size = 9068, upload-time = "2026-03-23T22:32:32.458Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/9e/6a/a83720e953b1682d2d109d3c2dbb0bc9bf28cc1cbc205be4ef4be5da709d/jsonpointer-3.1.1-py3-none-any.whl", hash = "sha256:8ff8b95779d071ba472cf5bc913028df06031797532f08a7d5b602d8b2a488ca", size = 7659, upload-time = "2026-03-23T22:32:31.568Z" }, +] + +[[package]] +name = "jsonschema" +version = "4.26.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "attrs" }, + { name = "jsonschema-specifications" }, + { name = "referencing" }, + { name = "rpds-py" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/b3/fc/e067678238fa451312d4c62bf6e6cf5ec56375422aee02f9cb5f909b3047/jsonschema-4.26.0.tar.gz", hash = "sha256:0c26707e2efad8aa1bfc5b7ce170f3fccc2e4918ff85989ba9ffa9facb2be326", size = 366583, upload-time = "2026-01-07T13:41:07.246Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/69/90/f63fb5873511e014207a475e2bb4e8b2e570d655b00ac19a9a0ca0a385ee/jsonschema-4.26.0-py3-none-any.whl", hash = "sha256:d489f15263b8d200f8387e64b4c3a75f06629559fb73deb8fdfb525f2dab50ce", size = 90630, upload-time = "2026-01-07T13:41:05.306Z" }, +] + +[package.optional-dependencies] +format-nongpl = [ + { name = "fqdn" }, + { name = "idna" }, + { name = "isoduration" }, + { name = "jsonpointer" }, + { name = "rfc3339-validator" }, + { name = "rfc3986-validator" }, + { name = "rfc3987-syntax" }, + { name = "uri-template" }, + { name = "webcolors" }, +] + +[[package]] +name = "jsonschema-specifications" +version = "2025.9.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "referencing" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/19/74/a633ee74eb36c44aa6d1095e7cc5569bebf04342ee146178e2d36600708b/jsonschema_specifications-2025.9.1.tar.gz", hash = "sha256:b540987f239e745613c7a9176f3edb72b832a4ac465cf02712288397832b5e8d", size = 32855, upload-time = "2025-09-08T01:34:59.186Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/41/45/1a4ed80516f02155c51f51e8cedb3c1902296743db0bbc66608a0db2814f/jsonschema_specifications-2025.9.1-py3-none-any.whl", hash = "sha256:98802fee3a11ee76ecaca44429fda8a41bff98b00a0f2838151b113f210cc6fe", size = 18437, upload-time = "2025-09-08T01:34:57.871Z" }, +] + +[[package]] +name = "jupyter" +version = "1.1.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "ipykernel" }, + { name = "ipywidgets" }, + { name = "jupyter-console" }, + { name = "jupyterlab" }, + { name = "nbconvert" }, + { name = "notebook" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/58/f3/af28ea964ab8bc1e472dba2e82627d36d470c51f5cd38c37502eeffaa25e/jupyter-1.1.1.tar.gz", hash = "sha256:d55467bceabdea49d7e3624af7e33d59c37fff53ed3a350e1ac957bed731de7a", size = 5714959, upload-time = "2024-08-30T07:15:48.299Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/38/64/285f20a31679bf547b75602702f7800e74dbabae36ef324f716c02804753/jupyter-1.1.1-py2.py3-none-any.whl", hash = "sha256:7a59533c22af65439b24bbe60373a4e95af8f16ac65a6c00820ad378e3f7cc83", size = 2657, upload-time = "2024-08-30T07:15:47.045Z" }, +] + +[[package]] +name = "jupyter-client" +version = "8.8.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "jupyter-core" }, + { name = "python-dateutil" }, + { name = "pyzmq" }, + { name = "tornado" }, + { name = "traitlets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/05/e4/ba649102a3bc3fbca54e7239fb924fd434c766f855693d86de0b1f2bec81/jupyter_client-8.8.0.tar.gz", hash = "sha256:d556811419a4f2d96c869af34e854e3f059b7cc2d6d01a9cd9c85c267691be3e", size = 348020, upload-time = "2026-01-08T13:55:47.938Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/2d/0b/ceb7694d864abc0a047649aec263878acb9f792e1fec3e676f22dc9015e3/jupyter_client-8.8.0-py3-none-any.whl", hash = "sha256:f93a5b99c5e23a507b773d3a1136bd6e16c67883ccdbd9a829b0bbdb98cd7d7a", size = 107371, upload-time = "2026-01-08T13:55:45.562Z" }, +] + +[[package]] +name = "jupyter-collaboration" +version = "4.3.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "jupyter-collaboration-ui" }, + { name = "jupyter-docprovider" }, + { name = "jupyter-server-ydoc" }, + { name = "jupyterlab" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/7b/b7/86b8f2aca6a554668c55c88401ba9ba6e355fdcc7d71cb3dd0bec85c330e/jupyter_collaboration-4.3.0.tar.gz", hash = "sha256:6ef03664fdda0fddf47d2904db29a659c8ef4d2f307080b89cdef72e7e7b24c9", size = 3734, upload-time = "2026-03-31T10:08:36.166Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e4/1b/b518e55344a2bb787a8025d6cb51353b91d7af07e3756cc06b2ecb88098d/jupyter_collaboration-4.3.0-py3-none-any.whl", hash = "sha256:6dd3d7129e95a04e11f1fd22915f167023bebd4badc4cf1b71f73fb2690f5648", size = 4751, upload-time = "2026-03-31T10:08:34.383Z" }, +] + +[[package]] +name = "jupyter-collaboration-ui" +version = "2.3.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/0a/cf/bbf8f4b6f27a91c5addd3e8371bb97939a1f9bf0bf988d9961532f9c26a6/jupyter_collaboration_ui-2.3.0.tar.gz", hash = "sha256:835e818614eb39645f2f583a57b2246d8d1ecff4ffd2d481ab4a6f6b2c45b997", size = 77339, upload-time = "2026-03-31T10:08:13.898Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/36/63/a1f297c16092e44a7e143fe4f414317057b8e2acf33ce3c182cd2b08c291/jupyter_collaboration_ui-2.3.0-py3-none-any.whl", hash = "sha256:e8b9f026615e9ed448b1518bb74f044e15519ad5282976fa54c002f2572139c0", size = 46498, upload-time = "2026-03-31T10:08:12.167Z" }, +] + +[[package]] +name = "jupyter-console" +version = "6.6.3" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "ipykernel" }, + { name = "ipython" }, + { name = "jupyter-client" }, + { name = "jupyter-core" }, + { name = "prompt-toolkit" }, + { name = "pygments" }, + { name = "pyzmq" }, + { name = "traitlets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/bd/2d/e2fd31e2fc41c14e2bcb6c976ab732597e907523f6b2420305f9fc7fdbdb/jupyter_console-6.6.3.tar.gz", hash = "sha256:566a4bf31c87adbfadf22cdf846e3069b59a71ed5da71d6ba4d8aaad14a53539", size = 34363, upload-time = "2023-03-06T14:13:31.02Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ca/77/71d78d58f15c22db16328a476426f7ac4a60d3a5a7ba3b9627ee2f7903d4/jupyter_console-6.6.3-py3-none-any.whl", hash = "sha256:309d33409fcc92ffdad25f0bcdf9a4a9daa61b6f341177570fdac03de5352485", size = 24510, upload-time = "2023-03-06T14:13:28.229Z" }, +] + +[[package]] +name = "jupyter-core" +version = "5.9.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "platformdirs" }, + { name = "traitlets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/02/49/9d1284d0dc65e2c757b74c6687b6d319b02f822ad039e5c512df9194d9dd/jupyter_core-5.9.1.tar.gz", hash = "sha256:4d09aaff303b9566c3ce657f580bd089ff5c91f5f89cf7d8846c3cdf465b5508", size = 89814, upload-time = "2025-10-16T19:19:18.444Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e7/e7/80988e32bf6f73919a113473a604f5a8f09094de312b9d52b79c2df7612b/jupyter_core-5.9.1-py3-none-any.whl", hash = "sha256:ebf87fdc6073d142e114c72c9e29a9d7ca03fad818c5d300ce2adc1fb0743407", size = 29032, upload-time = "2025-10-16T19:19:16.783Z" }, +] + +[[package]] +name = "jupyter-docprovider" +version = "2.3.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/d8/d3/45571d4a7a4a2024750e582c17c1311052476dab4a2a02dbd68c7b5d0df0/jupyter_docprovider-2.3.0.tar.gz", hash = "sha256:c09808e15e93f2ea4ff194a18c7a8c8f83d81049fa91b09de24b09adeac9d572", size = 49964, upload-time = "2026-03-31T10:08:25.383Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/74/4a/af30f008f176d38c183a107b6556ba36f9e1a9970d5f730739bed4663c4a/jupyter_docprovider-2.3.0-py3-none-any.whl", hash = "sha256:44f9a8bfa47f069154e111b869f71c0e4297f201c56fa15ff308fe4b64c50f43", size = 35575, upload-time = "2026-03-31T10:08:23.534Z" }, +] + +[[package]] +name = "jupyter-events" +version = "0.12.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "jsonschema", extra = ["format-nongpl"] }, + { name = "packaging" }, + { name = "python-json-logger" }, + { name = "pyyaml" }, + { name = "referencing" }, + { name = "rfc3339-validator" }, + { name = "rfc3986-validator" }, + { name = "traitlets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/18/f8/475c4241b2b75af0deaae453ed003c6c851766dbc44d332d8baf245dc931/jupyter_events-0.12.1.tar.gz", hash = "sha256:faff25f77218335752f35f23c5fe6e4a392a7bd99a5939ccb9b8fbf594636cf3", size = 62854, upload-time = "2026-04-20T23:17:50.66Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/eb/6c/6fcde0c8f616ed360ffd3587f7db9e225a7e62b583a04494d2f069cf64ea/jupyter_events-0.12.1-py3-none-any.whl", hash = "sha256:c366585253f537a627da52fa7ca7410c5b5301fe893f511e7b077c2d93ec8bcf", size = 19512, upload-time = "2026-04-20T23:17:48.927Z" }, +] + +[[package]] +name = "jupyter-kernel-client" +version = "0.9.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "jupyter-client" }, + { name = "jupyter-core" }, + { name = "jupyter-mimetypes" }, + { name = "requests" }, + { name = "traitlets" }, + { name = "typing-extensions" }, + { name = "websocket-client" }, +] +wheels = [ + { url = "https://files.pythonhosted.org/packages/7a/68/287315ba355aa93bda2e344de5febc45e6de1b47d8f4a5b69400b24cfdfd/jupyter_kernel_client-0.9.0-py3-none-any.whl", hash = "sha256:77acb8f2f738d97625d6bd01ee8cf21c4d59790b7ba464108712db3870416f20", size = 40097, upload-time = "2026-02-11T06:42:05.133Z" }, +] + +[[package]] +name = "jupyter-lsp" +version = "2.3.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "jupyter-server" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/36/ff/1e4a61f5170a9a1d978f3ac3872449de6c01fc71eaf89657824c878b1549/jupyter_lsp-2.3.1.tar.gz", hash = "sha256:fdf8a4aa7d85813976d6e29e95e6a2c8f752701f926f2715305249a3829805a6", size = 55677, upload-time = "2026-04-02T08:10:06.749Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/23/e8/9d61dcbd1dce8ef418f06befd4ac084b4720429c26b0b1222bc218685eff/jupyter_lsp-2.3.1-py3-none-any.whl", hash = "sha256:71b954d834e85ff3096400554f2eefaf7fe37053036f9a782b0f7c5e42dadb81", size = 77513, upload-time = "2026-04-02T08:10:01.753Z" }, +] + +[[package]] +name = "jupyter-mcp-server" +version = "1.0.2" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "click" }, + { name = "fastapi" }, + { name = "jupyter-kernel-client" }, + { name = "jupyter-mcp-tools" }, + { name = "jupyter-nbmodel-client" }, + { name = "jupyter-server" }, + { name = "jupyter-server-client" }, + { name = "jupyter-server-nbmodel" }, + { name = "mcp", extra = ["cli"] }, + { name = "opentelemetry-api" }, + { name = "opentelemetry-sdk" }, + { name = "pydantic" }, + { name = "tornado" }, + { name = "traitlets" }, + { name = "uvicorn" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/55/ae/7843fe02bb57c5a2928fa830b01779a3c2395ca382e91e690aac987fa054/jupyter_mcp_server-1.0.2.tar.gz", hash = "sha256:a1e1b4816d4cbc53944a6f76385e0c3b6b4d8aa5c9f855cbce153fad1b18c3fe", size = 80102, upload-time = "2026-04-27T06:08:37.559Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/0e/5e/f306a728502f2d26988c76a882121dd1bb1a87c59e6db8b096a9a7b35903/jupyter_mcp_server-1.0.2-py3-none-any.whl", hash = "sha256:6be3c923fc6469094856e329dd593e4cbd4938cedb068b65697b3b1bc8543538", size = 111133, upload-time = "2026-04-27T06:08:36.01Z" }, +] + +[[package]] +name = "jupyter-mcp-tools" +version = "0.1.6" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "aiohttp" }, + { name = "jupyter-server" }, +] +wheels = [ + { url = "https://files.pythonhosted.org/packages/a1/f4/28e9cbdd05d3146d5adedc474f64a2d669c21b0e672d09b0885a1cc7c85a/jupyter_mcp_tools-0.1.6-py3-none-any.whl", hash = "sha256:45cb18658d5853a62faccd5e8ea17becc3a1850482a43c49a9e4bb2c854effd7", size = 45664, upload-time = "2026-01-08T15:32:10.559Z" }, +] + +[[package]] +name = "jupyter-mimetypes" +version = "0.2.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "pyarrow" }, + { name = "typing-extensions" }, +] +wheels = [ + { url = "https://files.pythonhosted.org/packages/72/45/cb4671e13fed39f721066ad1a00714d4b607982b8d3e97a25f836198d1df/jupyter_mimetypes-0.2.0-py3-none-any.whl", hash = "sha256:e6dcd989258e3fc944365b656d9173191517e0e393bd878e97ce500e5b388527", size = 16724, upload-time = "2025-08-10T18:18:27.309Z" }, +] + +[[package]] +name = "jupyter-nbmodel-client" +version = "0.14.7" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "jupyter-ydoc" }, + { name = "nbformat" }, + { name = "pycrdt" }, + { name = "requests" }, + { name = "websockets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/c3/f9/bf5a0ba321a227ba4c94bafa3d3950f9e773658773a81e379381b8687e25/jupyter_nbmodel_client-0.14.7.tar.gz", hash = "sha256:c9ff959ea26f5b2876271dfe37882c4674551e0e2aafc7633a4ebc57c0260563", size = 25909, upload-time = "2026-04-25T10:40:46.108Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/3d/61/5d6ada9177f164f3ca0394af899d09a7bb82b6ba9bb5f1d559a9d9f53758/jupyter_nbmodel_client-0.14.7-py3-none-any.whl", hash = "sha256:ff9371378608dd46f5cb58e394493aa6bde4efcbabbcb988fc331f55b5b7cef3", size = 23025, upload-time = "2026-04-25T10:40:44.046Z" }, +] + +[[package]] +name = "jupyter-server" +version = "2.17.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "anyio" }, + { name = "argon2-cffi" }, + { name = "jinja2" }, + { name = "jupyter-client" }, + { name = "jupyter-core" }, + { name = "jupyter-events" }, + { name = "jupyter-server-terminals" }, + { name = "nbconvert" }, + { name = "nbformat" }, + { name = "packaging" }, + { name = "prometheus-client" }, + { name = "pywinpty", marker = "os_name == 'nt'" }, + { name = "pyzmq" }, + { name = "send2trash" }, + { name = "terminado" }, + { name = "tornado" }, + { name = "traitlets" }, + { name = "websocket-client" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/5b/ac/e040ec363d7b6b1f11304cc9f209dac4517ece5d5e01821366b924a64a50/jupyter_server-2.17.0.tar.gz", hash = "sha256:c38ea898566964c888b4772ae1ed58eca84592e88251d2cfc4d171f81f7e99d5", size = 731949, upload-time = "2025-08-21T14:42:54.042Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/92/80/a24767e6ca280f5a49525d987bf3e4d7552bf67c8be07e8ccf20271f8568/jupyter_server-2.17.0-py3-none-any.whl", hash = "sha256:e8cb9c7db4251f51ed307e329b81b72ccf2056ff82d50524debde1ee1870e13f", size = 388221, upload-time = "2025-08-21T14:42:52.034Z" }, +] + +[[package]] +name = "jupyter-server-client" +version = "0.1.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "pydantic" }, + { name = "requests" }, +] +wheels = [ + { url = "https://files.pythonhosted.org/packages/0f/2e/d22899abfdbc6fdd33860152f2108e6f377b11083e951bb67e6472e0247b/jupyter_server_client-0.1.1-py3-none-any.whl", hash = "sha256:5fc28099b95ea4b02e0bc85760507701c4c4315801897129e4b6c606582cf025", size = 19683, upload-time = "2025-10-22T11:19:29.907Z" }, +] + +[[package]] +name = "jupyter-server-fileid" +version = "0.9.3" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "jupyter-events" }, + { name = "jupyter-server" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/0d/eb/7c2c09454bbf66b3727ba8c431d16159d642c0eb1aa179412a4f7af721cf/jupyter_server_fileid-0.9.3.tar.gz", hash = "sha256:521608bb87f606a8637fcbdce2f3d24a8b3cc89d2eef61751cb40e468d4e54be", size = 54959, upload-time = "2024-09-06T07:18:40.412Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/54/d6/5e5bca083664b1dd368e261107cbe2d350e3bdc62bdba8720fdbb9b9db39/jupyter_server_fileid-0.9.3-py3-none-any.whl", hash = "sha256:f73c01c19f90005d3fff93607b91b4955ba4e1dccdde9bfe8026646f94053791", size = 16922, upload-time = "2024-09-06T07:18:38.445Z" }, +] + +[[package]] +name = "jupyter-server-nbmodel" +version = "0.1.1a4" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "jupyter-client" }, + { name = "jupyter-server" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/0a/2a/1d13aa10cd573f6b823acc833f6f4cf6d1c2bd3b201f066496b78f964ed6/jupyter_server_nbmodel-0.1.1a4.tar.gz", hash = "sha256:ee072daa1e6b09db53f20e8f2d180d4f5e68dbff40003ad14dddfea8addf5cc3", size = 207265, upload-time = "2025-10-30T15:12:02.073Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/5e/70/23edf6756dd2ae7c81ce5c2add66d986674d58072192152b80d417efbc4d/jupyter_server_nbmodel-0.1.1a4-py3-none-any.whl", hash = "sha256:f1c40906aaf98d6b66c3c9d5ebd6cad1b278d00c09d0a9227b0c15d972ec399a", size = 35316, upload-time = "2025-10-30T15:11:59.987Z" }, +] + +[[package]] +name = "jupyter-server-terminals" +version = "0.5.4" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "pywinpty", marker = "os_name == 'nt'" }, + { name = "terminado" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/f4/a7/bcd0a9b0cbba88986fe944aaaf91bfda603e5a50bda8ed15123f381a3b2f/jupyter_server_terminals-0.5.4.tar.gz", hash = "sha256:bbda128ed41d0be9020349f9f1f2a4ab9952a73ed5f5ac9f1419794761fb87f5", size = 31770, upload-time = "2026-01-14T16:53:20.213Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/d1/2d/6674563f71c6320841fc300911a55143925112a72a883e2ca71fba4c618d/jupyter_server_terminals-0.5.4-py3-none-any.whl", hash = "sha256:55be353fc74a80bc7f3b20e6be50a55a61cd525626f578dcb66a5708e2007d14", size = 13704, upload-time = "2026-01-14T16:53:18.738Z" }, +] + +[[package]] +name = "jupyter-server-ydoc" +version = "2.3.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "jsonschema" }, + { name = "jupyter-events" }, + { name = "jupyter-server" }, + { name = "jupyter-server-fileid" }, + { name = "jupyter-ydoc" }, + { name = "pycrdt" }, + { name = "pycrdt-websocket" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/f1/88/f81afcbd7cfca28c4d04086360a623319220f46a87f3206c1672f0411441/jupyter_server_ydoc-2.3.0.tar.gz", hash = "sha256:36f311491e9f289f461fcdf26afb9b72cdf0eac3ceed0c0cbc8ec43afc8efebc", size = 32103, upload-time = "2026-03-31T10:08:02.176Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/cd/73/18007b4fad0813a039b0c63c40b2f34f0f65f278c1d1670a323ff2d18638/jupyter_server_ydoc-2.3.0-py3-none-any.whl", hash = "sha256:888c4092592585f80d34f81e093aee4ce1b9d2e2601efb9e65eb5bdd23893a79", size = 33275, upload-time = "2026-03-31T10:08:00.239Z" }, +] + +[[package]] +name = "jupyter-ydoc" +version = "3.4.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "anyio" }, + { name = "pycrdt" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/22/f7/400e8ee54adb2396f7a70a85a8951bc696303a9bd413c5e35cc6c0c5c214/jupyter_ydoc-3.4.1.tar.gz", hash = "sha256:fb31f0e7033b8a5bf8920334e2b3dd9bdaba3052de3164de28e14525fd0bc4d8", size = 973504, upload-time = "2026-04-14T12:14:19.967Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/06/ff/15eca734d96277895573026dff14edd63d9d1709c02363f5902ea9767d71/jupyter_ydoc-3.4.1-py3-none-any.whl", hash = "sha256:848e5a9f37403846b7554b87d71495a38b6249579af64b1bde8de650b2e49d65", size = 14534, upload-time = "2026-04-14T12:14:18.439Z" }, +] + +[[package]] +name = "jupyterlab" +version = "4.5.7" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "async-lru" }, + { name = "httpx" }, + { name = "ipykernel" }, + { name = "jinja2" }, + { name = "jupyter-core" }, + { name = "jupyter-lsp" }, + { name = "jupyter-server" }, + { name = "jupyterlab-server" }, + { name = "notebook-shim" }, + { name = "packaging" }, + { name = "setuptools" }, + { name = "tornado" }, + { name = "traitlets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/2b/22/8440ec827762146e7cdecf04335bd348795899d29dc6ae82238707353a2c/jupyterlab-4.5.7.tar.gz", hash = "sha256:55a9822c4754da305f41e113452c68383e214dcf96de760146af89ce5d5117b0", size = 23992763, upload-time = "2026-04-29T16:43:51.328Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/3d/aa/537b8f7d80e799af19af35fb3ddfc970b951088a13c57dd9387dcfbb7f61/jupyterlab-4.5.7-py3-none-any.whl", hash = "sha256:fba4cb0e2c44a52859669d8c98b45de029d5e515f8407bf8534d2a8fc5f0964d", size = 12450123, upload-time = "2026-04-29T16:43:46.639Z" }, +] + +[[package]] +name = "jupyterlab-pygments" +version = "0.3.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/90/51/9187be60d989df97f5f0aba133fa54e7300f17616e065d1ada7d7646b6d6/jupyterlab_pygments-0.3.0.tar.gz", hash = "sha256:721aca4d9029252b11cfa9d185e5b5af4d54772bb8072f9b7036f4170054d35d", size = 512900, upload-time = "2023-11-23T09:26:37.44Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/b1/dd/ead9d8ea85bf202d90cc513b533f9c363121c7792674f78e0d8a854b63b4/jupyterlab_pygments-0.3.0-py3-none-any.whl", hash = "sha256:841a89020971da1d8693f1a99997aefc5dc424bb1b251fd6322462a1b8842780", size = 15884, upload-time = "2023-11-23T09:26:34.325Z" }, +] + +[[package]] +name = "jupyterlab-server" +version = "2.28.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "babel" }, + { name = "jinja2" }, + { name = "json5" }, + { name = "jsonschema" }, + { name = "jupyter-server" }, + { name = "packaging" }, + { name = "requests" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/d6/2c/90153f189e421e93c4bb4f9e3f59802a1f01abd2ac5cf40b152d7f735232/jupyterlab_server-2.28.0.tar.gz", hash = "sha256:35baa81898b15f93573e2deca50d11ac0ae407ebb688299d3a5213265033712c", size = 76996, upload-time = "2025-10-22T13:59:18.37Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e0/07/a000fe835f76b7e1143242ab1122e6362ef1c03f23f83a045c38859c2ae0/jupyterlab_server-2.28.0-py3-none-any.whl", hash = "sha256:e4355b148fdcf34d312bbbc80f22467d6d20460e8b8736bf235577dd18506968", size = 59830, upload-time = "2025-10-22T13:59:16.767Z" }, +] + +[[package]] +name = "jupyterlab-widgets" +version = "3.0.16" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/26/2d/ef58fed122b268c69c0aa099da20bc67657cdfb2e222688d5731bd5b971d/jupyterlab_widgets-3.0.16.tar.gz", hash = "sha256:423da05071d55cf27a9e602216d35a3a65a3e41cdf9c5d3b643b814ce38c19e0", size = 897423, upload-time = "2025-11-01T21:11:29.724Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ab/b5/36c712098e6191d1b4e349304ef73a8d06aed77e56ceaac8c0a306c7bda1/jupyterlab_widgets-3.0.16-py3-none-any.whl", hash = "sha256:45fa36d9c6422cf2559198e4db481aa243c7a32d9926b500781c830c80f7ecf8", size = 914926, upload-time = "2025-11-01T21:11:28.008Z" }, +] + +[[package]] +name = "kiwisolver" +version = "1.5.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/d0/67/9c61eccb13f0bdca9307614e782fec49ffdde0f7a2314935d489fa93cd9c/kiwisolver-1.5.0.tar.gz", hash = "sha256:d4193f3d9dc3f6f79aaed0e5637f45d98850ebf01f7ca20e69457f3e8946b66a", size = 103482, upload-time = "2026-03-09T13:15:53.382Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/9d/69/024d6711d5ba575aa65d5538042e99964104e97fa153a9f10bc369182bc2/kiwisolver-1.5.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:fd40bb9cd0891c4c3cb1ddf83f8bbfa15731a248fdc8162669405451e2724b09", size = 123166, upload-time = "2026-03-09T13:13:48.032Z" }, + { url = "https://files.pythonhosted.org/packages/ce/48/adbb40df306f587054a348831220812b9b1d787aff714cfbc8556e38fccd/kiwisolver-1.5.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:c0e1403fd7c26d77c1f03e096dc58a5c726503fa0db0456678b8668f76f521e3", size = 66395, upload-time = "2026-03-09T13:13:49.365Z" }, + { url = "https://files.pythonhosted.org/packages/a8/3a/d0a972b34e1c63e2409413104216cd1caa02c5a37cb668d1687d466c1c45/kiwisolver-1.5.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:dda366d548e89a90d88a86c692377d18d8bd64b39c1fb2b92cb31370e2896bbd", size = 64065, upload-time = "2026-03-09T13:13:50.562Z" }, + { url = "https://files.pythonhosted.org/packages/2b/0a/7b98e1e119878a27ba8618ca1e18b14f992ff1eda40f47bccccf4de44121/kiwisolver-1.5.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:332b4f0145c30b5f5ad9374881133e5aa64320428a57c2c2b61e9d891a51c2f3", size = 1477903, upload-time = "2026-03-09T13:13:52.084Z" }, + { url = "https://files.pythonhosted.org/packages/18/d8/55638d89ffd27799d5cc3d8aa28e12f4ce7a64d67b285114dbedc8ea4136/kiwisolver-1.5.0-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0c50b89ffd3e1a911c69a1dd3de7173c0cd10b130f56222e57898683841e4f96", size = 1278751, upload-time = "2026-03-09T13:13:54.673Z" }, + { url = "https://files.pythonhosted.org/packages/b8/97/b4c8d0d18421ecceba20ad8701358453b88e32414e6f6950b5a4bad54e65/kiwisolver-1.5.0-cp313-cp313-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:4db576bb8c3ef9365f8b40fe0f671644de6736ae2c27a2c62d7d8a1b4329f099", size = 1296793, upload-time = "2026-03-09T13:13:56.287Z" }, + { url = "https://files.pythonhosted.org/packages/c4/10/f862f94b6389d8957448ec9df59450b81bec4abb318805375c401a1e6892/kiwisolver-1.5.0-cp313-cp313-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:0b85aad90cea8ac6797a53b5d5f2e967334fa4d1149f031c4537569972596cb8", size = 1346041, upload-time = "2026-03-09T13:13:58.269Z" }, + { url = "https://files.pythonhosted.org/packages/a3/6a/f1650af35821eaf09de398ec0bc2aefc8f211f0cda50204c9f1673741ba9/kiwisolver-1.5.0-cp313-cp313-manylinux_2_39_riscv64.whl", hash = "sha256:d36ca54cb4c6c4686f7cbb7b817f66f5911c12ddb519450bbe86707155028f87", size = 987292, upload-time = "2026-03-09T13:13:59.871Z" }, + { url = "https://files.pythonhosted.org/packages/de/19/d7fb82984b9238115fe629c915007be608ebd23dc8629703d917dbfaffd4/kiwisolver-1.5.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:38f4a703656f493b0ad185211ccfca7f0386120f022066b018eb5296d8613e23", size = 2227865, upload-time = "2026-03-09T13:14:01.401Z" }, + { url = "https://files.pythonhosted.org/packages/7f/b9/46b7f386589fd222dac9e9de9c956ce5bcefe2ee73b4e79891381dda8654/kiwisolver-1.5.0-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:3ac2360e93cb41be81121755c6462cff3beaa9967188c866e5fce5cf13170859", size = 2324369, upload-time = "2026-03-09T13:14:02.972Z" }, + { url = "https://files.pythonhosted.org/packages/92/8b/95e237cf3d9c642960153c769ddcbe278f182c8affb20cecc1cc983e7cc5/kiwisolver-1.5.0-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:c95cab08d1965db3d84a121f1c7ce7479bdd4072c9b3dafd8fecce48a2e6b902", size = 1977989, upload-time = "2026-03-09T13:14:04.503Z" }, + { url = "https://files.pythonhosted.org/packages/1b/95/980c9df53501892784997820136c01f62bc1865e31b82b9560f980c0e649/kiwisolver-1.5.0-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:fc20894c3d21194d8041a28b65622d5b86db786da6e3cfe73f0c762951a61167", size = 2491645, upload-time = "2026-03-09T13:14:06.106Z" }, + { url = "https://files.pythonhosted.org/packages/cb/32/900647fd0840abebe1561792c6b31e6a7c0e278fc3973d30572a965ca14c/kiwisolver-1.5.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:7a32f72973f0f950c1920475d5c5ea3d971b81b6f0ec53b8d0a956cc965f22e0", size = 2295237, upload-time = "2026-03-09T13:14:08.891Z" }, + { url = "https://files.pythonhosted.org/packages/be/8a/be60e3bbcf513cc5a50f4a3e88e1dcecebb79c1ad607a7222877becaa101/kiwisolver-1.5.0-cp313-cp313-win_amd64.whl", hash = "sha256:0bf3acf1419fa93064a4c2189ac0b58e3be7872bf6ee6177b0d4c63dc4cea276", size = 73573, upload-time = "2026-03-09T13:14:12.327Z" }, + { url = "https://files.pythonhosted.org/packages/4d/d2/64be2e429eb4fca7f7e1c52a91b12663aeaf25de3895e5cca0f47ef2a8d0/kiwisolver-1.5.0-cp313-cp313-win_arm64.whl", hash = "sha256:fa8eb9ecdb7efb0b226acec134e0d709e87a909fa4971a54c0c4f6e88635484c", size = 64998, upload-time = "2026-03-09T13:14:13.469Z" }, + { url = "https://files.pythonhosted.org/packages/b0/69/ce68dd0c85755ae2de490bf015b62f2cea5f6b14ff00a463f9d0774449ff/kiwisolver-1.5.0-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:db485b3847d182b908b483b2ed133c66d88d49cacf98fd278fadafe11b4478d1", size = 125700, upload-time = "2026-03-09T13:14:14.636Z" }, + { url = "https://files.pythonhosted.org/packages/74/aa/937aac021cf9d4349990d47eb319309a51355ed1dbdc9c077cdc9224cb11/kiwisolver-1.5.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:be12f931839a3bdfe28b584db0e640a65a8bcbc24560ae3fdb025a449b3d754e", size = 67537, upload-time = "2026-03-09T13:14:15.808Z" }, + { url = "https://files.pythonhosted.org/packages/ee/20/3a87fbece2c40ad0f6f0aefa93542559159c5f99831d596050e8afae7a9f/kiwisolver-1.5.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:16b85d37c2cbb3253226d26e64663f755d88a03439a9c47df6246b35defbdfb7", size = 65514, upload-time = "2026-03-09T13:14:18.035Z" }, + { url = "https://files.pythonhosted.org/packages/f0/7f/f943879cda9007c45e1f7dba216d705c3a18d6b35830e488b6c6a4e7cdf0/kiwisolver-1.5.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4432b835675f0ea7414aab3d37d119f7226d24869b7a829caeab49ebda407b0c", size = 1584848, upload-time = "2026-03-09T13:14:19.745Z" }, + { url = "https://files.pythonhosted.org/packages/37/f8/4d4f85cc1870c127c88d950913370dd76138482161cd07eabbc450deff01/kiwisolver-1.5.0-cp313-cp313t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1b0feb50971481a2cc44d94e88bdb02cdd497618252ae226b8eb1201b957e368", size = 1391542, upload-time = "2026-03-09T13:14:21.54Z" }, + { url = "https://files.pythonhosted.org/packages/04/0b/65dd2916c84d252b244bd405303220f729e7c17c9d7d33dca6feeff9ffc4/kiwisolver-1.5.0-cp313-cp313t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:56fa888f10d0f367155e76ce849fa1166fc9730d13bd2d65a2aa13b6f5424489", size = 1404447, upload-time = "2026-03-09T13:14:23.205Z" }, + { url = "https://files.pythonhosted.org/packages/39/5c/2606a373247babce9b1d056c03a04b65f3cf5290a8eac5d7bdead0a17e21/kiwisolver-1.5.0-cp313-cp313t-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:940dda65d5e764406b9fb92761cbf462e4e63f712ab60ed98f70552e496f3bf1", size = 1455918, upload-time = "2026-03-09T13:14:24.74Z" }, + { url = "https://files.pythonhosted.org/packages/d5/d1/c6078b5756670658e9192a2ef11e939c92918833d2745f85cd14a6004bdf/kiwisolver-1.5.0-cp313-cp313t-manylinux_2_39_riscv64.whl", hash = "sha256:89fc958c702ee9a745e4700378f5d23fddbc46ff89e8fdbf5395c24d5c1452a3", size = 1072856, upload-time = "2026-03-09T13:14:26.597Z" }, + { url = "https://files.pythonhosted.org/packages/cb/c8/7def6ddf16eb2b3741d8b172bdaa9af882b03c78e9b0772975408801fa63/kiwisolver-1.5.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:9027d773c4ff81487181a925945743413f6069634d0b122d0b37684ccf4f1e18", size = 2333580, upload-time = "2026-03-09T13:14:28.237Z" }, + { url = "https://files.pythonhosted.org/packages/9e/87/2ac1fce0eb1e616fcd3c35caa23e665e9b1948bb984f4764790924594128/kiwisolver-1.5.0-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:5b233ea3e165e43e35dba1d2b8ecc21cf070b45b65ae17dd2747d2713d942021", size = 2423018, upload-time = "2026-03-09T13:14:30.018Z" }, + { url = "https://files.pythonhosted.org/packages/67/13/c6700ccc6cc218716bfcda4935e4b2997039869b4ad8a94f364c5a3b8e63/kiwisolver-1.5.0-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:ce9bf03dad3b46408c08649c6fbd6ca28a9fce0eb32fdfffa6775a13103b5310", size = 2062804, upload-time = "2026-03-09T13:14:32.888Z" }, + { url = "https://files.pythonhosted.org/packages/1b/bd/877056304626943ff0f1f44c08f584300c199b887cb3176cd7e34f1515f1/kiwisolver-1.5.0-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:fc4d3f1fb9ca0ae9f97b095963bc6326f1dbfd3779d6679a1e016b9baaa153d3", size = 2597482, upload-time = "2026-03-09T13:14:34.971Z" }, + { url = "https://files.pythonhosted.org/packages/75/19/c60626c47bf0f8ac5dcf72c6c98e266d714f2fbbfd50cf6dab5ede3aaa50/kiwisolver-1.5.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:f443b4825c50a51ee68585522ab4a1d1257fac65896f282b4c6763337ac9f5d2", size = 2394328, upload-time = "2026-03-09T13:14:36.816Z" }, + { url = "https://files.pythonhosted.org/packages/47/84/6a6d5e5bb8273756c27b7d810d47f7ef2f1f9b9fd23c9ee9a3f8c75c9cef/kiwisolver-1.5.0-cp313-cp313t-win_arm64.whl", hash = "sha256:893ff3a711d1b515ba9da14ee090519bad4610ed1962fbe298a434e8c5f8db53", size = 68410, upload-time = "2026-03-09T13:14:38.695Z" }, + { url = "https://files.pythonhosted.org/packages/e4/d7/060f45052f2a01ad5762c8fdecd6d7a752b43400dc29ff75cd47225a40fd/kiwisolver-1.5.0-cp314-cp314-macosx_10_15_universal2.whl", hash = "sha256:8df31fe574b8b3993cc61764f40941111b25c2d9fea13d3ce24a49907cd2d615", size = 123231, upload-time = "2026-03-09T13:14:41.323Z" }, + { url = "https://files.pythonhosted.org/packages/c2/a7/78da680eadd06ff35edef6ef68a1ad273bad3e2a0936c9a885103230aece/kiwisolver-1.5.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:1d49a49ac4cbfb7c1375301cd1ec90169dfeae55ff84710d782260ce77a75a02", size = 66489, upload-time = "2026-03-09T13:14:42.534Z" }, + { url = "https://files.pythonhosted.org/packages/49/b2/97980f3ad4fae37dd7fe31626e2bf75fbf8bdf5d303950ec1fab39a12da8/kiwisolver-1.5.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:0cbe94b69b819209a62cb27bdfa5dc2a8977d8de2f89dfd97ba4f53ed3af754e", size = 64063, upload-time = "2026-03-09T13:14:44.759Z" }, + { url = "https://files.pythonhosted.org/packages/e7/f9/b06c934a6aa8bc91f566bd2a214fd04c30506c2d9e2b6b171953216a65b6/kiwisolver-1.5.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:80aa065ffd378ff784822a6d7c3212f2d5f5e9c3589614b5c228b311fd3063ac", size = 1475913, upload-time = "2026-03-09T13:14:46.247Z" }, + { url = "https://files.pythonhosted.org/packages/6b/f0/f768ae564a710135630672981231320bc403cf9152b5596ec5289de0f106/kiwisolver-1.5.0-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4e7f886f47ab881692f278ae901039a234e4025a68e6dfab514263a0b1c4ae05", size = 1282782, upload-time = "2026-03-09T13:14:48.458Z" }, + { url = "https://files.pythonhosted.org/packages/e2/9f/1de7aad00697325f05238a5f2eafbd487fb637cc27a558b5367a5f37fb7f/kiwisolver-1.5.0-cp314-cp314-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:5060731cc3ed12ca3a8b57acd4aeca5bbc2f49216dd0bec1650a1acd89486bcd", size = 1300815, upload-time = "2026-03-09T13:14:50.721Z" }, + { url = "https://files.pythonhosted.org/packages/5a/c2/297f25141d2e468e0ce7f7a7b92e0cf8918143a0cbd3422c1ad627e85a06/kiwisolver-1.5.0-cp314-cp314-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:7a4aa69609f40fce3cbc3f87b2061f042eee32f94b8f11db707b66a26461591a", size = 1347925, upload-time = "2026-03-09T13:14:52.304Z" }, + { url = "https://files.pythonhosted.org/packages/b9/d3/f4c73a02eb41520c47610207b21afa8cdd18fdbf64ffd94674ae21c4812d/kiwisolver-1.5.0-cp314-cp314-manylinux_2_39_riscv64.whl", hash = "sha256:d168fda2dbff7b9b5f38e693182d792a938c31db4dac3a80a4888de603c99554", size = 991322, upload-time = "2026-03-09T13:14:54.637Z" }, + { url = "https://files.pythonhosted.org/packages/7b/46/d3f2efef7732fcda98d22bf4ad5d3d71d545167a852ca710a494f4c15343/kiwisolver-1.5.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:413b820229730d358efd838ecbab79902fe97094565fdc80ddb6b0a18c18a581", size = 2232857, upload-time = "2026-03-09T13:14:56.471Z" }, + { url = "https://files.pythonhosted.org/packages/3f/ec/2d9756bf2b6d26ae4349b8d3662fb3993f16d80c1f971c179ce862b9dbae/kiwisolver-1.5.0-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:5124d1ea754509b09e53738ec185584cc609aae4a3b510aaf4ed6aa047ef9303", size = 2329376, upload-time = "2026-03-09T13:14:58.072Z" }, + { url = "https://files.pythonhosted.org/packages/8f/9f/876a0a0f2260f1bde92e002b3019a5fabc35e0939c7d945e0fa66185eb20/kiwisolver-1.5.0-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:e4415a8db000bf49a6dd1c478bf70062eaacff0f462b92b0ba68791a905861f9", size = 1982549, upload-time = "2026-03-09T13:14:59.668Z" }, + { url = "https://files.pythonhosted.org/packages/6c/4f/ba3624dfac23a64d54ac4179832860cb537c1b0af06024936e82ca4154a0/kiwisolver-1.5.0-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:d618fd27420381a4f6044faa71f46d8bfd911bd077c555f7138ed88729bfbe79", size = 2494680, upload-time = "2026-03-09T13:15:01.364Z" }, + { url = "https://files.pythonhosted.org/packages/39/b7/97716b190ab98911b20d10bf92eca469121ec483b8ce0edd314f51bc85af/kiwisolver-1.5.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:5092eb5b1172947f57d6ea7d89b2f29650414e4293c47707eb499ec07a0ac796", size = 2297905, upload-time = "2026-03-09T13:15:03.925Z" }, + { url = "https://files.pythonhosted.org/packages/a3/36/4e551e8aa55c9188bca9abb5096805edbf7431072b76e2298e34fd3a3008/kiwisolver-1.5.0-cp314-cp314-win_amd64.whl", hash = "sha256:d76e2d8c75051d58177e762164d2e9ab92886534e3a12e795f103524f221dd8e", size = 75086, upload-time = "2026-03-09T13:15:07.775Z" }, + { url = "https://files.pythonhosted.org/packages/70/15/9b90f7df0e31a003c71649cf66ef61c3c1b862f48c81007fa2383c8bd8d7/kiwisolver-1.5.0-cp314-cp314-win_arm64.whl", hash = "sha256:fa6248cd194edff41d7ea9425ced8ca3a6f838bfb295f6f1d6e6bb694a8518df", size = 66577, upload-time = "2026-03-09T13:15:09.139Z" }, + { url = "https://files.pythonhosted.org/packages/17/01/7dc8c5443ff42b38e72731643ed7cf1ed9bf01691ae5cdca98501999ed83/kiwisolver-1.5.0-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:d1ffeb80b5676463d7a7d56acbe8e37a20ce725570e09549fe738e02ca6b7e1e", size = 125794, upload-time = "2026-03-09T13:15:10.525Z" }, + { url = "https://files.pythonhosted.org/packages/46/8a/b4ebe46ebaac6a303417fab10c2e165c557ddaff558f9699d302b256bc53/kiwisolver-1.5.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:bc4d8e252f532ab46a1de9349e2d27b91fce46736a9eedaa37beaca66f574ed4", size = 67646, upload-time = "2026-03-09T13:15:12.016Z" }, + { url = "https://files.pythonhosted.org/packages/60/35/10a844afc5f19d6f567359bf4789e26661755a2f36200d5d1ed8ad0126e5/kiwisolver-1.5.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:6783e069732715ad0c3ce96dbf21dbc2235ab0593f2baf6338101f70371f4028", size = 65511, upload-time = "2026-03-09T13:15:13.311Z" }, + { url = "https://files.pythonhosted.org/packages/f8/8a/685b297052dd041dcebce8e8787b58923b6e78acc6115a0dc9189011c44b/kiwisolver-1.5.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e7c4c09a490dc4d4a7f8cbee56c606a320f9dc28cf92a7157a39d1ce7676a657", size = 1584858, upload-time = "2026-03-09T13:15:15.103Z" }, + { url = "https://files.pythonhosted.org/packages/9e/80/04865e3d4638ac5bddec28908916df4a3075b8c6cc101786a96803188b96/kiwisolver-1.5.0-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2a075bd7bd19c70cf67c8badfa36cf7c5d8de3c9ddb8420c51e10d9c50e94920", size = 1392539, upload-time = "2026-03-09T13:15:16.661Z" }, + { url = "https://files.pythonhosted.org/packages/ba/01/77a19cacc0893fa13fafa46d1bba06fb4dc2360b3292baf4b56d8e067b24/kiwisolver-1.5.0-cp314-cp314t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:bdd3e53429ff02aa319ba59dfe4ceeec345bf46cf180ec2cf6fd5b942e7975e9", size = 1405310, upload-time = "2026-03-09T13:15:18.229Z" }, + { url = "https://files.pythonhosted.org/packages/53/39/bcaf5d0cca50e604cfa9b4e3ae1d64b50ca1ae5b754122396084599ef903/kiwisolver-1.5.0-cp314-cp314t-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:3cdcb35dc9d807259c981a85531048ede628eabcffb3239adf3d17463518992d", size = 1456244, upload-time = "2026-03-09T13:15:20.444Z" }, + { url = "https://files.pythonhosted.org/packages/d0/7a/72c187abc6975f6978c3e39b7cf67aeb8b3c0a8f9790aa7fd412855e9e1f/kiwisolver-1.5.0-cp314-cp314t-manylinux_2_39_riscv64.whl", hash = "sha256:70d593af6a6ca332d1df73d519fddb5148edb15cd90d5f0155e3746a6d4fcc65", size = 1073154, upload-time = "2026-03-09T13:15:22.039Z" }, + { url = "https://files.pythonhosted.org/packages/c7/ca/cf5b25783ebbd59143b4371ed0c8428a278abe68d6d0104b01865b1bbd0f/kiwisolver-1.5.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:377815a8616074cabbf3f53354e1d040c35815a134e01d7614b7692e4bf8acfa", size = 2334377, upload-time = "2026-03-09T13:15:23.741Z" }, + { url = "https://files.pythonhosted.org/packages/4a/e5/b1f492adc516796e88751282276745340e2a72dcd0d36cf7173e0daf3210/kiwisolver-1.5.0-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:0255a027391d52944eae1dbb5d4cc5903f57092f3674e8e544cdd2622826b3f0", size = 2425288, upload-time = "2026-03-09T13:15:25.789Z" }, + { url = "https://files.pythonhosted.org/packages/e6/e5/9b21fbe91a61b8f409d74a26498706e97a48008bfcd1864373d32a6ba31c/kiwisolver-1.5.0-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:012b1eb16e28718fa782b5e61dc6f2da1f0792ca73bd05d54de6cb9561665fc9", size = 2063158, upload-time = "2026-03-09T13:15:27.63Z" }, + { url = "https://files.pythonhosted.org/packages/b1/02/83f47986138310f95ea95531f851b2a62227c11cbc3e690ae1374fe49f0f/kiwisolver-1.5.0-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:0e3aafb33aed7479377e5e9a82e9d4bf87063741fc99fc7ae48b0f16e32bdd6f", size = 2597260, upload-time = "2026-03-09T13:15:29.421Z" }, + { url = "https://files.pythonhosted.org/packages/07/18/43a5f24608d8c313dd189cf838c8e68d75b115567c6279de7796197cfb6a/kiwisolver-1.5.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:e7a116ae737f0000343218c4edf5bd45893bfeaff0993c0b215d7124c9f77646", size = 2394403, upload-time = "2026-03-09T13:15:31.517Z" }, + { url = "https://files.pythonhosted.org/packages/3b/b5/98222136d839b8afabcaa943b09bd05888c2d36355b7e448550211d1fca4/kiwisolver-1.5.0-cp314-cp314t-win_amd64.whl", hash = "sha256:1dd9b0b119a350976a6d781e7278ec7aca0b201e1a9e2d23d9804afecb6ca681", size = 79687, upload-time = "2026-03-09T13:15:33.204Z" }, + { url = "https://files.pythonhosted.org/packages/99/a2/ca7dc962848040befed12732dff6acae7fb3c4f6fc4272b3f6c9a30b8713/kiwisolver-1.5.0-cp314-cp314t-win_arm64.whl", hash = "sha256:58f812017cd2985c21fbffb4864d59174d4903dd66fa23815e74bbc7a0e2dd57", size = 70032, upload-time = "2026-03-09T13:15:34.411Z" }, +] + +[[package]] +name = "lark" +version = "1.3.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/da/34/28fff3ab31ccff1fd4f6c7c7b0ceb2b6968d8ea4950663eadcb5720591a0/lark-1.3.1.tar.gz", hash = "sha256:b426a7a6d6d53189d318f2b6236ab5d6429eaf09259f1ca33eb716eed10d2905", size = 382732, upload-time = "2025-10-27T18:25:56.653Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/82/3d/14ce75ef66813643812f3093ab17e46d3a206942ce7376d31ec2d36229e7/lark-1.3.1-py3-none-any.whl", hash = "sha256:c629b661023a014c37da873b4ff58a817398d12635d3bbb2c5a03be7fe5d1e12", size = 113151, upload-time = "2025-10-27T18:25:54.882Z" }, +] + +[[package]] +name = "loguru" +version = "0.7.3" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "colorama", marker = "sys_platform == 'win32'" }, + { name = "win32-setctime", marker = "sys_platform == 'win32'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/3a/05/a1dae3dffd1116099471c643b8924f5aa6524411dc6c63fdae648c4f1aca/loguru-0.7.3.tar.gz", hash = "sha256:19480589e77d47b8d85b2c827ad95d49bf31b0dcde16593892eb51dd18706eb6", size = 63559, upload-time = "2024-12-06T11:20:56.608Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/0c/29/0348de65b8cc732daa3e33e67806420b2ae89bdce2b04af740289c5c6c8c/loguru-0.7.3-py3-none-any.whl", hash = "sha256:31a33c10c8e1e10422bfd431aeb5d351c7cf7fa671e3c4df004162264b28220c", size = 61595, upload-time = "2024-12-06T11:20:54.538Z" }, +] + +[[package]] +name = "markdown-it-py" +version = "4.0.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "mdurl" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/5b/f5/4ec618ed16cc4f8fb3b701563655a69816155e79e24a17b651541804721d/markdown_it_py-4.0.0.tar.gz", hash = "sha256:cb0a2b4aa34f932c007117b194e945bd74e0ec24133ceb5bac59009cda1cb9f3", size = 73070, upload-time = "2025-08-11T12:57:52.854Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/94/54/e7d793b573f298e1c9013b8c4dade17d481164aa517d1d7148619c2cedbf/markdown_it_py-4.0.0-py3-none-any.whl", hash = "sha256:87327c59b172c5011896038353a81343b6754500a08cd7a4973bb48c6d578147", size = 87321, upload-time = "2025-08-11T12:57:51.923Z" }, +] + +[[package]] +name = "markupsafe" +version = "3.0.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/7e/99/7690b6d4034fffd95959cbe0c02de8deb3098cc577c67bb6a24fe5d7caa7/markupsafe-3.0.3.tar.gz", hash = "sha256:722695808f4b6457b320fdc131280796bdceb04ab50fe1795cd540799ebe1698", size = 80313, upload-time = "2025-09-27T18:37:40.426Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/38/2f/907b9c7bbba283e68f20259574b13d005c121a0fa4c175f9bed27c4597ff/markupsafe-3.0.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:e1cf1972137e83c5d4c136c43ced9ac51d0e124706ee1c8aa8532c1287fa8795", size = 11622, upload-time = "2025-09-27T18:36:41.777Z" }, + { url = "https://files.pythonhosted.org/packages/9c/d9/5f7756922cdd676869eca1c4e3c0cd0df60ed30199ffd775e319089cb3ed/markupsafe-3.0.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:116bb52f642a37c115f517494ea5feb03889e04df47eeff5b130b1808ce7c219", size = 12029, upload-time = "2025-09-27T18:36:43.257Z" }, + { url = "https://files.pythonhosted.org/packages/00/07/575a68c754943058c78f30db02ee03a64b3c638586fba6a6dd56830b30a3/markupsafe-3.0.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:133a43e73a802c5562be9bbcd03d090aa5a1fe899db609c29e8c8d815c5f6de6", size = 24374, upload-time = "2025-09-27T18:36:44.508Z" }, + { url = "https://files.pythonhosted.org/packages/a9/21/9b05698b46f218fc0e118e1f8168395c65c8a2c750ae2bab54fc4bd4e0e8/markupsafe-3.0.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ccfcd093f13f0f0b7fdd0f198b90053bf7b2f02a3927a30e63f3ccc9df56b676", size = 22980, upload-time = "2025-09-27T18:36:45.385Z" }, + { url = "https://files.pythonhosted.org/packages/7f/71/544260864f893f18b6827315b988c146b559391e6e7e8f7252839b1b846a/markupsafe-3.0.3-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:509fa21c6deb7a7a273d629cf5ec029bc209d1a51178615ddf718f5918992ab9", size = 21990, upload-time = "2025-09-27T18:36:46.916Z" }, + { url = "https://files.pythonhosted.org/packages/c2/28/b50fc2f74d1ad761af2f5dcce7492648b983d00a65b8c0e0cb457c82ebbe/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a4afe79fb3de0b7097d81da19090f4df4f8d3a2b3adaa8764138aac2e44f3af1", size = 23784, upload-time = "2025-09-27T18:36:47.884Z" }, + { url = "https://files.pythonhosted.org/packages/ed/76/104b2aa106a208da8b17a2fb72e033a5a9d7073c68f7e508b94916ed47a9/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:795e7751525cae078558e679d646ae45574b47ed6e7771863fcc079a6171a0fc", size = 21588, upload-time = "2025-09-27T18:36:48.82Z" }, + { url = "https://files.pythonhosted.org/packages/b5/99/16a5eb2d140087ebd97180d95249b00a03aa87e29cc224056274f2e45fd6/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:8485f406a96febb5140bfeca44a73e3ce5116b2501ac54fe953e488fb1d03b12", size = 23041, upload-time = "2025-09-27T18:36:49.797Z" }, + { url = "https://files.pythonhosted.org/packages/19/bc/e7140ed90c5d61d77cea142eed9f9c303f4c4806f60a1044c13e3f1471d0/markupsafe-3.0.3-cp313-cp313-win32.whl", hash = "sha256:bdd37121970bfd8be76c5fb069c7751683bdf373db1ed6c010162b2a130248ed", size = 14543, upload-time = "2025-09-27T18:36:51.584Z" }, + { url = "https://files.pythonhosted.org/packages/05/73/c4abe620b841b6b791f2edc248f556900667a5a1cf023a6646967ae98335/markupsafe-3.0.3-cp313-cp313-win_amd64.whl", hash = "sha256:9a1abfdc021a164803f4d485104931fb8f8c1efd55bc6b748d2f5774e78b62c5", size = 15113, upload-time = "2025-09-27T18:36:52.537Z" }, + { url = "https://files.pythonhosted.org/packages/f0/3a/fa34a0f7cfef23cf9500d68cb7c32dd64ffd58a12b09225fb03dd37d5b80/markupsafe-3.0.3-cp313-cp313-win_arm64.whl", hash = "sha256:7e68f88e5b8799aa49c85cd116c932a1ac15caaa3f5db09087854d218359e485", size = 13911, upload-time = "2025-09-27T18:36:53.513Z" }, + { url = "https://files.pythonhosted.org/packages/e4/d7/e05cd7efe43a88a17a37b3ae96e79a19e846f3f456fe79c57ca61356ef01/markupsafe-3.0.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:218551f6df4868a8d527e3062d0fb968682fe92054e89978594c28e642c43a73", size = 11658, upload-time = "2025-09-27T18:36:54.819Z" }, + { url = "https://files.pythonhosted.org/packages/99/9e/e412117548182ce2148bdeacdda3bb494260c0b0184360fe0d56389b523b/markupsafe-3.0.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:3524b778fe5cfb3452a09d31e7b5adefeea8c5be1d43c4f810ba09f2ceb29d37", size = 12066, upload-time = "2025-09-27T18:36:55.714Z" }, + { url = "https://files.pythonhosted.org/packages/bc/e6/fa0ffcda717ef64a5108eaa7b4f5ed28d56122c9a6d70ab8b72f9f715c80/markupsafe-3.0.3-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4e885a3d1efa2eadc93c894a21770e4bc67899e3543680313b09f139e149ab19", size = 25639, upload-time = "2025-09-27T18:36:56.908Z" }, + { url = "https://files.pythonhosted.org/packages/96/ec/2102e881fe9d25fc16cb4b25d5f5cde50970967ffa5dddafdb771237062d/markupsafe-3.0.3-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8709b08f4a89aa7586de0aadc8da56180242ee0ada3999749b183aa23df95025", size = 23569, upload-time = "2025-09-27T18:36:57.913Z" }, + { url = "https://files.pythonhosted.org/packages/4b/30/6f2fce1f1f205fc9323255b216ca8a235b15860c34b6798f810f05828e32/markupsafe-3.0.3-cp313-cp313t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:b8512a91625c9b3da6f127803b166b629725e68af71f8184ae7e7d54686a56d6", size = 23284, upload-time = "2025-09-27T18:36:58.833Z" }, + { url = "https://files.pythonhosted.org/packages/58/47/4a0ccea4ab9f5dcb6f79c0236d954acb382202721e704223a8aafa38b5c8/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:9b79b7a16f7fedff2495d684f2b59b0457c3b493778c9eed31111be64d58279f", size = 24801, upload-time = "2025-09-27T18:36:59.739Z" }, + { url = "https://files.pythonhosted.org/packages/6a/70/3780e9b72180b6fecb83a4814d84c3bf4b4ae4bf0b19c27196104149734c/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:12c63dfb4a98206f045aa9563db46507995f7ef6d83b2f68eda65c307c6829eb", size = 22769, upload-time = "2025-09-27T18:37:00.719Z" }, + { url = "https://files.pythonhosted.org/packages/98/c5/c03c7f4125180fc215220c035beac6b9cb684bc7a067c84fc69414d315f5/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:8f71bc33915be5186016f675cd83a1e08523649b0e33efdb898db577ef5bb009", size = 23642, upload-time = "2025-09-27T18:37:01.673Z" }, + { url = "https://files.pythonhosted.org/packages/80/d6/2d1b89f6ca4bff1036499b1e29a1d02d282259f3681540e16563f27ebc23/markupsafe-3.0.3-cp313-cp313t-win32.whl", hash = "sha256:69c0b73548bc525c8cb9a251cddf1931d1db4d2258e9599c28c07ef3580ef354", size = 14612, upload-time = "2025-09-27T18:37:02.639Z" }, + { url = "https://files.pythonhosted.org/packages/2b/98/e48a4bfba0a0ffcf9925fe2d69240bfaa19c6f7507b8cd09c70684a53c1e/markupsafe-3.0.3-cp313-cp313t-win_amd64.whl", hash = "sha256:1b4b79e8ebf6b55351f0d91fe80f893b4743f104bff22e90697db1590e47a218", size = 15200, upload-time = "2025-09-27T18:37:03.582Z" }, + { url = "https://files.pythonhosted.org/packages/0e/72/e3cc540f351f316e9ed0f092757459afbc595824ca724cbc5a5d4263713f/markupsafe-3.0.3-cp313-cp313t-win_arm64.whl", hash = "sha256:ad2cf8aa28b8c020ab2fc8287b0f823d0a7d8630784c31e9ee5edea20f406287", size = 13973, upload-time = "2025-09-27T18:37:04.929Z" }, + { url = "https://files.pythonhosted.org/packages/33/8a/8e42d4838cd89b7dde187011e97fe6c3af66d8c044997d2183fbd6d31352/markupsafe-3.0.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:eaa9599de571d72e2daf60164784109f19978b327a3910d3e9de8c97b5b70cfe", size = 11619, upload-time = "2025-09-27T18:37:06.342Z" }, + { url = "https://files.pythonhosted.org/packages/b5/64/7660f8a4a8e53c924d0fa05dc3a55c9cee10bbd82b11c5afb27d44b096ce/markupsafe-3.0.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:c47a551199eb8eb2121d4f0f15ae0f923d31350ab9280078d1e5f12b249e0026", size = 12029, upload-time = "2025-09-27T18:37:07.213Z" }, + { url = "https://files.pythonhosted.org/packages/da/ef/e648bfd021127bef5fa12e1720ffed0c6cbb8310c8d9bea7266337ff06de/markupsafe-3.0.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f34c41761022dd093b4b6896d4810782ffbabe30f2d443ff5f083e0cbbb8c737", size = 24408, upload-time = "2025-09-27T18:37:09.572Z" }, + { url = "https://files.pythonhosted.org/packages/41/3c/a36c2450754618e62008bf7435ccb0f88053e07592e6028a34776213d877/markupsafe-3.0.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:457a69a9577064c05a97c41f4e65148652db078a3a509039e64d3467b9e7ef97", size = 23005, upload-time = "2025-09-27T18:37:10.58Z" }, + { url = "https://files.pythonhosted.org/packages/bc/20/b7fdf89a8456b099837cd1dc21974632a02a999ec9bf7ca3e490aacd98e7/markupsafe-3.0.3-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:e8afc3f2ccfa24215f8cb28dcf43f0113ac3c37c2f0f0806d8c70e4228c5cf4d", size = 22048, upload-time = "2025-09-27T18:37:11.547Z" }, + { url = "https://files.pythonhosted.org/packages/9a/a7/591f592afdc734f47db08a75793a55d7fbcc6902a723ae4cfbab61010cc5/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:ec15a59cf5af7be74194f7ab02d0f59a62bdcf1a537677ce67a2537c9b87fcda", size = 23821, upload-time = "2025-09-27T18:37:12.48Z" }, + { url = "https://files.pythonhosted.org/packages/7d/33/45b24e4f44195b26521bc6f1a82197118f74df348556594bd2262bda1038/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:0eb9ff8191e8498cca014656ae6b8d61f39da5f95b488805da4bb029cccbfbaf", size = 21606, upload-time = "2025-09-27T18:37:13.485Z" }, + { url = "https://files.pythonhosted.org/packages/ff/0e/53dfaca23a69fbfbbf17a4b64072090e70717344c52eaaaa9c5ddff1e5f0/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:2713baf880df847f2bece4230d4d094280f4e67b1e813eec43b4c0e144a34ffe", size = 23043, upload-time = "2025-09-27T18:37:14.408Z" }, + { url = "https://files.pythonhosted.org/packages/46/11/f333a06fc16236d5238bfe74daccbca41459dcd8d1fa952e8fbd5dccfb70/markupsafe-3.0.3-cp314-cp314-win32.whl", hash = "sha256:729586769a26dbceff69f7a7dbbf59ab6572b99d94576a5592625d5b411576b9", size = 14747, upload-time = "2025-09-27T18:37:15.36Z" }, + { url = "https://files.pythonhosted.org/packages/28/52/182836104b33b444e400b14f797212f720cbc9ed6ba34c800639d154e821/markupsafe-3.0.3-cp314-cp314-win_amd64.whl", hash = "sha256:bdc919ead48f234740ad807933cdf545180bfbe9342c2bb451556db2ed958581", size = 15341, upload-time = "2025-09-27T18:37:16.496Z" }, + { url = "https://files.pythonhosted.org/packages/6f/18/acf23e91bd94fd7b3031558b1f013adfa21a8e407a3fdb32745538730382/markupsafe-3.0.3-cp314-cp314-win_arm64.whl", hash = "sha256:5a7d5dc5140555cf21a6fefbdbf8723f06fcd2f63ef108f2854de715e4422cb4", size = 14073, upload-time = "2025-09-27T18:37:17.476Z" }, + { url = "https://files.pythonhosted.org/packages/3c/f0/57689aa4076e1b43b15fdfa646b04653969d50cf30c32a102762be2485da/markupsafe-3.0.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:1353ef0c1b138e1907ae78e2f6c63ff67501122006b0f9abad68fda5f4ffc6ab", size = 11661, upload-time = "2025-09-27T18:37:18.453Z" }, + { url = "https://files.pythonhosted.org/packages/89/c3/2e67a7ca217c6912985ec766c6393b636fb0c2344443ff9d91404dc4c79f/markupsafe-3.0.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:1085e7fbddd3be5f89cc898938f42c0b3c711fdcb37d75221de2666af647c175", size = 12069, upload-time = "2025-09-27T18:37:19.332Z" }, + { url = "https://files.pythonhosted.org/packages/f0/00/be561dce4e6ca66b15276e184ce4b8aec61fe83662cce2f7d72bd3249d28/markupsafe-3.0.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1b52b4fb9df4eb9ae465f8d0c228a00624de2334f216f178a995ccdcf82c4634", size = 25670, upload-time = "2025-09-27T18:37:20.245Z" }, + { url = "https://files.pythonhosted.org/packages/50/09/c419f6f5a92e5fadde27efd190eca90f05e1261b10dbd8cbcb39cd8ea1dc/markupsafe-3.0.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fed51ac40f757d41b7c48425901843666a6677e3e8eb0abcff09e4ba6e664f50", size = 23598, upload-time = "2025-09-27T18:37:21.177Z" }, + { url = "https://files.pythonhosted.org/packages/22/44/a0681611106e0b2921b3033fc19bc53323e0b50bc70cffdd19f7d679bb66/markupsafe-3.0.3-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:f190daf01f13c72eac4efd5c430a8de82489d9cff23c364c3ea822545032993e", size = 23261, upload-time = "2025-09-27T18:37:22.167Z" }, + { url = "https://files.pythonhosted.org/packages/5f/57/1b0b3f100259dc9fffe780cfb60d4be71375510e435efec3d116b6436d43/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:e56b7d45a839a697b5eb268c82a71bd8c7f6c94d6fd50c3d577fa39a9f1409f5", size = 24835, upload-time = "2025-09-27T18:37:23.296Z" }, + { url = "https://files.pythonhosted.org/packages/26/6a/4bf6d0c97c4920f1597cc14dd720705eca0bf7c787aebc6bb4d1bead5388/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:f3e98bb3798ead92273dc0e5fd0f31ade220f59a266ffd8a4f6065e0a3ce0523", size = 22733, upload-time = "2025-09-27T18:37:24.237Z" }, + { url = "https://files.pythonhosted.org/packages/14/c7/ca723101509b518797fedc2fdf79ba57f886b4aca8a7d31857ba3ee8281f/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:5678211cb9333a6468fb8d8be0305520aa073f50d17f089b5b4b477ea6e67fdc", size = 23672, upload-time = "2025-09-27T18:37:25.271Z" }, + { url = "https://files.pythonhosted.org/packages/fb/df/5bd7a48c256faecd1d36edc13133e51397e41b73bb77e1a69deab746ebac/markupsafe-3.0.3-cp314-cp314t-win32.whl", hash = "sha256:915c04ba3851909ce68ccc2b8e2cd691618c4dc4c4232fb7982bca3f41fd8c3d", size = 14819, upload-time = "2025-09-27T18:37:26.285Z" }, + { url = "https://files.pythonhosted.org/packages/1a/8a/0402ba61a2f16038b48b39bccca271134be00c5c9f0f623208399333c448/markupsafe-3.0.3-cp314-cp314t-win_amd64.whl", hash = "sha256:4faffd047e07c38848ce017e8725090413cd80cbc23d86e55c587bf979e579c9", size = 15426, upload-time = "2025-09-27T18:37:27.316Z" }, + { url = "https://files.pythonhosted.org/packages/70/bc/6f1c2f612465f5fa89b95bead1f44dcb607670fd42891d8fdcd5d039f4f4/markupsafe-3.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:32001d6a8fc98c8cb5c947787c5d08b0a50663d139f1305bac5885d98d9b40fa", size = 14146, upload-time = "2025-09-27T18:37:28.327Z" }, +] + +[[package]] +name = "matplotlib" +version = "3.10.9" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "contourpy" }, + { name = "cycler" }, + { name = "fonttools" }, + { name = "kiwisolver" }, + { name = "numpy" }, + { name = "packaging" }, + { name = "pillow" }, + { name = "pyparsing" }, + { name = "python-dateutil" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/63/1b/4be5be87d43d327a0cf4de1a56e86f7f84c89312452406cf122efe2839e6/matplotlib-3.10.9.tar.gz", hash = "sha256:fd66508e8c6877d98e586654b608a0456db8d7e8a546eb1e2600efd957302358", size = 34811233, upload-time = "2026-04-24T00:14:13.539Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/aa/d3/8d4f6afbecb49fc04e060a57c0fce39ea51cc163a6bd87303ccd698e4fa6/matplotlib-3.10.9-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:b580440f1ff81a0e34122051a3dfabb7e4b7f9e380629929bde0eff9af72165f", size = 8320331, upload-time = "2026-04-24T00:12:39.688Z" }, + { url = "https://files.pythonhosted.org/packages/63/d9/9e14bc7564bf92d5ffa801ae5fac819ce74b925dfb55e3ebde61a3bbad3e/matplotlib-3.10.9-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:b1b745c489cd1a77a0dc1120a05dc87af9798faebc913601feb8c73d89bf2d1e", size = 8216461, upload-time = "2026-04-24T00:12:42.494Z" }, + { url = "https://files.pythonhosted.org/packages/8a/17/4402d0d14ccf1dfc70932600b68097fbbf9c898a4871d2cbbe79c7801a32/matplotlib-3.10.9-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:8f3bcac1ca5ed000a6f4337d47ba67dfddf37ed6a46c15fd7f014997f7bf865f", size = 8790091, upload-time = "2026-04-24T00:12:44.789Z" }, + { url = "https://files.pythonhosted.org/packages/3e/0b/322aeec06dd9b91411f92028b37d447342770a24392aa4813e317064dad5/matplotlib-3.10.9-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7a8d66a55def891c33147ba3ba9bfcabf0b526a43764c818acbb4525e5ed0838", size = 9605027, upload-time = "2026-04-24T00:12:47.583Z" }, + { url = "https://files.pythonhosted.org/packages/74/88/5f13482f55e7b00bcfc09838b093c2456e1379978d2a146844aae05350ad/matplotlib-3.10.9-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:d843374407c4017a6403b59c6c81606773d136f3259d5b6da3131bc814542cc2", size = 9671269, upload-time = "2026-04-24T00:12:50.878Z" }, + { url = "https://files.pythonhosted.org/packages/c5/e0/0840fd2f93da988ec660b8ad1984abe9f25d2aed22a5e394ff1c68c88307/matplotlib-3.10.9-cp313-cp313-win_amd64.whl", hash = "sha256:f4399f64b3e94cd500195490972ae1ee81170df1636fa15364d157d5bdd7b921", size = 8217588, upload-time = "2026-04-24T00:12:53.784Z" }, + { url = "https://files.pythonhosted.org/packages/47/b9/d706d06dd605c49b9f83a2aed8c13e3e5db70697d7a80b7e3d7915de6b17/matplotlib-3.10.9-cp313-cp313-win_arm64.whl", hash = "sha256:ba7b3b8ef09eab7df0e86e9ae086faa433efbfbdb46afcb3aa16aabf779469a8", size = 8136913, upload-time = "2026-04-24T00:12:56.501Z" }, + { url = "https://files.pythonhosted.org/packages/9b/45/6e32d96978264c8ca8c4b1010adb955a1a49cfaf314e212bbc8908f04a61/matplotlib-3.10.9-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:09218df8a93712bd6ea133e83a153c755448cf7868316c531cffcc43f69d1cc9", size = 8368019, upload-time = "2026-04-24T00:12:58.896Z" }, + { url = "https://files.pythonhosted.org/packages/86/0a/c8e3d3bba245f0f7fc424937f8ff7ef77291a36af3edb97ccd78aa93d84f/matplotlib-3.10.9-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:82368699727bfb7b0182e1aa13082e3c08e092fa1a25d3e1fd92405bff96f6d4", size = 8264645, upload-time = "2026-04-24T00:13:01.406Z" }, + { url = "https://files.pythonhosted.org/packages/3d/aa/5bf5a14fe4fed73a4209a155606f8096ff797aad89c6c35179026571133e/matplotlib-3.10.9-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:3225f4e1edcb8c86c884ddf79ebe20ecd0a67d30188f279897554ccd8fded4dc", size = 8802194, upload-time = "2026-04-24T00:13:03.702Z" }, + { url = "https://files.pythonhosted.org/packages/dd/5e/b4be852d6bba6fd15893fadf91ff26ae49cb91aac789e95dde9d342e664f/matplotlib-3.10.9-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:de2445a0c6690d21b7eb6ce071cebad6d40a2e9bdf10d039074a96ba19797b99", size = 9622684, upload-time = "2026-04-24T00:13:06.647Z" }, + { url = "https://files.pythonhosted.org/packages/4c/3d/ed428c971139112ef730f62770654d609467346d09d4b62617e1afd68a5a/matplotlib-3.10.9-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:b2b9516251cb89ff618d757daec0e2ed1bf21248013844a853d87ef85ab3081d", size = 9680790, upload-time = "2026-04-24T00:13:10.009Z" }, + { url = "https://files.pythonhosted.org/packages/e7/09/052e884aaf2b985c63cb79f715f1d5b6a3eaa7de78f6a52b9dbc077d5b53/matplotlib-3.10.9-cp313-cp313t-win_amd64.whl", hash = "sha256:e9fae004b941b23ff2edcf1567a857ed77bafc8086ffa258190462328434faf8", size = 8287571, upload-time = "2026-04-24T00:13:13.087Z" }, + { url = "https://files.pythonhosted.org/packages/f4/38/ae27288e788c35a4250491422f3db7750366fc8c97d6f36fbdecfc1f5518/matplotlib-3.10.9-cp313-cp313t-win_arm64.whl", hash = "sha256:6b63d9c7c769b88ab81e10dc86e4e0607cf56817b9f9e6cf24b2a5f1693b8e38", size = 8188292, upload-time = "2026-04-24T00:13:15.546Z" }, + { url = "https://files.pythonhosted.org/packages/d6/e6/3bd8afd04949f02eabc1c17115ea5255e19cacd4d06fc5abdde4eeb0052c/matplotlib-3.10.9-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:172db52c9e683f5d12eaf57f0f54834190e12581fe1cc2a19595a8f5acb4e77d", size = 8321276, upload-time = "2026-04-24T00:13:18.318Z" }, + { url = "https://files.pythonhosted.org/packages/41/86/86231232fff41c9f8e4a1a7d7a597d349a02527109c3af7d618366122139/matplotlib-3.10.9-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:97e35e8d39ccc85859095e01a53847432ba9a53ddf7986f7a54a11b73d0e143f", size = 8218218, upload-time = "2026-04-24T00:13:20.974Z" }, + { url = "https://files.pythonhosted.org/packages/85/8f/becc9722cafc64f5d2eb0b7c1bf5f585271c618a45dbd8fabeb021f898b6/matplotlib-3.10.9-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:aba1615dabe83188e19d4f75a253c6a08423e04c1425e64039f800050a69de6b", size = 9608145, upload-time = "2026-04-24T00:13:23.228Z" }, + { url = "https://files.pythonhosted.org/packages/32/5d/f7e914f7d9325abff4057cee62c0fa70263683189f774473cbfb534cd13b/matplotlib-3.10.9-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:34cf8167e023ad956c15f36302911d5406bd99a9862c1a8499ea6f7c0e015dc2", size = 9885085, upload-time = "2026-04-24T00:13:25.849Z" }, + { url = "https://files.pythonhosted.org/packages/a5/fd/fa69f2221534e80cc5772ac2b7d222011a2acafc2ec7216d5dd174c864ae/matplotlib-3.10.9-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:59476c6d29d612b8e9bb6ce8c5b631be6ba8f9e3a2421f22a02b192c7dd28716", size = 9672358, upload-time = "2026-04-24T00:13:28.906Z" }, + { url = "https://files.pythonhosted.org/packages/ab/1a/5a4f747a8b271cbb024946d2dd3c913ab5032ba430626f8c3528ada96b4b/matplotlib-3.10.9-cp314-cp314-win_amd64.whl", hash = "sha256:336b9acc64d309063126edcdaca00db9373af3c476bb94388fe9c5a53ad13e6f", size = 8349970, upload-time = "2026-04-24T00:13:31.904Z" }, + { url = "https://files.pythonhosted.org/packages/64/dc/95d60ecaefe30680a154b52ea96ab4b0dab547f1fd6aa12f5fb655e89cae/matplotlib-3.10.9-cp314-cp314-win_arm64.whl", hash = "sha256:2dc9477819ffd78ad12a20df1d9d6a6bd4fec6aaa9072681465fddca052f1456", size = 8272785, upload-time = "2026-04-24T00:13:34.511Z" }, + { url = "https://files.pythonhosted.org/packages/70/a0/005d68bc8b8418300ce6591f18586910a8526806e2ab663933d9f20a41e9/matplotlib-3.10.9-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:da4e09638420548f31c354032a6250e473c68e5a4e96899b4844cf39ddea23fe", size = 8367999, upload-time = "2026-04-24T00:13:36.962Z" }, + { url = "https://files.pythonhosted.org/packages/22/05/1236cc9290be70b2498af20ca348add76e3fffe7f67b477db5133a84f3ea/matplotlib-3.10.9-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:345f6f68ecc8da0ca56fad2ea08fde1a115eda530079eca185d50a7bc3e146c6", size = 8264543, upload-time = "2026-04-24T00:13:39.851Z" }, + { url = "https://files.pythonhosted.org/packages/cd/c2/071f5a5ff6c5bd63aaaf2f45c811d9bf2ced94bde188d9e1a519e21d0cba/matplotlib-3.10.9-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4edcfbd8565339aa62f1cd4012f7180926fdbe71850f7b0d3c379c175cd6b66c", size = 9622800, upload-time = "2026-04-24T00:13:42.296Z" }, + { url = "https://files.pythonhosted.org/packages/95/57/da7d1f10a85624b9e7db68e069dd94e58dc41dbf9463c5921632ecbe3661/matplotlib-3.10.9-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6be157fe17fc37cb95ac1d7374cf717ce9259616edec911a78d9d26dae8522d4", size = 9888561, upload-time = "2026-04-24T00:13:45.026Z" }, + { url = "https://files.pythonhosted.org/packages/67/b2/ef8d6bb59b0edb6c16c968b70f548aa13b54348972def5aa6ac85df67145/matplotlib-3.10.9-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:4e42042d54db34fda4e95a7bd3e5789c2a995d2dad3eb8850232ee534092fbbf", size = 9680884, upload-time = "2026-04-24T00:13:48.066Z" }, + { url = "https://files.pythonhosted.org/packages/61/1c/d21bfeb9931881ebe96bcfcff27c7ae4b160ae0ec291a714c42641a56d75/matplotlib-3.10.9-cp314-cp314t-win_amd64.whl", hash = "sha256:c27df8b3848f32a83d1767566595e43cfaa4460380974da06f4279a7ec143c39", size = 8432333, upload-time = "2026-04-24T00:13:51.008Z" }, + { url = "https://files.pythonhosted.org/packages/78/23/92493c3e6e1b635ccfff146f7b99e674808787915420373ac399283764c2/matplotlib-3.10.9-cp314-cp314t-win_arm64.whl", hash = "sha256:a49f1eadc84ca85fd72fa4e89e70e61bf86452df6f971af04b12c60761a0772c", size = 8324785, upload-time = "2026-04-24T00:13:53.633Z" }, +] + +[[package]] +name = "matplotlib-inline" +version = "0.2.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "traitlets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/c7/74/97e72a36efd4ae2bccb3463284300f8953f199b5ffbc04cbbb0ec78f74b1/matplotlib_inline-0.2.1.tar.gz", hash = "sha256:e1ee949c340d771fc39e241ea75683deb94762c8fa5f2927ec57c83c4dffa9fe", size = 8110, upload-time = "2025-10-23T09:00:22.126Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/af/33/ee4519fa02ed11a94aef9559552f3b17bb863f2ecfe1a35dc7f548cde231/matplotlib_inline-0.2.1-py3-none-any.whl", hash = "sha256:d56ce5156ba6085e00a9d54fead6ed29a9c47e215cd1bba2e976ef39f5710a76", size = 9516, upload-time = "2025-10-23T09:00:20.675Z" }, +] + +[[package]] +name = "mcp" +version = "1.27.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "anyio" }, + { name = "httpx" }, + { name = "httpx-sse" }, + { name = "jsonschema" }, + { name = "pydantic" }, + { name = "pydantic-settings" }, + { name = "pyjwt", extra = ["crypto"] }, + { name = "python-multipart" }, + { name = "pywin32", marker = "sys_platform == 'win32'" }, + { name = "sse-starlette" }, + { name = "starlette" }, + { name = "typing-extensions" }, + { name = "typing-inspection" }, + { name = "uvicorn", marker = "sys_platform != 'emscripten'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/8b/eb/c0cfc62075dc6e1ec1c64d352ae09ac051d9334311ed226f1f425312848a/mcp-1.27.0.tar.gz", hash = "sha256:d3dc35a7eec0d458c1da4976a48f982097ddaab87e278c5511d5a4a56e852b83", size = 607509, upload-time = "2026-04-02T14:48:08.88Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/9c/46/f6b4ad632c67ef35209a66127e4bddc95759649dd595f71f13fba11bdf9a/mcp-1.27.0-py3-none-any.whl", hash = "sha256:5ce1fa81614958e267b21fb2aa34e0aea8e2c6ede60d52aba45fd47246b4d741", size = 215967, upload-time = "2026-04-02T14:48:07.24Z" }, +] + +[package.optional-dependencies] +cli = [ + { name = "python-dotenv" }, + { name = "typer" }, +] + +[[package]] +name = "mdurl" +version = "0.1.2" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/d6/54/cfe61301667036ec958cb99bd3efefba235e65cdeb9c84d24a8293ba1d90/mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba", size = 8729, upload-time = "2022-08-14T12:40:10.846Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/b3/38/89ba8ad64ae25be8de66a6d463314cf1eb366222074cfda9ee839c56a4b4/mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8", size = 9979, upload-time = "2022-08-14T12:40:09.779Z" }, +] + +[[package]] +name = "mistune" +version = "3.2.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/ca/84/620cc3f7e3adf6f5067e10f4dbae71295d8f9e16d5d3f9ef97c40f2f592c/mistune-3.2.1.tar.gz", hash = "sha256:7c8e5501d38bac1582e067e46c8343f17d57ea1aaa735823f3aba1fd59c88a28", size = 98003, upload-time = "2026-05-03T14:33:22.312Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/2a/7f/a946aa4f8752b37102b41e64dca18a1976ac705c3a0d1dfe74d820a02552/mistune-3.2.1-py3-none-any.whl", hash = "sha256:78cdb0ba5e938053ccf63651b352508d2efa9411dc8810bfb05f2dc5140c0048", size = 53749, upload-time = "2026-05-03T14:33:20.551Z" }, +] + +[[package]] +name = "mpmath" +version = "1.3.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/e0/47/dd32fa426cc72114383ac549964eecb20ecfd886d1e5ccf5340b55b02f57/mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f", size = 508106, upload-time = "2023-03-07T16:47:11.061Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/43/e3/7d92a15f894aa0c9c4b49b8ee9ac9850d6e63b03c9c32c0367a13ae62209/mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c", size = 536198, upload-time = "2023-03-07T16:47:09.197Z" }, +] + +[[package]] +name = "multidict" +version = "6.7.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/1a/c2/c2d94cbe6ac1753f3fc980da97b3d930efe1da3af3c9f5125354436c073d/multidict-6.7.1.tar.gz", hash = "sha256:ec6652a1bee61c53a3e5776b6049172c53b6aaba34f18c9ad04f82712bac623d", size = 102010, upload-time = "2026-01-26T02:46:45.979Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f2/22/929c141d6c0dba87d3e1d38fbdf1ba8baba86b7776469f2bc2d3227a1e67/multidict-6.7.1-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:2b41f5fed0ed563624f1c17630cb9941cf2309d4df00e494b551b5f3e3d67a23", size = 76174, upload-time = "2026-01-26T02:44:18.509Z" }, + { url = "https://files.pythonhosted.org/packages/c7/75/bc704ae15fee974f8fccd871305e254754167dce5f9e42d88a2def741a1d/multidict-6.7.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:84e61e3af5463c19b67ced91f6c634effb89ef8bfc5ca0267f954451ed4bb6a2", size = 45116, upload-time = "2026-01-26T02:44:19.745Z" }, + { url = "https://files.pythonhosted.org/packages/79/76/55cd7186f498ed080a18440c9013011eb548f77ae1b297206d030eb1180a/multidict-6.7.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:935434b9853c7c112eee7ac891bc4cb86455aa631269ae35442cb316790c1445", size = 43524, upload-time = "2026-01-26T02:44:21.571Z" }, + { url = "https://files.pythonhosted.org/packages/e9/3c/414842ef8d5a1628d68edee29ba0e5bcf235dbfb3ccd3ea303a7fe8c72ff/multidict-6.7.1-cp313-cp313-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:432feb25a1cb67fe82a9680b4d65fb542e4635cb3166cd9c01560651ad60f177", size = 249368, upload-time = "2026-01-26T02:44:22.803Z" }, + { url = "https://files.pythonhosted.org/packages/f6/32/befed7f74c458b4a525e60519fe8d87eef72bb1e99924fa2b0f9d97a221e/multidict-6.7.1-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e82d14e3c948952a1a85503817e038cba5905a3352de76b9a465075d072fba23", size = 256952, upload-time = "2026-01-26T02:44:24.306Z" }, + { url = "https://files.pythonhosted.org/packages/03/d6/c878a44ba877f366630c860fdf74bfb203c33778f12b6ac274936853c451/multidict-6.7.1-cp313-cp313-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:4cfb48c6ea66c83bcaaf7e4dfa7ec1b6bbcf751b7db85a328902796dfde4c060", size = 240317, upload-time = "2026-01-26T02:44:25.772Z" }, + { url = "https://files.pythonhosted.org/packages/68/49/57421b4d7ad2e9e60e25922b08ceb37e077b90444bde6ead629095327a6f/multidict-6.7.1-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:1d540e51b7e8e170174555edecddbd5538105443754539193e3e1061864d444d", size = 267132, upload-time = "2026-01-26T02:44:27.648Z" }, + { url = "https://files.pythonhosted.org/packages/b7/fe/ec0edd52ddbcea2a2e89e174f0206444a61440b40f39704e64dc807a70bd/multidict-6.7.1-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:273d23f4b40f3dce4d6c8a821c741a86dec62cded82e1175ba3d99be128147ed", size = 268140, upload-time = "2026-01-26T02:44:29.588Z" }, + { url = "https://files.pythonhosted.org/packages/b0/73/6e1b01cbeb458807aa0831742232dbdd1fa92bfa33f52a3f176b4ff3dc11/multidict-6.7.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9d624335fd4fa1c08a53f8b4be7676ebde19cd092b3895c421045ca87895b429", size = 254277, upload-time = "2026-01-26T02:44:30.902Z" }, + { url = "https://files.pythonhosted.org/packages/6a/b2/5fb8c124d7561a4974c342bc8c778b471ebbeb3cc17df696f034a7e9afe7/multidict-6.7.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:12fad252f8b267cc75b66e8fc51b3079604e8d43a75428ffe193cd9e2195dfd6", size = 252291, upload-time = "2026-01-26T02:44:32.31Z" }, + { url = "https://files.pythonhosted.org/packages/5a/96/51d4e4e06bcce92577fcd488e22600bd38e4fd59c20cb49434d054903bd2/multidict-6.7.1-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:03ede2a6ffbe8ef936b92cb4529f27f42be7f56afcdab5ab739cd5f27fb1cbf9", size = 250156, upload-time = "2026-01-26T02:44:33.734Z" }, + { url = "https://files.pythonhosted.org/packages/db/6b/420e173eec5fba721a50e2a9f89eda89d9c98fded1124f8d5c675f7a0c0f/multidict-6.7.1-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:90efbcf47dbe33dcf643a1e400d67d59abeac5db07dc3f27d6bdeae497a2198c", size = 249742, upload-time = "2026-01-26T02:44:35.222Z" }, + { url = "https://files.pythonhosted.org/packages/44/a3/ec5b5bd98f306bc2aa297b8c6f11a46714a56b1e6ef5ebda50a4f5d7c5fb/multidict-6.7.1-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:5c4b9bfc148f5a91be9244d6264c53035c8a0dcd2f51f1c3c6e30e30ebaa1c84", size = 262221, upload-time = "2026-01-26T02:44:36.604Z" }, + { url = "https://files.pythonhosted.org/packages/cd/f7/e8c0d0da0cd1e28d10e624604e1a36bcc3353aaebdfdc3a43c72bc683a12/multidict-6.7.1-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:401c5a650f3add2472d1d288c26deebc540f99e2fb83e9525007a74cd2116f1d", size = 258664, upload-time = "2026-01-26T02:44:38.008Z" }, + { url = "https://files.pythonhosted.org/packages/52/da/151a44e8016dd33feed44f730bd856a66257c1ee7aed4f44b649fb7edeb3/multidict-6.7.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:97891f3b1b3ffbded884e2916cacf3c6fc87b66bb0dde46f7357404750559f33", size = 249490, upload-time = "2026-01-26T02:44:39.386Z" }, + { url = "https://files.pythonhosted.org/packages/87/af/a3b86bf9630b732897f6fc3f4c4714b90aa4361983ccbdcd6c0339b21b0c/multidict-6.7.1-cp313-cp313-win32.whl", hash = "sha256:e1c5988359516095535c4301af38d8a8838534158f649c05dd1050222321bcb3", size = 41695, upload-time = "2026-01-26T02:44:41.318Z" }, + { url = "https://files.pythonhosted.org/packages/b2/35/e994121b0e90e46134673422dd564623f93304614f5d11886b1b3e06f503/multidict-6.7.1-cp313-cp313-win_amd64.whl", hash = "sha256:960c83bf01a95b12b08fd54324a4eb1d5b52c88932b5cba5d6e712bb3ed12eb5", size = 45884, upload-time = "2026-01-26T02:44:42.488Z" }, + { url = "https://files.pythonhosted.org/packages/ca/61/42d3e5dbf661242a69c97ea363f2d7b46c567da8eadef8890022be6e2ab0/multidict-6.7.1-cp313-cp313-win_arm64.whl", hash = "sha256:563fe25c678aaba333d5399408f5ec3c383ca5b663e7f774dd179a520b8144df", size = 43122, upload-time = "2026-01-26T02:44:43.664Z" }, + { url = "https://files.pythonhosted.org/packages/6d/b3/e6b21c6c4f314bb956016b0b3ef2162590a529b84cb831c257519e7fde44/multidict-6.7.1-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:c76c4bec1538375dad9d452d246ca5368ad6e1c9039dadcf007ae59c70619ea1", size = 83175, upload-time = "2026-01-26T02:44:44.894Z" }, + { url = "https://files.pythonhosted.org/packages/fb/76/23ecd2abfe0957b234f6c960f4ade497f55f2c16aeb684d4ecdbf1c95791/multidict-6.7.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:57b46b24b5d5ebcc978da4ec23a819a9402b4228b8a90d9c656422b4bdd8a963", size = 48460, upload-time = "2026-01-26T02:44:46.106Z" }, + { url = "https://files.pythonhosted.org/packages/c4/57/a0ed92b23f3a042c36bc4227b72b97eca803f5f1801c1ab77c8a212d455e/multidict-6.7.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:e954b24433c768ce78ab7929e84ccf3422e46deb45a4dc9f93438f8217fa2d34", size = 46930, upload-time = "2026-01-26T02:44:47.278Z" }, + { url = "https://files.pythonhosted.org/packages/b5/66/02ec7ace29162e447f6382c495dc95826bf931d3818799bbef11e8f7df1a/multidict-6.7.1-cp313-cp313t-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:3bd231490fa7217cc832528e1cd8752a96f0125ddd2b5749390f7c3ec8721b65", size = 242582, upload-time = "2026-01-26T02:44:48.604Z" }, + { url = "https://files.pythonhosted.org/packages/58/18/64f5a795e7677670e872673aca234162514696274597b3708b2c0d276cce/multidict-6.7.1-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:253282d70d67885a15c8a7716f3a73edf2d635793ceda8173b9ecc21f2fb8292", size = 250031, upload-time = "2026-01-26T02:44:50.544Z" }, + { url = "https://files.pythonhosted.org/packages/c8/ed/e192291dbbe51a8290c5686f482084d31bcd9d09af24f63358c3d42fd284/multidict-6.7.1-cp313-cp313t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:0b4c48648d7649c9335cf1927a8b87fa692de3dcb15faa676c6a6f1f1aabda43", size = 228596, upload-time = "2026-01-26T02:44:51.951Z" }, + { url = "https://files.pythonhosted.org/packages/1e/7e/3562a15a60cf747397e7f2180b0a11dc0c38d9175a650e75fa1b4d325e15/multidict-6.7.1-cp313-cp313t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:98bc624954ec4d2c7cb074b8eefc2b5d0ce7d482e410df446414355d158fe4ca", size = 257492, upload-time = "2026-01-26T02:44:53.902Z" }, + { url = "https://files.pythonhosted.org/packages/24/02/7d0f9eae92b5249bb50ac1595b295f10e263dd0078ebb55115c31e0eaccd/multidict-6.7.1-cp313-cp313t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:1b99af4d9eec0b49927b4402bcbb58dea89d3e0db8806a4086117019939ad3dd", size = 255899, upload-time = "2026-01-26T02:44:55.316Z" }, + { url = "https://files.pythonhosted.org/packages/00/e3/9b60ed9e23e64c73a5cde95269ef1330678e9c6e34dd4eb6b431b85b5a10/multidict-6.7.1-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6aac4f16b472d5b7dc6f66a0d49dd57b0e0902090be16594dc9ebfd3d17c47e7", size = 247970, upload-time = "2026-01-26T02:44:56.783Z" }, + { url = "https://files.pythonhosted.org/packages/3e/06/538e58a63ed5cfb0bd4517e346b91da32fde409d839720f664e9a4ae4f9d/multidict-6.7.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:21f830fe223215dffd51f538e78c172ed7c7f60c9b96a2bf05c4848ad49921c3", size = 245060, upload-time = "2026-01-26T02:44:58.195Z" }, + { url = "https://files.pythonhosted.org/packages/b2/2f/d743a3045a97c895d401e9bd29aaa09b94f5cbdf1bd561609e5a6c431c70/multidict-6.7.1-cp313-cp313t-musllinux_1_2_armv7l.whl", hash = "sha256:f5dd81c45b05518b9aa4da4aa74e1c93d715efa234fd3e8a179df611cc85e5f4", size = 235888, upload-time = "2026-01-26T02:44:59.57Z" }, + { url = "https://files.pythonhosted.org/packages/38/83/5a325cac191ab28b63c52f14f1131f3b0a55ba3b9aa65a6d0bf2a9b921a0/multidict-6.7.1-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:eb304767bca2bb92fb9c5bd33cedc95baee5bb5f6c88e63706533a1c06ad08c8", size = 243554, upload-time = "2026-01-26T02:45:01.054Z" }, + { url = "https://files.pythonhosted.org/packages/20/1f/9d2327086bd15da2725ef6aae624208e2ef828ed99892b17f60c344e57ed/multidict-6.7.1-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:c9035dde0f916702850ef66460bc4239d89d08df4d02023a5926e7446724212c", size = 252341, upload-time = "2026-01-26T02:45:02.484Z" }, + { url = "https://files.pythonhosted.org/packages/e8/2c/2a1aa0280cf579d0f6eed8ee5211c4f1730bd7e06c636ba2ee6aafda302e/multidict-6.7.1-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:af959b9beeb66c822380f222f0e0a1889331597e81f1ded7f374f3ecb0fd6c52", size = 246391, upload-time = "2026-01-26T02:45:03.862Z" }, + { url = "https://files.pythonhosted.org/packages/e5/03/7ca022ffc36c5a3f6e03b179a5ceb829be9da5783e6fe395f347c0794680/multidict-6.7.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:41f2952231456154ee479651491e94118229844dd7226541788be783be2b5108", size = 243422, upload-time = "2026-01-26T02:45:05.296Z" }, + { url = "https://files.pythonhosted.org/packages/dc/1d/b31650eab6c5778aceed46ba735bd97f7c7d2f54b319fa916c0f96e7805b/multidict-6.7.1-cp313-cp313t-win32.whl", hash = "sha256:df9f19c28adcb40b6aae30bbaa1478c389efd50c28d541d76760199fc1037c32", size = 47770, upload-time = "2026-01-26T02:45:06.754Z" }, + { url = "https://files.pythonhosted.org/packages/ac/5b/2d2d1d522e51285bd61b1e20df8f47ae1a9d80839db0b24ea783b3832832/multidict-6.7.1-cp313-cp313t-win_amd64.whl", hash = "sha256:d54ecf9f301853f2c5e802da559604b3e95bb7a3b01a9c295c6ee591b9882de8", size = 53109, upload-time = "2026-01-26T02:45:08.044Z" }, + { url = "https://files.pythonhosted.org/packages/3d/a3/cc409ba012c83ca024a308516703cf339bdc4b696195644a7215a5164a24/multidict-6.7.1-cp313-cp313t-win_arm64.whl", hash = "sha256:5a37ca18e360377cfda1d62f5f382ff41f2b8c4ccb329ed974cc2e1643440118", size = 45573, upload-time = "2026-01-26T02:45:09.349Z" }, + { url = "https://files.pythonhosted.org/packages/91/cc/db74228a8be41884a567e88a62fd589a913708fcf180d029898c17a9a371/multidict-6.7.1-cp314-cp314-macosx_10_15_universal2.whl", hash = "sha256:8f333ec9c5eb1b7105e3b84b53141e66ca05a19a605368c55450b6ba208cb9ee", size = 75190, upload-time = "2026-01-26T02:45:10.651Z" }, + { url = "https://files.pythonhosted.org/packages/d5/22/492f2246bb5b534abd44804292e81eeaf835388901f0c574bac4eeec73c5/multidict-6.7.1-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:a407f13c188f804c759fc6a9f88286a565c242a76b27626594c133b82883b5c2", size = 44486, upload-time = "2026-01-26T02:45:11.938Z" }, + { url = "https://files.pythonhosted.org/packages/f1/4f/733c48f270565d78b4544f2baddc2fb2a245e5a8640254b12c36ac7ac68e/multidict-6.7.1-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:0e161ddf326db5577c3a4cc2d8648f81456e8a20d40415541587a71620d7a7d1", size = 43219, upload-time = "2026-01-26T02:45:14.346Z" }, + { url = "https://files.pythonhosted.org/packages/24/bb/2c0c2287963f4259c85e8bcbba9182ced8d7fca65c780c38e99e61629d11/multidict-6.7.1-cp314-cp314-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:1e3a8bb24342a8201d178c3b4984c26ba81a577c80d4d525727427460a50c22d", size = 245132, upload-time = "2026-01-26T02:45:15.712Z" }, + { url = "https://files.pythonhosted.org/packages/a7/f9/44d4b3064c65079d2467888794dea218d1601898ac50222ab8a9a8094460/multidict-6.7.1-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:97231140a50f5d447d3164f994b86a0bed7cd016e2682f8650d6a9158e14fd31", size = 252420, upload-time = "2026-01-26T02:45:17.293Z" }, + { url = "https://files.pythonhosted.org/packages/8b/13/78f7275e73fa17b24c9a51b0bd9d73ba64bb32d0ed51b02a746eb876abe7/multidict-6.7.1-cp314-cp314-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:6b10359683bd8806a200fd2909e7c8ca3a7b24ec1d8132e483d58e791d881048", size = 233510, upload-time = "2026-01-26T02:45:19.356Z" }, + { url = "https://files.pythonhosted.org/packages/4b/25/8167187f62ae3cbd52da7893f58cb036b47ea3fb67138787c76800158982/multidict-6.7.1-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:283ddac99f7ac25a4acadbf004cb5ae34480bbeb063520f70ce397b281859362", size = 264094, upload-time = "2026-01-26T02:45:20.834Z" }, + { url = "https://files.pythonhosted.org/packages/a1/e7/69a3a83b7b030cf283fb06ce074a05a02322359783424d7edf0f15fe5022/multidict-6.7.1-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:538cec1e18c067d0e6103aa9a74f9e832904c957adc260e61cd9d8cf0c3b3d37", size = 260786, upload-time = "2026-01-26T02:45:22.818Z" }, + { url = "https://files.pythonhosted.org/packages/fe/3b/8ec5074bcfc450fe84273713b4b0a0dd47c0249358f5d82eb8104ffe2520/multidict-6.7.1-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7eee46ccb30ff48a1e35bb818cc90846c6be2b68240e42a78599166722cea709", size = 248483, upload-time = "2026-01-26T02:45:24.368Z" }, + { url = "https://files.pythonhosted.org/packages/48/5a/d5a99e3acbca0e29c5d9cba8f92ceb15dce78bab963b308ae692981e3a5d/multidict-6.7.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:fa263a02f4f2dd2d11a7b1bb4362aa7cb1049f84a9235d31adf63f30143469a0", size = 248403, upload-time = "2026-01-26T02:45:25.982Z" }, + { url = "https://files.pythonhosted.org/packages/35/48/e58cd31f6c7d5102f2a4bf89f96b9cf7e00b6c6f3d04ecc44417c00a5a3c/multidict-6.7.1-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:2e1425e2f99ec5bd36c15a01b690a1a2456209c5deed58f95469ffb46039ccbb", size = 240315, upload-time = "2026-01-26T02:45:27.487Z" }, + { url = "https://files.pythonhosted.org/packages/94/33/1cd210229559cb90b6786c30676bb0c58249ff42f942765f88793b41fdce/multidict-6.7.1-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:497394b3239fc6f0e13a78a3e1b61296e72bf1c5f94b4c4eb80b265c37a131cd", size = 245528, upload-time = "2026-01-26T02:45:28.991Z" }, + { url = "https://files.pythonhosted.org/packages/64/f2/6e1107d226278c876c783056b7db43d800bb64c6131cec9c8dfb6903698e/multidict-6.7.1-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:233b398c29d3f1b9676b4b6f75c518a06fcb2ea0b925119fb2c1bc35c05e1601", size = 258784, upload-time = "2026-01-26T02:45:30.503Z" }, + { url = "https://files.pythonhosted.org/packages/4d/c1/11f664f14d525e4a1b5327a82d4de61a1db604ab34c6603bb3c2cc63ad34/multidict-6.7.1-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:93b1818e4a6e0930454f0f2af7dfce69307ca03cdcfb3739bf4d91241967b6c1", size = 251980, upload-time = "2026-01-26T02:45:32.603Z" }, + { url = "https://files.pythonhosted.org/packages/e1/9f/75a9ac888121d0c5bbd4ecf4eead45668b1766f6baabfb3b7f66a410e231/multidict-6.7.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:f33dc2a3abe9249ea5d8360f969ec7f4142e7ac45ee7014d8f8d5acddf178b7b", size = 243602, upload-time = "2026-01-26T02:45:34.043Z" }, + { url = "https://files.pythonhosted.org/packages/9a/e7/50bf7b004cc8525d80dbbbedfdc7aed3e4c323810890be4413e589074032/multidict-6.7.1-cp314-cp314-win32.whl", hash = "sha256:3ab8b9d8b75aef9df299595d5388b14530839f6422333357af1339443cff777d", size = 40930, upload-time = "2026-01-26T02:45:36.278Z" }, + { url = "https://files.pythonhosted.org/packages/e0/bf/52f25716bbe93745595800f36fb17b73711f14da59ed0bb2eba141bc9f0f/multidict-6.7.1-cp314-cp314-win_amd64.whl", hash = "sha256:5e01429a929600e7dab7b166062d9bb54a5eed752384c7384c968c2afab8f50f", size = 45074, upload-time = "2026-01-26T02:45:37.546Z" }, + { url = "https://files.pythonhosted.org/packages/97/ab/22803b03285fa3a525f48217963da3a65ae40f6a1b6f6cf2768879e208f9/multidict-6.7.1-cp314-cp314-win_arm64.whl", hash = "sha256:4885cb0e817aef5d00a2e8451d4665c1808378dc27c2705f1bf4ef8505c0d2e5", size = 42471, upload-time = "2026-01-26T02:45:38.889Z" }, + { url = "https://files.pythonhosted.org/packages/e0/6d/f9293baa6146ba9507e360ea0292b6422b016907c393e2f63fc40ab7b7b5/multidict-6.7.1-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:0458c978acd8e6ea53c81eefaddbbee9c6c5e591f41b3f5e8e194780fe026581", size = 82401, upload-time = "2026-01-26T02:45:40.254Z" }, + { url = "https://files.pythonhosted.org/packages/7a/68/53b5494738d83558d87c3c71a486504d8373421c3e0dbb6d0db48ad42ee0/multidict-6.7.1-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:c0abd12629b0af3cf590982c0b413b1e7395cd4ec026f30986818ab95bfaa94a", size = 48143, upload-time = "2026-01-26T02:45:41.635Z" }, + { url = "https://files.pythonhosted.org/packages/37/e8/5284c53310dcdc99ce5d66563f6e5773531a9b9fe9ec7a615e9bc306b05f/multidict-6.7.1-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:14525a5f61d7d0c94b368a42cff4c9a4e7ba2d52e2672a7b23d84dc86fb02b0c", size = 46507, upload-time = "2026-01-26T02:45:42.99Z" }, + { url = "https://files.pythonhosted.org/packages/e4/fc/6800d0e5b3875568b4083ecf5f310dcf91d86d52573160834fb4bfcf5e4f/multidict-6.7.1-cp314-cp314t-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:17307b22c217b4cf05033dabefe68255a534d637c6c9b0cc8382718f87be4262", size = 239358, upload-time = "2026-01-26T02:45:44.376Z" }, + { url = "https://files.pythonhosted.org/packages/41/75/4ad0973179361cdf3a113905e6e088173198349131be2b390f9fa4da5fc6/multidict-6.7.1-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7a7e590ff876a3eaf1c02a4dfe0724b6e69a9e9de6d8f556816f29c496046e59", size = 246884, upload-time = "2026-01-26T02:45:47.167Z" }, + { url = "https://files.pythonhosted.org/packages/c3/9c/095bb28b5da139bd41fb9a5d5caff412584f377914bd8787c2aa98717130/multidict-6.7.1-cp314-cp314t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:5fa6a95dfee63893d80a34758cd0e0c118a30b8dcb46372bf75106c591b77889", size = 225878, upload-time = "2026-01-26T02:45:48.698Z" }, + { url = "https://files.pythonhosted.org/packages/07/d0/c0a72000243756e8f5a277b6b514fa005f2c73d481b7d9e47cd4568aa2e4/multidict-6.7.1-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a0543217a6a017692aa6ae5cc39adb75e587af0f3a82288b1492eb73dd6cc2a4", size = 253542, upload-time = "2026-01-26T02:45:50.164Z" }, + { url = "https://files.pythonhosted.org/packages/c0/6b/f69da15289e384ecf2a68837ec8b5ad8c33e973aa18b266f50fe55f24b8c/multidict-6.7.1-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:f99fe611c312b3c1c0ace793f92464d8cd263cc3b26b5721950d977b006b6c4d", size = 252403, upload-time = "2026-01-26T02:45:51.779Z" }, + { url = "https://files.pythonhosted.org/packages/a2/76/b9669547afa5a1a25cd93eaca91c0da1c095b06b6d2d8ec25b713588d3a1/multidict-6.7.1-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9004d8386d133b7e6135679424c91b0b854d2d164af6ea3f289f8f2761064609", size = 244889, upload-time = "2026-01-26T02:45:53.27Z" }, + { url = "https://files.pythonhosted.org/packages/7e/a9/a50d2669e506dad33cfc45b5d574a205587b7b8a5f426f2fbb2e90882588/multidict-6.7.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:e628ef0e6859ffd8273c69412a2465c4be4a9517d07261b33334b5ec6f3c7489", size = 241982, upload-time = "2026-01-26T02:45:54.919Z" }, + { url = "https://files.pythonhosted.org/packages/c5/bb/1609558ad8b456b4827d3c5a5b775c93b87878fd3117ed3db3423dfbce1b/multidict-6.7.1-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:841189848ba629c3552035a6a7f5bf3b02eb304e9fea7492ca220a8eda6b0e5c", size = 232415, upload-time = "2026-01-26T02:45:56.981Z" }, + { url = "https://files.pythonhosted.org/packages/d8/59/6f61039d2aa9261871e03ab9dc058a550d240f25859b05b67fd70f80d4b3/multidict-6.7.1-cp314-cp314t-musllinux_1_2_i686.whl", hash = "sha256:ce1bbd7d780bb5a0da032e095c951f7014d6b0a205f8318308140f1a6aba159e", size = 240337, upload-time = "2026-01-26T02:45:58.698Z" }, + { url = "https://files.pythonhosted.org/packages/a1/29/fdc6a43c203890dc2ae9249971ecd0c41deaedfe00d25cb6564b2edd99eb/multidict-6.7.1-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:b26684587228afed0d50cf804cc71062cc9c1cdf55051c4c6345d372947b268c", size = 248788, upload-time = "2026-01-26T02:46:00.862Z" }, + { url = "https://files.pythonhosted.org/packages/a9/14/a153a06101323e4cf086ecee3faadba52ff71633d471f9685c42e3736163/multidict-6.7.1-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:9f9af11306994335398293f9958071019e3ab95e9a707dc1383a35613f6abcb9", size = 242842, upload-time = "2026-01-26T02:46:02.824Z" }, + { url = "https://files.pythonhosted.org/packages/41/5f/604ae839e64a4a6efc80db94465348d3b328ee955e37acb24badbcd24d83/multidict-6.7.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:b4938326284c4f1224178a560987b6cf8b4d38458b113d9b8c1db1a836e640a2", size = 240237, upload-time = "2026-01-26T02:46:05.898Z" }, + { url = "https://files.pythonhosted.org/packages/5f/60/c3a5187bf66f6fb546ff4ab8fb5a077cbdd832d7b1908d4365c7f74a1917/multidict-6.7.1-cp314-cp314t-win32.whl", hash = "sha256:98655c737850c064a65e006a3df7c997cd3b220be4ec8fe26215760b9697d4d7", size = 48008, upload-time = "2026-01-26T02:46:07.468Z" }, + { url = "https://files.pythonhosted.org/packages/0c/f7/addf1087b860ac60e6f382240f64fb99f8bfb532bb06f7c542b83c29ca61/multidict-6.7.1-cp314-cp314t-win_amd64.whl", hash = "sha256:497bde6223c212ba11d462853cfa4f0ae6ef97465033e7dc9940cdb3ab5b48e5", size = 53542, upload-time = "2026-01-26T02:46:08.809Z" }, + { url = "https://files.pythonhosted.org/packages/4c/81/4629d0aa32302ef7b2ec65c75a728cc5ff4fa410c50096174c1632e70b3e/multidict-6.7.1-cp314-cp314t-win_arm64.whl", hash = "sha256:2bbd113e0d4af5db41d5ebfe9ccaff89de2120578164f86a5d17d5a576d1e5b2", size = 44719, upload-time = "2026-01-26T02:46:11.146Z" }, + { url = "https://files.pythonhosted.org/packages/81/08/7036c080d7117f28a4af526d794aab6a84463126db031b007717c1a6676e/multidict-6.7.1-py3-none-any.whl", hash = "sha256:55d97cc6dae627efa6a6e548885712d4864b81110ac76fa4e534c03819fa4a56", size = 12319, upload-time = "2026-01-26T02:46:44.004Z" }, +] + +[[package]] +name = "multiprocess" +version = "0.70.19" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "dill" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/a2/f2/e783ac7f2aeeed14e9e12801f22529cc7e6b7ab80928d6dcce4e9f00922d/multiprocess-0.70.19.tar.gz", hash = "sha256:952021e0e6c55a4a9fe4cd787895b86e239a40e76802a789d6305398d3975897", size = 2079989, upload-time = "2026-01-19T06:47:39.744Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e3/45/8004d1e6b9185c1a444d6b55ac5682acf9d98035e54386d967366035a03a/multiprocess-0.70.19-py310-none-any.whl", hash = "sha256:97404393419dcb2a8385910864eedf47a3cadf82c66345b44f036420eb0b5d87", size = 134948, upload-time = "2026-01-19T06:47:32.325Z" }, + { url = "https://files.pythonhosted.org/packages/86/c2/dec9722dc3474c164a0b6bcd9a7ed7da542c98af8cabce05374abab35edd/multiprocess-0.70.19-py311-none-any.whl", hash = "sha256:928851ae7973aea4ce0eaf330bbdafb2e01398a91518d5c8818802845564f45c", size = 144457, upload-time = "2026-01-19T06:47:33.711Z" }, + { url = "https://files.pythonhosted.org/packages/71/70/38998b950a97ea279e6bd657575d22d1a2047256caf707d9a10fbce4f065/multiprocess-0.70.19-py312-none-any.whl", hash = "sha256:3a56c0e85dd5025161bac5ce138dcac1e49174c7d8e74596537e729fd5c53c28", size = 150281, upload-time = "2026-01-19T06:47:35.037Z" }, + { url = "https://files.pythonhosted.org/packages/7f/74/d2c27e03cb84251dfe7249b8e82923643c6d48fa4883b9476b025e7dc7eb/multiprocess-0.70.19-py313-none-any.whl", hash = "sha256:8d5eb4ec5017ba2fab4e34a747c6d2c2b6fecfe9e7236e77988db91580ada952", size = 156414, upload-time = "2026-01-19T06:47:35.915Z" }, + { url = "https://files.pythonhosted.org/packages/a0/61/af9115673a5870fd885247e2f1b68c4f1197737da315b520a91c757a861a/multiprocess-0.70.19-py314-none-any.whl", hash = "sha256:e8cc7fbdff15c0613f0a1f1f8744bef961b0a164c0ca29bdff53e9d2d93c5e5f", size = 160318, upload-time = "2026-01-19T06:47:37.497Z" }, + { url = "https://files.pythonhosted.org/packages/7e/82/69e539c4c2027f1e1697e09aaa2449243085a0edf81ae2c6341e84d769b6/multiprocess-0.70.19-py39-none-any.whl", hash = "sha256:0d4b4397ed669d371c81dcd1ef33fd384a44d6c3de1bd0ca7ac06d837720d3c5", size = 133477, upload-time = "2026-01-19T06:47:38.619Z" }, +] + +[[package]] +name = "nbclient" +version = "0.10.4" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "jupyter-client" }, + { name = "jupyter-core" }, + { name = "nbformat" }, + { name = "traitlets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/56/91/1c1d5a4b9a9ebba2b4e32b8c852c2975c872aec1fe42ab5e516b2cecd193/nbclient-0.10.4.tar.gz", hash = "sha256:1e54091b16e6da39e297b0ece3e10f6f29f4ac4e8ee515d29f8a7099bd6553c9", size = 62554, upload-time = "2025-12-23T07:45:46.369Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/83/a0/5b0c2f11142ed1dddec842457d3f65eaf71a0080894eb6f018755b319c3a/nbclient-0.10.4-py3-none-any.whl", hash = "sha256:9162df5a7373d70d606527300a95a975a47c137776cd942e52d9c7e29ff83440", size = 25465, upload-time = "2025-12-23T07:45:44.51Z" }, +] + +[[package]] +name = "nbconvert" +version = "7.17.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "beautifulsoup4" }, + { name = "bleach", extra = ["css"] }, + { name = "defusedxml" }, + { name = "jinja2" }, + { name = "jupyter-core" }, + { name = "jupyterlab-pygments" }, + { name = "markupsafe" }, + { name = "mistune" }, + { name = "nbclient" }, + { name = "nbformat" }, + { name = "packaging" }, + { name = "pandocfilters" }, + { name = "pygments" }, + { name = "traitlets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/01/b1/708e53fe2e429c103c6e6e159106bcf0357ac41aa4c28772bd8402339051/nbconvert-7.17.1.tar.gz", hash = "sha256:34d0d0a7e73ce3cbab6c5aae8f4f468797280b01fd8bd2ca746da8569eddd7d2", size = 865311, upload-time = "2026-04-08T00:44:14.914Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/67/f8/bb0a9d5f46819c821dc1f004aa2cc29b1d91453297dbf5ff20470f00f193/nbconvert-7.17.1-py3-none-any.whl", hash = "sha256:aa85c087b435e7bf1ffd03319f658e285f2b89eccab33bc1ba7025495ab3e7c8", size = 261927, upload-time = "2026-04-08T00:44:12.845Z" }, +] + +[[package]] +name = "nbformat" +version = "5.10.4" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "fastjsonschema" }, + { name = "jsonschema" }, + { name = "jupyter-core" }, + { name = "traitlets" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/6d/fd/91545e604bc3dad7dca9ed03284086039b294c6b3d75c0d2fa45f9e9caf3/nbformat-5.10.4.tar.gz", hash = "sha256:322168b14f937a5d11362988ecac2a4952d3d8e3a2cbeb2319584631226d5b3a", size = 142749, upload-time = "2024-04-04T11:20:37.371Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/a9/82/0340caa499416c78e5d8f5f05947ae4bc3cba53c9f038ab6e9ed964e22f1/nbformat-5.10.4-py3-none-any.whl", hash = "sha256:3b48d6c8fbca4b299bf3982ea7db1af21580e4fec269ad087b9e81588891200b", size = 78454, upload-time = "2024-04-04T11:20:34.895Z" }, +] + +[[package]] +name = "nest-asyncio" +version = "1.6.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/83/f8/51569ac65d696c8ecbee95938f89d4abf00f47d58d48f6fbabfe8f0baefe/nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe", size = 7418, upload-time = "2024-01-21T14:25:19.227Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/a0/c4/c2971a3ba4c6103a3d10c4b0f24f461ddc027f0f09763220cf35ca1401b3/nest_asyncio-1.6.0-py3-none-any.whl", hash = "sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c", size = 5195, upload-time = "2024-01-21T14:25:17.223Z" }, +] + +[[package]] +name = "networkx" +version = "3.6.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/6a/51/63fe664f3908c97be9d2e4f1158eb633317598cfa6e1fc14af5383f17512/networkx-3.6.1.tar.gz", hash = "sha256:26b7c357accc0c8cde558ad486283728b65b6a95d85ee1cd66bafab4c8168509", size = 2517025, upload-time = "2025-12-08T17:02:39.908Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/9e/c9/b2622292ea83fbb4ec318f5b9ab867d0a28ab43c5717bb85b0a5f6b3b0a4/networkx-3.6.1-py3-none-any.whl", hash = "sha256:d47fbf302e7d9cbbb9e2555a0d267983d2aa476bac30e90dfbe5669bd57f3762", size = 2068504, upload-time = "2025-12-08T17:02:38.159Z" }, +] + +[[package]] +name = "notebook" +version = "7.5.6" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "jupyter-server" }, + { name = "jupyterlab" }, + { name = "jupyterlab-server" }, + { name = "notebook-shim" }, + { name = "tornado" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/2a/c2/cf59bd2e6f2c8b976b52477e3e53bf6f97bc714ed046a51821afb428eaee/notebook-7.5.6.tar.gz", hash = "sha256:621174aade80108f0020b0f00738000b215f75fa3cd90771ad7aa0f24536a4e1", size = 14170814, upload-time = "2026-04-30T11:46:26.613Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e9/d6/1fd0646b9bbd9efbb0b8ae21b2325fbef515769a5621c03e31d8eb8da587/notebook-7.5.6-py3-none-any.whl", hash = "sha256:4dde3f8fb55fa8fb7946d58c6e869ce9baf46d00fc070664f62604569d0faca0", size = 14581730, upload-time = "2026-04-30T11:46:22.342Z" }, +] + +[[package]] +name = "notebook-shim" +version = "0.2.4" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "jupyter-server" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/54/d2/92fa3243712b9a3e8bafaf60aac366da1cada3639ca767ff4b5b3654ec28/notebook_shim-0.2.4.tar.gz", hash = "sha256:b4b2cfa1b65d98307ca24361f5b30fe785b53c3fd07b7a47e89acb5e6ac638cb", size = 13167, upload-time = "2024-02-14T23:35:18.353Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f9/33/bd5b9137445ea4b680023eb0469b2bb969d61303dedb2aac6560ff3d14a1/notebook_shim-0.2.4-py3-none-any.whl", hash = "sha256:411a5be4e9dc882a074ccbcae671eda64cceb068767e9a3419096986560e1cef", size = 13307, upload-time = "2024-02-14T23:35:16.286Z" }, +] + +[[package]] +name = "numpy" +version = "2.4.4" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/d7/9f/b8cef5bffa569759033adda9481211426f12f53299629b410340795c2514/numpy-2.4.4.tar.gz", hash = "sha256:2d390634c5182175533585cc89f3608a4682ccb173cc9bb940b2881c8d6f8fa0", size = 20731587, upload-time = "2026-03-29T13:22:01.298Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/14/1d/d0a583ce4fefcc3308806a749a536c201ed6b5ad6e1322e227ee4848979d/numpy-2.4.4-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:08f2e31ed5e6f04b118e49821397f12767934cfdd12a1ce86a058f91e004ee50", size = 16684933, upload-time = "2026-03-29T13:19:22.47Z" }, + { url = "https://files.pythonhosted.org/packages/c1/62/2b7a48fbb745d344742c0277f01286dead15f3f68e4f359fbfcf7b48f70f/numpy-2.4.4-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:e823b8b6edc81e747526f70f71a9c0a07ac4e7ad13020aa736bb7c9d67196115", size = 14694532, upload-time = "2026-03-29T13:19:25.581Z" }, + { url = "https://files.pythonhosted.org/packages/e5/87/499737bfba066b4a3bebff24a8f1c5b2dee410b209bc6668c9be692580f0/numpy-2.4.4-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:4a19d9dba1a76618dd86b164d608566f393f8ec6ac7c44f0cc879011c45e65af", size = 5199661, upload-time = "2026-03-29T13:19:28.31Z" }, + { url = "https://files.pythonhosted.org/packages/cd/da/464d551604320d1491bc345efed99b4b7034143a85787aab78d5691d5a0e/numpy-2.4.4-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:d2a8490669bfe99a233298348acc2d824d496dee0e66e31b66a6022c2ad74a5c", size = 6547539, upload-time = "2026-03-29T13:19:30.97Z" }, + { url = "https://files.pythonhosted.org/packages/7d/90/8d23e3b0dafd024bf31bdec225b3bb5c2dbfa6912f8a53b8659f21216cbf/numpy-2.4.4-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:45dbed2ab436a9e826e302fcdcbe9133f9b0006e5af7168afb8963a6520da103", size = 15668806, upload-time = "2026-03-29T13:19:33.887Z" }, + { url = "https://files.pythonhosted.org/packages/d1/73/a9d864e42a01896bb5974475438f16086be9ba1f0d19d0bb7a07427c4a8b/numpy-2.4.4-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c901b15172510173f5cb310eae652908340f8dede90fff9e3bf6c0d8dfd92f83", size = 16632682, upload-time = "2026-03-29T13:19:37.336Z" }, + { url = "https://files.pythonhosted.org/packages/34/fb/14570d65c3bde4e202a031210475ae9cde9b7686a2e7dc97ee67d2833b35/numpy-2.4.4-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:99d838547ace2c4aace6c4f76e879ddfe02bb58a80c1549928477862b7a6d6ed", size = 17019810, upload-time = "2026-03-29T13:19:40.963Z" }, + { url = "https://files.pythonhosted.org/packages/8a/77/2ba9d87081fd41f6d640c83f26fb7351e536b7ce6dd9061b6af5904e8e46/numpy-2.4.4-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:0aec54fd785890ecca25a6003fd9a5aed47ad607bbac5cd64f836ad8666f4959", size = 18357394, upload-time = "2026-03-29T13:19:44.859Z" }, + { url = "https://files.pythonhosted.org/packages/a2/23/52666c9a41708b0853fa3b1a12c90da38c507a3074883823126d4e9d5b30/numpy-2.4.4-cp313-cp313-win32.whl", hash = "sha256:07077278157d02f65c43b1b26a3886bce886f95d20aabd11f87932750dfb14ed", size = 5959556, upload-time = "2026-03-29T13:19:47.661Z" }, + { url = "https://files.pythonhosted.org/packages/57/fb/48649b4971cde70d817cf97a2a2fdc0b4d8308569f1dd2f2611959d2e0cf/numpy-2.4.4-cp313-cp313-win_amd64.whl", hash = "sha256:5c70f1cc1c4efbe316a572e2d8b9b9cc44e89b95f79ca3331553fbb63716e2bf", size = 12317311, upload-time = "2026-03-29T13:19:50.67Z" }, + { url = "https://files.pythonhosted.org/packages/ba/d8/11490cddd564eb4de97b4579ef6bfe6a736cc07e94c1598590ae25415e01/numpy-2.4.4-cp313-cp313-win_arm64.whl", hash = "sha256:ef4059d6e5152fa1a39f888e344c73fdc926e1b2dd58c771d67b0acfbf2aa67d", size = 10222060, upload-time = "2026-03-29T13:19:54.229Z" }, + { url = "https://files.pythonhosted.org/packages/99/5d/dab4339177a905aad3e2221c915b35202f1ec30d750dd2e5e9d9a72b804b/numpy-2.4.4-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:4bbc7f303d125971f60ec0aaad5e12c62d0d2c925f0ab1273debd0e4ba37aba5", size = 14822302, upload-time = "2026-03-29T13:19:57.585Z" }, + { url = "https://files.pythonhosted.org/packages/eb/e4/0564a65e7d3d97562ed6f9b0fd0fb0a6f559ee444092f105938b50043876/numpy-2.4.4-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:4d6d57903571f86180eb98f8f0c839fa9ebbfb031356d87f1361be91e433f5b7", size = 5327407, upload-time = "2026-03-29T13:20:00.601Z" }, + { url = "https://files.pythonhosted.org/packages/29/8d/35a3a6ce5ad371afa58b4700f1c820f8f279948cca32524e0a695b0ded83/numpy-2.4.4-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:4636de7fd195197b7535f231b5de9e4b36d2c440b6e566d2e4e4746e6af0ca93", size = 6647631, upload-time = "2026-03-29T13:20:02.855Z" }, + { url = "https://files.pythonhosted.org/packages/f4/da/477731acbd5a58a946c736edfdabb2ac5b34c3d08d1ba1a7b437fa0884df/numpy-2.4.4-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ad2e2ef14e0b04e544ea2fa0a36463f847f113d314aa02e5b402fdf910ef309e", size = 15727691, upload-time = "2026-03-29T13:20:06.004Z" }, + { url = "https://files.pythonhosted.org/packages/e6/db/338535d9b152beabeb511579598418ba0212ce77cf9718edd70262cc4370/numpy-2.4.4-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5a285b3b96f951841799528cd1f4f01cd70e7e0204b4abebac9463eecfcf2a40", size = 16681241, upload-time = "2026-03-29T13:20:09.417Z" }, + { url = "https://files.pythonhosted.org/packages/e2/a9/ad248e8f58beb7a0219b413c9c7d8151c5d285f7f946c3e26695bdbbe2df/numpy-2.4.4-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:f8474c4241bc18b750be2abea9d7a9ec84f46ef861dbacf86a4f6e043401f79e", size = 17085767, upload-time = "2026-03-29T13:20:13.126Z" }, + { url = "https://files.pythonhosted.org/packages/b5/1a/3b88ccd3694681356f70da841630e4725a7264d6a885c8d442a697e1146b/numpy-2.4.4-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:4e874c976154687c1f71715b034739b45c7711bec81db01914770373d125e392", size = 18403169, upload-time = "2026-03-29T13:20:17.096Z" }, + { url = "https://files.pythonhosted.org/packages/c2/c9/fcfd5d0639222c6eac7f304829b04892ef51c96a75d479214d77e3ce6e33/numpy-2.4.4-cp313-cp313t-win32.whl", hash = "sha256:9c585a1790d5436a5374bac930dad6ed244c046ed91b2b2a3634eb2971d21008", size = 6083477, upload-time = "2026-03-29T13:20:20.195Z" }, + { url = "https://files.pythonhosted.org/packages/d5/e3/3938a61d1c538aaec8ed6fd6323f57b0c2d2d2219512434c5c878db76553/numpy-2.4.4-cp313-cp313t-win_amd64.whl", hash = "sha256:93e15038125dc1e5345d9b5b68aa7f996ec33b98118d18c6ca0d0b7d6198b7e8", size = 12457487, upload-time = "2026-03-29T13:20:22.946Z" }, + { url = "https://files.pythonhosted.org/packages/97/6a/7e345032cc60501721ef94e0e30b60f6b0bd601f9174ebd36389a2b86d40/numpy-2.4.4-cp313-cp313t-win_arm64.whl", hash = "sha256:0dfd3f9d3adbe2920b68b5cd3d51444e13a10792ec7154cd0a2f6e74d4ab3233", size = 10292002, upload-time = "2026-03-29T13:20:25.909Z" }, + { url = "https://files.pythonhosted.org/packages/6e/06/c54062f85f673dd5c04cbe2f14c3acb8c8b95e3384869bb8cc9bff8cb9df/numpy-2.4.4-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:f169b9a863d34f5d11b8698ead99febeaa17a13ca044961aa8e2662a6c7766a0", size = 16684353, upload-time = "2026-03-29T13:20:29.504Z" }, + { url = "https://files.pythonhosted.org/packages/4c/39/8a320264a84404c74cc7e79715de85d6130fa07a0898f67fb5cd5bd79908/numpy-2.4.4-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:2483e4584a1cb3092da4470b38866634bafb223cbcd551ee047633fd2584599a", size = 14704914, upload-time = "2026-03-29T13:20:33.547Z" }, + { url = "https://files.pythonhosted.org/packages/91/fb/287076b2614e1d1044235f50f03748f31fa287e3dbe6abeb35cdfa351eca/numpy-2.4.4-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:2d19e6e2095506d1736b7d80595e0f252d76b89f5e715c35e06e937679ea7d7a", size = 5210005, upload-time = "2026-03-29T13:20:36.45Z" }, + { url = "https://files.pythonhosted.org/packages/63/eb/fcc338595309910de6ecabfcef2419a9ce24399680bfb149421fa2df1280/numpy-2.4.4-cp314-cp314-macosx_14_0_x86_64.whl", hash = "sha256:6a246d5914aa1c820c9443ddcee9c02bec3e203b0c080349533fae17727dfd1b", size = 6544974, upload-time = "2026-03-29T13:20:39.014Z" }, + { url = "https://files.pythonhosted.org/packages/44/5d/e7e9044032a716cdfaa3fba27a8e874bf1c5f1912a1ddd4ed071bf8a14a6/numpy-2.4.4-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:989824e9faf85f96ec9c7761cd8d29c531ad857bfa1daa930cba85baaecf1a9a", size = 15684591, upload-time = "2026-03-29T13:20:42.146Z" }, + { url = "https://files.pythonhosted.org/packages/98/7c/21252050676612625449b4807d6b695b9ce8a7c9e1c197ee6216c8a65c7c/numpy-2.4.4-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:27a8d92cd10f1382a67d7cf4db7ce18341b66438bdd9f691d7b0e48d104c2a9d", size = 16637700, upload-time = "2026-03-29T13:20:46.204Z" }, + { url = "https://files.pythonhosted.org/packages/b1/29/56d2bbef9465db24ef25393383d761a1af4f446a1df9b8cded4fe3a5a5d7/numpy-2.4.4-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:e44319a2953c738205bf3354537979eaa3998ed673395b964c1176083dd46252", size = 17035781, upload-time = "2026-03-29T13:20:50.242Z" }, + { url = "https://files.pythonhosted.org/packages/e3/2b/a35a6d7589d21f44cea7d0a98de5ddcbb3d421b2622a5c96b1edf18707c3/numpy-2.4.4-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:e892aff75639bbef0d2a2cfd55535510df26ff92f63c92cd84ef8d4ba5a5557f", size = 18362959, upload-time = "2026-03-29T13:20:54.019Z" }, + { url = "https://files.pythonhosted.org/packages/64/c9/d52ec581f2390e0f5f85cbfd80fb83d965fc15e9f0e1aec2195faa142cde/numpy-2.4.4-cp314-cp314-win32.whl", hash = "sha256:1378871da56ca8943c2ba674530924bb8ca40cd228358a3b5f302ad60cf875fc", size = 6008768, upload-time = "2026-03-29T13:20:56.912Z" }, + { url = "https://files.pythonhosted.org/packages/fa/22/4cc31a62a6c7b74a8730e31a4274c5dc80e005751e277a2ce38e675e4923/numpy-2.4.4-cp314-cp314-win_amd64.whl", hash = "sha256:715d1c092715954784bc79e1174fc2a90093dc4dc84ea15eb14dad8abdcdeb74", size = 12449181, upload-time = "2026-03-29T13:20:59.548Z" }, + { url = "https://files.pythonhosted.org/packages/70/2e/14cda6f4d8e396c612d1bf97f22958e92148801d7e4f110cabebdc0eef4b/numpy-2.4.4-cp314-cp314-win_arm64.whl", hash = "sha256:2c194dd721e54ecad9ad387c1d35e63dce5c4450c6dc7dd5611283dda239aabb", size = 10496035, upload-time = "2026-03-29T13:21:02.524Z" }, + { url = "https://files.pythonhosted.org/packages/b1/e8/8fed8c8d848d7ecea092dc3469643f9d10bc3a134a815a3b033da1d2039b/numpy-2.4.4-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:2aa0613a5177c264ff5921051a5719d20095ea586ca88cc802c5c218d1c67d3e", size = 14824958, upload-time = "2026-03-29T13:21:05.671Z" }, + { url = "https://files.pythonhosted.org/packages/05/1a/d8007a5138c179c2bf33ef44503e83d70434d2642877ee8fbb230e7c0548/numpy-2.4.4-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:42c16925aa5a02362f986765f9ebabf20de75cdefdca827d14315c568dcab113", size = 5330020, upload-time = "2026-03-29T13:21:08.635Z" }, + { url = "https://files.pythonhosted.org/packages/99/64/ffb99ac6ae93faf117bcbd5c7ba48a7f45364a33e8e458545d3633615dda/numpy-2.4.4-cp314-cp314t-macosx_14_0_x86_64.whl", hash = "sha256:874f200b2a981c647340f841730fc3a2b54c9d940566a3c4149099591e2c4c3d", size = 6650758, upload-time = "2026-03-29T13:21:10.949Z" }, + { url = "https://files.pythonhosted.org/packages/6e/6e/795cc078b78a384052e73b2f6281ff7a700e9bf53bcce2ee579d4f6dd879/numpy-2.4.4-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c9b39d38a9bd2ae1becd7eac1303d031c5c110ad31f2b319c6e7d98b135c934d", size = 15729948, upload-time = "2026-03-29T13:21:14.047Z" }, + { url = "https://files.pythonhosted.org/packages/5f/86/2acbda8cc2af5f3d7bfc791192863b9e3e19674da7b5e533fded124d1299/numpy-2.4.4-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b268594bccac7d7cf5844c7732e3f20c50921d94e36d7ec9b79e9857694b1b2f", size = 16679325, upload-time = "2026-03-29T13:21:17.561Z" }, + { url = "https://files.pythonhosted.org/packages/bc/59/cafd83018f4aa55e0ac6fa92aa066c0a1877b77a615ceff1711c260ffae8/numpy-2.4.4-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:ac6b31e35612a26483e20750126d30d0941f949426974cace8e6b5c58a3657b0", size = 17084883, upload-time = "2026-03-29T13:21:21.106Z" }, + { url = "https://files.pythonhosted.org/packages/f0/85/a42548db84e65ece46ab2caea3d3f78b416a47af387fcbb47ec28e660dc2/numpy-2.4.4-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:8e3ed142f2728df44263aaf5fb1f5b0b99f4070c553a0d7f033be65338329150", size = 18403474, upload-time = "2026-03-29T13:21:24.828Z" }, + { url = "https://files.pythonhosted.org/packages/ed/ad/483d9e262f4b831000062e5d8a45e342166ec8aaa1195264982bca267e62/numpy-2.4.4-cp314-cp314t-win32.whl", hash = "sha256:dddbbd259598d7240b18c9d87c56a9d2fb3b02fe266f49a7c101532e78c1d871", size = 6155500, upload-time = "2026-03-29T13:21:28.205Z" }, + { url = "https://files.pythonhosted.org/packages/c7/03/2fc4e14c7bd4ff2964b74ba90ecb8552540b6315f201df70f137faa5c589/numpy-2.4.4-cp314-cp314t-win_amd64.whl", hash = "sha256:a7164afb23be6e37ad90b2f10426149fd75aee07ca55653d2aa41e66c4ef697e", size = 12637755, upload-time = "2026-03-29T13:21:31.107Z" }, + { url = "https://files.pythonhosted.org/packages/58/78/548fb8e07b1a341746bfbecb32f2c268470f45fa028aacdbd10d9bc73aab/numpy-2.4.4-cp314-cp314t-win_arm64.whl", hash = "sha256:ba203255017337d39f89bdd58417f03c4426f12beed0440cfd933cb15f8669c7", size = 10566643, upload-time = "2026-03-29T13:21:34.339Z" }, +] + +[[package]] +name = "nvidia-cublas" +version = "13.1.0.3" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e1/a5/fce49e2ae977e0ccc084e5adafceb4f0ac0c8333cb6863501618a7277f67/nvidia_cublas-13.1.0.3-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:c86fc7f7ae36d7528288c5d88098edcb7b02c633d262e7ddbb86b0ad91be5df2", size = 542851226, upload-time = "2025-10-09T08:59:04.818Z" }, + { url = "https://files.pythonhosted.org/packages/e7/44/423ac00af4dd95a5aeb27207e2c0d9b7118702149bf4704c3ddb55bb7429/nvidia_cublas-13.1.0.3-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:ee8722c1f0145ab246bccb9e452153b5e0515fd094c3678df50b2a0888b8b171", size = 423133236, upload-time = "2025-10-09T08:59:32.536Z" }, +] + +[[package]] +name = "nvidia-cuda-cupti" +version = "13.0.85" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/2a/2a/80353b103fc20ce05ef51e928daed4b6015db4aaa9162ed0997090fe2250/nvidia_cuda_cupti-13.0.85-py3-none-manylinux_2_25_aarch64.whl", hash = "sha256:796bd679890ee55fb14a94629b698b6db54bcfd833d391d5e94017dd9d7d3151", size = 10310827, upload-time = "2025-09-04T08:26:42.012Z" }, + { url = "https://files.pythonhosted.org/packages/33/6d/737d164b4837a9bbd202f5ae3078975f0525a55730fe871d8ed4e3b952b0/nvidia_cuda_cupti-13.0.85-py3-none-manylinux_2_25_x86_64.whl", hash = "sha256:4eb01c08e859bf924d222250d2e8f8b8ff6d3db4721288cf35d14252a4d933c8", size = 10715597, upload-time = "2025-09-04T08:26:51.312Z" }, +] + +[[package]] +name = "nvidia-cuda-nvrtc" +version = "13.0.88" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/c3/68/483a78f5e8f31b08fb1bb671559968c0ca3a065ac7acabfc7cee55214fd6/nvidia_cuda_nvrtc-13.0.88-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:ad9b6d2ead2435f11cbb6868809d2adeeee302e9bb94bcf0539c7a40d80e8575", size = 90215200, upload-time = "2025-09-04T08:28:44.204Z" }, + { url = "https://files.pythonhosted.org/packages/b7/dc/6bb80850e0b7edd6588d560758f17e0550893a1feaf436807d64d2da040f/nvidia_cuda_nvrtc-13.0.88-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d27f20a0ca67a4bb34268a5e951033496c5b74870b868bacd046b1b8e0c3267b", size = 43015449, upload-time = "2025-09-04T08:28:20.239Z" }, +] + +[[package]] +name = "nvidia-cuda-runtime" +version = "13.0.96" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/87/4f/17d7b9b8e285199c58ce28e31b5c5bbaa4d8271af06a89b6405258245de2/nvidia_cuda_runtime-13.0.96-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:ef9bcbe90493a2b9d810e43d249adb3d02e98dd30200d86607d8d02687c43f55", size = 2261060, upload-time = "2025-10-09T08:55:15.78Z" }, + { url = "https://files.pythonhosted.org/packages/2e/24/d1558f3b68b1d26e706813b1d10aa1d785e4698c425af8db8edc3dced472/nvidia_cuda_runtime-13.0.96-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:7f82250d7782aa23b6cfe765ecc7db554bd3c2870c43f3d1821f1d18aebf0548", size = 2243632, upload-time = "2025-10-09T08:55:36.117Z" }, +] + +[[package]] +name = "nvidia-cudnn-cu13" +version = "9.19.0.56" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "nvidia-cublas", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" }, +] +wheels = [ + { url = "https://files.pythonhosted.org/packages/f1/84/26025437c1e6b61a707442184fa0c03d083b661adf3a3eecfd6d21677740/nvidia_cudnn_cu13-9.19.0.56-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:6ed29ffaee1176c612daf442e4dd6cfeb6a0caa43ddcbeb59da94953030b1be4", size = 433781201, upload-time = "2026-02-03T20:40:53.805Z" }, + { url = "https://files.pythonhosted.org/packages/a3/22/0b4b932655d17a6da1b92fa92ab12844b053bb2ac2475e179ba6f043da1e/nvidia_cudnn_cu13-9.19.0.56-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:d20e1734305e9d68889a96e3f35094d733ff1f83932ebe462753973e53a572bf", size = 366066321, upload-time = "2026-02-03T20:44:52.837Z" }, +] + +[[package]] +name = "nvidia-cufft" +version = "12.0.0.61" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "nvidia-nvjitlink", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" }, +] +wheels = [ + { url = "https://files.pythonhosted.org/packages/8b/ae/f417a75c0259e85c1d2f83ca4e960289a5f814ed0cea74d18c353d3e989d/nvidia_cufft-12.0.0.61-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:2708c852ef8cd89d1d2068bdbece0aa188813a0c934db3779b9b1faa8442e5f5", size = 214053554, upload-time = "2025-09-04T08:31:38.196Z" }, + { url = "https://files.pythonhosted.org/packages/a8/2f/7b57e29836ea8714f81e9898409196f47d772d5ddedddf1592eadb8ab743/nvidia_cufft-12.0.0.61-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:6c44f692dce8fd5ffd3e3df134b6cdb9c2f72d99cf40b62c32dde45eea9ddad3", size = 214085489, upload-time = "2025-09-04T08:31:56.044Z" }, +] + +[[package]] +name = "nvidia-cufile" +version = "1.15.1.6" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/3f/70/4f193de89a48b71714e74602ee14d04e4019ad36a5a9f20c425776e72cd6/nvidia_cufile-1.15.1.6-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:08a3ecefae5a01c7f5117351c64f17c7c62efa5fffdbe24fc7d298da19cd0b44", size = 1223672, upload-time = "2025-09-04T08:32:22.779Z" }, + { url = "https://files.pythonhosted.org/packages/ab/73/cc4a14c9813a8a0d509417cf5f4bdaba76e924d58beb9864f5a7baceefbf/nvidia_cufile-1.15.1.6-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:bdc0deedc61f548bddf7733bdc216456c2fdb101d020e1ab4b88d232d5e2f6d1", size = 1136992, upload-time = "2025-09-04T08:32:14.119Z" }, +] + +[[package]] +name = "nvidia-curand" +version = "10.4.0.35" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/1e/72/7c2ae24fb6b63a32e6ae5d241cc65263ea18d08802aaae087d9f013335a2/nvidia_curand-10.4.0.35-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:133df5a7509c3e292aaa2b477afd0194f06ce4ea24d714d616ff36439cee349a", size = 61962106, upload-time = "2025-08-04T10:21:41.128Z" }, + { url = "https://files.pythonhosted.org/packages/a5/9f/be0a41ca4a4917abf5cb9ae0daff1a6060cc5de950aec0396de9f3b52bc5/nvidia_curand-10.4.0.35-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:1aee33a5da6e1db083fe2b90082def8915f30f3248d5896bcec36a579d941bfc", size = 59544258, upload-time = "2025-08-04T10:22:03.992Z" }, +] + +[[package]] +name = "nvidia-cusolver" +version = "12.0.4.66" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "nvidia-cublas", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" }, + { name = "nvidia-cusparse", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" }, + { name = "nvidia-nvjitlink", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" }, +] +wheels = [ + { url = "https://files.pythonhosted.org/packages/c8/c3/b30c9e935fc01e3da443ec0116ed1b2a009bb867f5324d3f2d7e533e776b/nvidia_cusolver-12.0.4.66-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:02c2457eaa9e39de20f880f4bd8820e6a1cfb9f9a34f820eb12a155aa5bc92d2", size = 223467760, upload-time = "2025-09-04T08:33:04.222Z" }, + { url = "https://files.pythonhosted.org/packages/5f/67/cba3777620cdacb99102da4042883709c41c709f4b6323c10781a9c3aa34/nvidia_cusolver-12.0.4.66-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:0a759da5dea5c0ea10fd307de75cdeb59e7ea4fcb8add0924859b944babf1112", size = 200941980, upload-time = "2025-09-04T08:33:22.767Z" }, +] + +[[package]] +name = "nvidia-cusparse" +version = "12.6.3.3" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "nvidia-nvjitlink", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" }, +] +wheels = [ + { url = "https://files.pythonhosted.org/packages/f8/94/5c26f33738ae35276672f12615a64bd008ed5be6d1ebcb23579285d960a9/nvidia_cusparse-12.6.3.3-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:80bcc4662f23f1054ee334a15c72b8940402975e0eab63178fc7e670aa59472c", size = 162155568, upload-time = "2025-09-04T08:33:42.864Z" }, + { url = "https://files.pythonhosted.org/packages/fa/18/623c77619c31d62efd55302939756966f3ecc8d724a14dab2b75f1508850/nvidia_cusparse-12.6.3.3-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:2b3c89c88d01ee0e477cb7f82ef60a11a4bcd57b6b87c33f789350b59759360b", size = 145942937, upload-time = "2025-09-04T08:33:58.029Z" }, +] + +[[package]] +name = "nvidia-cusparselt-cu13" +version = "0.8.0" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/46/10/8dcd1175260706a2fc92a16a52e306b71d4c1ea0b0cc4a9484183399818a/nvidia_cusparselt_cu13-0.8.0-py3-none-manylinux2014_aarch64.whl", hash = "sha256:400c6ed1cf6780fc6efedd64ec9f1345871767e6a1a0a552a1ea0578117ea77c", size = 220791277, upload-time = "2025-08-13T19:22:40.982Z" }, + { url = "https://files.pythonhosted.org/packages/fd/53/43b0d71f4e702fa9733f8b4571fdca50a8813f1e450b656c239beff12315/nvidia_cusparselt_cu13-0.8.0-py3-none-manylinux2014_x86_64.whl", hash = "sha256:25e30a8a7323935d4ad0340b95a0b69926eee755767e8e0b1cf8dd85b197d3fd", size = 169884119, upload-time = "2025-08-13T19:23:41.967Z" }, +] + +[[package]] +name = "nvidia-nccl-cu13" +version = "2.28.9" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/39/55/1920646a2e43ffd4fc958536b276197ed740e9e0c54105b4bb3521591fc7/nvidia_nccl_cu13-2.28.9-py3-none-manylinux_2_18_aarch64.whl", hash = "sha256:01c873ba1626b54caa12272ed228dc5b2781545e0ae8ba3f432a8ef1c6d78643", size = 196561677, upload-time = "2025-11-18T05:49:03.45Z" }, + { url = "https://files.pythonhosted.org/packages/b0/b4/878fefaad5b2bcc6fcf8d474a25e3e3774bc5133e4b58adff4d0bca238bc/nvidia_nccl_cu13-2.28.9-py3-none-manylinux_2_18_x86_64.whl", hash = "sha256:e4553a30f34195f3fa1da02a6da3d6337d28f2003943aa0a3d247bbc25fefc42", size = 196493177, upload-time = "2025-11-18T05:49:17.677Z" }, +] + +[[package]] +name = "nvidia-nvjitlink" +version = "13.0.88" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/56/7a/123e033aaff487c77107195fa5a2b8686795ca537935a24efae476c41f05/nvidia_nvjitlink-13.0.88-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:13a74f429e23b921c1109976abefacc69835f2f433ebd323d3946e11d804e47b", size = 40713933, upload-time = "2025-09-04T08:35:43.553Z" }, + { url = "https://files.pythonhosted.org/packages/ab/2c/93c5250e64df4f894f1cbb397c6fd71f79813f9fd79d7cd61de3f97b3c2d/nvidia_nvjitlink-13.0.88-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:e931536ccc7d467a98ba1d8b89ff7fa7f1fa3b13f2b0069118cd7f47bff07d0c", size = 38768748, upload-time = "2025-09-04T08:35:20.008Z" }, +] + +[[package]] +name = "nvidia-nvshmem-cu13" +version = "3.4.5" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/dc/0f/05cc9c720236dcd2db9c1ab97fff629e96821be2e63103569da0c9b72f19/nvidia_nvshmem_cu13-3.4.5-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:6dc2a197f38e5d0376ad52cd1a2a3617d3cdc150fd5966f4aee9bcebb1d68fe9", size = 60215947, upload-time = "2025-09-06T00:32:20.022Z" }, + { url = "https://files.pythonhosted.org/packages/3c/35/a9bf80a609e74e3b000fef598933235c908fcefcef9026042b8e6dfde2a9/nvidia_nvshmem_cu13-3.4.5-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:290f0a2ee94c9f3687a02502f3b9299a9f9fe826e6d0287ee18482e78d495b80", size = 60412546, upload-time = "2025-09-06T00:32:41.564Z" }, +] + +[[package]] +name = "nvidia-nvtx" +version = "13.0.85" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/c2/f3/d86c845465a2723ad7e1e5c36dcd75ddb82898b3f53be47ebd429fb2fa5d/nvidia_nvtx-13.0.85-py3-none-manylinux1_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:4936d1d6780fbe68db454f5e72a42ff64d1fd6397df9f363ae786930fd5c1cd4", size = 148047, upload-time = "2025-09-04T08:29:01.761Z" }, + { url = "https://files.pythonhosted.org/packages/a8/64/3708a90d1ebe202ffdeb7185f878a3c84d15c2b2c31858da2ce0583e2def/nvidia_nvtx-13.0.85-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:cb7780edb6b14107373c835bf8b72e7a178bac7367e23da7acb108f973f157a6", size = 148878, upload-time = "2025-09-04T08:28:53.627Z" }, +] + +[[package]] +name = "onnxruntime" +version = "1.25.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "flatbuffers" }, + { name = "numpy" }, + { name = "packaging" }, + { name = "protobuf" }, +] +wheels = [ + { url = "https://files.pythonhosted.org/packages/d2/ee/db3ac55ef770347a926ac0f1317df0ab42c8bc604350833b30c7356bf936/onnxruntime-1.25.1-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:e9d9b3b1694196bc3c5bc66f760a237a5e27d7688aaa2e2c9c0f66abd0486699", size = 17770761, upload-time = "2026-04-27T21:59:54.853Z" }, + { url = "https://files.pythonhosted.org/packages/dc/9a/33225481a94a59906fce44e27ab12fc3bddd2aaecdc6160bd73341ca1aba/onnxruntime-1.25.1-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:311d29b943e46a55ca72ca1ea48d7815c993122bfc359f68215fddeb9583fff4", size = 15871542, upload-time = "2026-04-27T21:59:41.881Z" }, + { url = "https://files.pythonhosted.org/packages/8b/09/f20aac60f6fcf840543be54d4e9252cfeb7e8c2bb6d22477aaeb180e763e/onnxruntime-1.25.1-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:98016a038b31160db23208706139fa3b99cd60bc1c5ffdade77aafd6a37a92ad", size = 18036960, upload-time = "2026-04-27T22:00:10.739Z" }, + { url = "https://files.pythonhosted.org/packages/50/83/47964ac7e2f7e2f9e83c69ec466642c6835466252cc2ef0561eafeb56b66/onnxruntime-1.25.1-cp313-cp313-win_amd64.whl", hash = "sha256:08717d6eee2820807ba60b1b17032af207bd7aaca5b6c4abaee71f83feae877b", size = 12904886, upload-time = "2026-04-27T22:00:39.878Z" }, + { url = "https://files.pythonhosted.org/packages/d4/6c/a6c5aea47dc95fca7728f8a5af67c184ec9e7d4e7882125c7062e4bba8dd/onnxruntime-1.25.1-cp313-cp313-win_arm64.whl", hash = "sha256:84f8963d70e00167bae273ab7e80e9795bfc5eb94f6b23236a99c5c11af00844", size = 12634117, upload-time = "2026-04-27T22:00:29.15Z" }, + { url = "https://files.pythonhosted.org/packages/a8/8a/3b65e7911eec86c125e3d6f43d690a6f68671500543c0390ecd6eb59b771/onnxruntime-1.25.1-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:03e800b3a4b48d9f3a2d23aacc4fa95486a3b406b14e51d1a9b8b6981d9adf9c", size = 15882935, upload-time = "2026-04-27T21:59:44.912Z" }, + { url = "https://files.pythonhosted.org/packages/3c/bb/410a760694f8ae7bbfc5fa81ccbeb7da241e6d520ee02a333a439cf462a2/onnxruntime-1.25.1-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fd83ef5c10cfc051a1cb465db692d57b996a1bc75a2a97b161398e29cdbc47ff", size = 18021727, upload-time = "2026-04-27T22:00:13.846Z" }, + { url = "https://files.pythonhosted.org/packages/fb/aa/04530bd38e31e26970fa1212346d76cf81705dc16a8ee5e6f4fb24634c11/onnxruntime-1.25.1-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:395eb662c437fa2407f44266e4778b75bff261b17c2a6fef042421f9069f871d", size = 17773721, upload-time = "2026-04-27T21:59:59.24Z" }, + { url = "https://files.pythonhosted.org/packages/ef/7f/ec79ab5cece6a688c944a7fa214a8511d548b9d5142a15d1a3d730b705f1/onnxruntime-1.25.1-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9ae85395f41b291ae3e61780ec5092640181d369ef6c268aa8141c478b509e69", size = 15875954, upload-time = "2026-04-27T21:59:49.394Z" }, + { url = "https://files.pythonhosted.org/packages/67/fe/20428215d822099ea2c1e3cf35c295cf1a58f467bf18b6c607597a39c18a/onnxruntime-1.25.1-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:828e1b12710fbedb6dfab5e7bae6f11563617cddf3c2e7e8d84c64de566a4a3a", size = 18038703, upload-time = "2026-04-27T22:00:16.199Z" }, + { url = "https://files.pythonhosted.org/packages/5a/b1/b15db965e6a68bc47ca7eb584de4e6b3d2d2f484d46cc57f715b596f6528/onnxruntime-1.25.1-cp314-cp314-win_amd64.whl", hash = "sha256:2affc9d2fd9ab013b9c9637464e649a0cca870d57ae18bfef74180eee65c3369", size = 13218513, upload-time = "2026-04-27T22:00:42.506Z" }, + { url = "https://files.pythonhosted.org/packages/5a/f9/25cd2d1b29cdc8140eee4afbb6fb930b69125526632b1d579bc747975306/onnxruntime-1.25.1-cp314-cp314-win_arm64.whl", hash = "sha256:3387d75d1a815b4b2495b4e47a05ef1b3bcb64a817ddc68587e0bfcb9702bcf6", size = 12969835, upload-time = "2026-04-27T22:00:31.504Z" }, + { url = "https://files.pythonhosted.org/packages/8d/0e/6c507d1e65b2421fb44e241cbba577c7276792279485024fb1752b43f5c5/onnxruntime-1.25.1-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:06280b06604660595037f783c6d24bc70cbe5c6093975f194cd1482e77d450de", size = 15883298, upload-time = "2026-04-27T21:59:51.991Z" }, + { url = "https://files.pythonhosted.org/packages/df/4e/1c9df57496409dc86b320bd38f29ad7a34b7115e4f35b8fca44a827568a7/onnxruntime-1.25.1-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7e79fd5ce7db10ebcc24e020e2ed0159476e69e2326b9b7828e5aadcf6184212", size = 18021249, upload-time = "2026-04-27T22:00:18.954Z" }, +] + +[[package]] +name = "opentelemetry-api" +version = "1.41.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "importlib-metadata" }, + { name = "typing-extensions" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/fa/fc/b7564cbef36601aef0d6c9bc01f7badb64be8e862c2e1c3c5c3b43b53e4f/opentelemetry_api-1.41.1.tar.gz", hash = "sha256:0ad1814d73b875f84494387dae86ce0b12c68556331ce6ce8fe789197c949621", size = 71416, upload-time = "2026-04-24T13:15:38.262Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/29/59/3e7118ed140f76b0982ba4321bdaed1997a0473f9720de2d10788a577033/opentelemetry_api-1.41.1-py3-none-any.whl", hash = "sha256:a22df900e75c76dc08440710e51f52f1aa6b451b429298896023e60db5b3139f", size = 69007, upload-time = "2026-04-24T13:15:15.662Z" }, +] + +[[package]] +name = "opentelemetry-sdk" +version = "1.41.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "opentelemetry-api" }, + { name = "opentelemetry-semantic-conventions" }, + { name = "typing-extensions" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/58/d0/54ee30dab82fb0acda23d144502771ff76ef8728459c83c3e89ef9fb1825/opentelemetry_sdk-1.41.1.tar.gz", hash = "sha256:724b615e1215b5aeacda0abb8a6a8922c9a1853068948bd0bd225a56d0c792e6", size = 230180, upload-time = "2026-04-24T13:15:50.991Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/b4/e7/a1420b698aad018e1cf60fdbaaccbe49021fb415e2a0d81c242f4c518f54/opentelemetry_sdk-1.41.1-py3-none-any.whl", hash = "sha256:edee379c126c1bce952b0c812b48fe8ff35b30df0eecf17e98afa4d598b7d85d", size = 180213, upload-time = "2026-04-24T13:15:33.767Z" }, +] + +[[package]] +name = "opentelemetry-semantic-conventions" +version = "0.62b1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "opentelemetry-api" }, + { name = "typing-extensions" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/9e/de/911ac9e309052aca1b20b2d5549d3db45d1011e1a610e552c6ccdd1b64f8/opentelemetry_semantic_conventions-0.62b1.tar.gz", hash = "sha256:c5cc6e04a7f8c7cdd30be2ed81499fa4e75bfbd52c9cb70d40af1f9cd3619802", size = 145750, upload-time = "2026-04-24T13:15:52.236Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/eb/a6/83dc2ab6fa397ee66fba04fe2e74bdf7be3b3870005359ceb7689103c058/opentelemetry_semantic_conventions-0.62b1-py3-none-any.whl", hash = "sha256:cf506938103d331fbb78eded0d9788095f7fd59016f2bda813c3324e5a74a93c", size = 231620, upload-time = "2026-04-24T13:15:35.454Z" }, +] + +[[package]] +name = "packaging" +version = "26.2" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/d7/f1/e7a6dd94a8d4a5626c03e4e99c87f241ba9e350cd9e6d75123f992427270/packaging-26.2.tar.gz", hash = "sha256:ff452ff5a3e828ce110190feff1178bb1f2ea2281fa2075aadb987c2fb221661", size = 228134, upload-time = "2026-04-24T20:15:23.917Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/df/b2/87e62e8c3e2f4b32e5fe99e0b86d576da1312593b39f47d8ceef365e95ed/packaging-26.2-py3-none-any.whl", hash = "sha256:5fc45236b9446107ff2415ce77c807cee2862cb6fac22b8a73826d0693b0980e", size = 100195, upload-time = "2026-04-24T20:15:22.081Z" }, +] + +[[package]] +name = "pandas" +version = "3.0.2" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "numpy" }, + { name = "python-dateutil" }, + { name = "tzdata", marker = "sys_platform == 'emscripten' or sys_platform == 'win32'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/da/99/b342345300f13440fe9fe385c3c481e2d9a595ee3bab4d3219247ac94e9a/pandas-3.0.2.tar.gz", hash = "sha256:f4753e73e34c8d83221ba58f232433fca2748be8b18dbca02d242ed153945043", size = 4645855, upload-time = "2026-03-31T06:48:30.816Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/bf/ca/3e639a1ea6fcd0617ca4e8ca45f62a74de33a56ae6cd552735470b22c8d3/pandas-3.0.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:b5918ba197c951dec132b0c5929a00c0bf05d5942f590d3c10a807f6e15a57d3", size = 10321105, upload-time = "2026-03-31T06:46:57.327Z" }, + { url = "https://files.pythonhosted.org/packages/0b/77/dbc82ff2fb0e63c6564356682bf201edff0ba16c98630d21a1fb312a8182/pandas-3.0.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:d606a041c89c0a474a4702d532ab7e73a14fe35c8d427b972a625c8e46373668", size = 9864088, upload-time = "2026-03-31T06:46:59.935Z" }, + { url = "https://files.pythonhosted.org/packages/5c/2b/341f1b04bbca2e17e13cd3f08c215b70ef2c60c5356ef1e8c6857449edc7/pandas-3.0.2-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:710246ba0616e86891b58ab95f2495143bb2bc83ab6b06747c74216f583a6ac9", size = 10369066, upload-time = "2026-03-31T06:47:02.792Z" }, + { url = "https://files.pythonhosted.org/packages/12/c5/cbb1ffefb20a93d3f0e1fdcda699fb84976210d411b008f97f48bf6ce27e/pandas-3.0.2-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5d3cfe227c725b1f3dff4278b43d8c784656a42a9325b63af6b1492a8232209e", size = 10876780, upload-time = "2026-03-31T06:47:06.205Z" }, + { url = "https://files.pythonhosted.org/packages/98/fe/2249ae5e0a69bd0ddf17353d0a5d26611d70970111f5b3600cdc8be883e7/pandas-3.0.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:c3b723df9087a9a9a840e263ebd9f88b64a12075d1bf2ea401a5a42f254f084d", size = 11375181, upload-time = "2026-03-31T06:47:09.383Z" }, + { url = "https://files.pythonhosted.org/packages/de/64/77a38b09e70b6464883b8d7584ab543e748e42c1b5d337a2ee088e0df741/pandas-3.0.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:a3096110bf9eac0070b7208465f2740e2d8a670d5cb6530b5bb884eca495fd39", size = 11928899, upload-time = "2026-03-31T06:47:12.686Z" }, + { url = "https://files.pythonhosted.org/packages/5e/52/42855bf626868413f761addd574acc6195880ae247a5346477a4361c3acb/pandas-3.0.2-cp313-cp313-win_amd64.whl", hash = "sha256:07a10f5c36512eead51bc578eb3354ad17578b22c013d89a796ab5eee90cd991", size = 9746574, upload-time = "2026-03-31T06:47:15.64Z" }, + { url = "https://files.pythonhosted.org/packages/88/39/21304ae06a25e8bf9fc820d69b29b2c495b2ae580d1e143146c309941760/pandas-3.0.2-cp313-cp313-win_arm64.whl", hash = "sha256:5fdbfa05931071aba28b408e59226186b01eb5e92bea2ab78b65863ca3228d84", size = 9047156, upload-time = "2026-03-31T06:47:18.595Z" }, + { url = "https://files.pythonhosted.org/packages/72/20/7defa8b27d4f330a903bb68eea33be07d839c5ea6bdda54174efcec0e1d2/pandas-3.0.2-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:dbc20dea3b9e27d0e66d74c42b2d0c1bed9c2ffe92adea33633e3bedeb5ac235", size = 10756238, upload-time = "2026-03-31T06:47:22.012Z" }, + { url = "https://files.pythonhosted.org/packages/e9/95/49433c14862c636afc0e9b2db83ff16b3ad92959364e52b2955e44c8e94c/pandas-3.0.2-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:b75c347eff42497452116ce05ef461822d97ce5b9ff8df6edacb8076092c855d", size = 10408520, upload-time = "2026-03-31T06:47:25.197Z" }, + { url = "https://files.pythonhosted.org/packages/3b/f8/462ad2b5881d6b8ec8e5f7ed2ea1893faa02290d13870a1600fe72ad8efc/pandas-3.0.2-cp313-cp313t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d1478075142e83a5571782ad007fb201ed074bdeac7ebcc8890c71442e96adf7", size = 10324154, upload-time = "2026-03-31T06:47:28.097Z" }, + { url = "https://files.pythonhosted.org/packages/0a/65/d1e69b649cbcddda23ad6e4c40ef935340f6f652a006e5cbc3555ac8adb3/pandas-3.0.2-cp313-cp313t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5880314e69e763d4c8b27937090de570f1fb8d027059a7ada3f7f8e98bdcb677", size = 10714449, upload-time = "2026-03-31T06:47:30.85Z" }, + { url = "https://files.pythonhosted.org/packages/47/a4/85b59bc65b8190ea3689882db6cdf32a5003c0ccd5a586c30fdcc3ffc4fc/pandas-3.0.2-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:b5329e26898896f06035241a626d7c335daa479b9bbc82be7c2742d048e41172", size = 11338475, upload-time = "2026-03-31T06:47:34.026Z" }, + { url = "https://files.pythonhosted.org/packages/1e/c4/bc6966c6e38e5d9478b935272d124d80a589511ed1612a5d21d36f664c68/pandas-3.0.2-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:81526c4afd31971f8b62671442a4b2b51e0aa9acc3819c9f0f12a28b6fcf85f1", size = 11786568, upload-time = "2026-03-31T06:47:36.941Z" }, + { url = "https://files.pythonhosted.org/packages/e8/74/09298ca9740beed1d3504e073d67e128aa07e5ca5ca2824b0c674c0b8676/pandas-3.0.2-cp313-cp313t-win_amd64.whl", hash = "sha256:7cadd7e9a44ec13b621aec60f9150e744cfc7a3dd32924a7e2f45edff31823b0", size = 10488652, upload-time = "2026-03-31T06:47:40.612Z" }, + { url = "https://files.pythonhosted.org/packages/bb/40/c6ea527147c73b24fc15c891c3fcffe9c019793119c5742b8784a062c7db/pandas-3.0.2-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:db0dbfd2a6cdf3770aa60464d50333d8f3d9165b2f2671bcc299b72de5a6677b", size = 10326084, upload-time = "2026-03-31T06:47:43.834Z" }, + { url = "https://files.pythonhosted.org/packages/95/25/bdb9326c3b5455f8d4d3549fce7abcf967259de146fe2cf7a82368141948/pandas-3.0.2-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:0555c5882688a39317179ab4a0ed41d3ebc8812ab14c69364bbee8fb7a3f6288", size = 9914146, upload-time = "2026-03-31T06:47:46.67Z" }, + { url = "https://files.pythonhosted.org/packages/8d/77/3a227ff3337aa376c60d288e1d61c5d097131d0ac71f954d90a8f369e422/pandas-3.0.2-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:01f31a546acd5574ef77fe199bc90b55527c225c20ccda6601cf6b0fd5ed597c", size = 10444081, upload-time = "2026-03-31T06:47:49.681Z" }, + { url = "https://files.pythonhosted.org/packages/15/88/3cdd54fa279341afa10acf8d2b503556b1375245dccc9315659f795dd2e9/pandas-3.0.2-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:deeca1b5a931fdf0c2212c8a659ade6d3b1edc21f0914ce71ef24456ca7a6535", size = 10897535, upload-time = "2026-03-31T06:47:53.033Z" }, + { url = "https://files.pythonhosted.org/packages/06/9d/98cc7a7624f7932e40f434299260e2917b090a579d75937cb8a57b9d2de3/pandas-3.0.2-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:0f48afd9bb13300ffb5a3316973324c787054ba6665cda0da3fbd67f451995db", size = 11446992, upload-time = "2026-03-31T06:47:56.193Z" }, + { url = "https://files.pythonhosted.org/packages/9a/cd/19ff605cc3760e80602e6826ddef2824d8e7050ed80f2e11c4b079741dc3/pandas-3.0.2-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:6c4d8458b97a35717b62469a4ea0e85abd5ed8687277f5ccfc67f8a5126f8c53", size = 11968257, upload-time = "2026-03-31T06:47:59.137Z" }, + { url = "https://files.pythonhosted.org/packages/db/60/aba6a38de456e7341285102bede27514795c1eaa353bc0e7638b6b785356/pandas-3.0.2-cp314-cp314-win_amd64.whl", hash = "sha256:b35d14bb5d8285d9494fe93815a9e9307c0876e10f1e8e89ac5b88f728ec8dcf", size = 9865893, upload-time = "2026-03-31T06:48:02.038Z" }, + { url = "https://files.pythonhosted.org/packages/08/71/e5ec979dd2e8a093dacb8864598c0ff59a0cee0bbcdc0bfec16a51684d4f/pandas-3.0.2-cp314-cp314-win_arm64.whl", hash = "sha256:63d141b56ef686f7f0d714cfb8de4e320475b86bf4b620aa0b7da89af8cbdbbb", size = 9188644, upload-time = "2026-03-31T06:48:05.045Z" }, + { url = "https://files.pythonhosted.org/packages/f1/6c/7b45d85db19cae1eb524f2418ceaa9d85965dcf7b764ed151386b7c540f0/pandas-3.0.2-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:140f0cffb1fa2524e874dde5b477d9defe10780d8e9e220d259b2c0874c89d9d", size = 10776246, upload-time = "2026-03-31T06:48:07.789Z" }, + { url = "https://files.pythonhosted.org/packages/a8/3e/7b00648b086c106e81766f25322b48aa8dfa95b55e621dbdf2fdd413a117/pandas-3.0.2-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:ae37e833ff4fed0ba352f6bdd8b73ba3ab3256a85e54edfd1ab51ae40cca0af8", size = 10424801, upload-time = "2026-03-31T06:48:10.897Z" }, + { url = "https://files.pythonhosted.org/packages/da/6e/558dd09a71b53b4008e7fc8a98ec6d447e9bfb63cdaeea10e5eb9b2dabe8/pandas-3.0.2-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4d888a5c678a419a5bb41a2a93818e8ed9fd3172246555c0b37b7cc27027effd", size = 10345643, upload-time = "2026-03-31T06:48:13.7Z" }, + { url = "https://files.pythonhosted.org/packages/be/e3/921c93b4d9a280409451dc8d07b062b503bbec0531d2627e73a756e99a82/pandas-3.0.2-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b444dc64c079e84df91baa8bf613d58405645461cabca929d9178f2cd392398d", size = 10743641, upload-time = "2026-03-31T06:48:16.659Z" }, + { url = "https://files.pythonhosted.org/packages/56/ca/fd17286f24fa3b4d067965d8d5d7e14fe557dd4f979a0b068ac0deaf8228/pandas-3.0.2-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:4544c7a54920de8eeacaa1466a6b7268ecfbc9bc64ab4dbb89c6bbe94d5e0660", size = 11361993, upload-time = "2026-03-31T06:48:19.475Z" }, + { url = "https://files.pythonhosted.org/packages/e4/a5/2f6ed612056819de445a433ca1f2821ac3dab7f150d569a59e9cc105de1d/pandas-3.0.2-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:734be7551687c00fbd760dc0522ed974f82ad230d4a10f54bf51b80d44a08702", size = 11815274, upload-time = "2026-03-31T06:48:22.695Z" }, + { url = "https://files.pythonhosted.org/packages/00/2f/b622683e99ec3ce00b0854bac9e80868592c5b051733f2cf3a868e5fea26/pandas-3.0.2-cp314-cp314t-win_amd64.whl", hash = "sha256:57a07209bebcbcf768d2d13c9b78b852f9a15978dac41b9e6421a81ad4cdd276", size = 10888530, upload-time = "2026-03-31T06:48:25.806Z" }, + { url = "https://files.pythonhosted.org/packages/cb/2b/f8434233fab2bd66a02ec014febe4e5adced20e2693e0e90a07d118ed30e/pandas-3.0.2-cp314-cp314t-win_arm64.whl", hash = "sha256:5371b72c2d4d415d08765f32d689217a43227484e81b2305b52076e328f6f482", size = 9455341, upload-time = "2026-03-31T06:48:28.418Z" }, +] + +[[package]] +name = "pandocfilters" +version = "1.5.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/70/6f/3dd4940bbe001c06a65f88e36bad298bc7a0de5036115639926b0c5c0458/pandocfilters-1.5.1.tar.gz", hash = "sha256:002b4a555ee4ebc03f8b66307e287fa492e4a77b4ea14d3f934328297bb4939e", size = 8454, upload-time = "2024-01-18T20:08:13.726Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ef/af/4fbc8cab944db5d21b7e2a5b8e9211a03a79852b1157e2c102fcc61ac440/pandocfilters-1.5.1-py2.py3-none-any.whl", hash = "sha256:93be382804a9cdb0a7267585f157e5d1731bbe5545a85b268d6f5fe6232de2bc", size = 8663, upload-time = "2024-01-18T20:08:11.28Z" }, +] + +[[package]] +name = "parso" +version = "0.8.7" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/30/4b/90c937815137d43ce71ba043cd3566221e9df6b9c805f24b5d138c9d40a7/parso-0.8.7.tar.gz", hash = "sha256:eaaac4c9fdd5e9e8852dc778d2d7405897ec510f2a298071453e5e3a07914bb1", size = 401824, upload-time = "2026-05-01T23:13:02.138Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/99/5d/8268b644392ee874ee82a635cd0df1773de230bde356c38de28e298392cc/parso-0.8.7-py2.py3-none-any.whl", hash = "sha256:a8926eb2a1b915486941fdbd31e86a4baf88fe8c210f25f2f35ecec5b574ca1c", size = 107025, upload-time = "2026-05-01T23:12:58.867Z" }, +] + +[[package]] +name = "pexpect" +version = "4.9.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "ptyprocess", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/42/92/cc564bf6381ff43ce1f4d06852fc19a2f11d180f23dc32d9588bee2f149d/pexpect-4.9.0.tar.gz", hash = "sha256:ee7d41123f3c9911050ea2c2dac107568dc43b2d3b0c7557a33212c398ead30f", size = 166450, upload-time = "2023-11-25T09:07:26.339Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/9e/c3/059298687310d527a58bb01f3b1965787ee3b40dce76752eda8b44e9a2c5/pexpect-4.9.0-py2.py3-none-any.whl", hash = "sha256:7236d1e080e4936be2dc3e326cec0af72acf9212a7e1d060210e70a47e253523", size = 63772, upload-time = "2023-11-25T06:56:14.81Z" }, +] + +[[package]] +name = "pillow" +version = "12.2.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/8c/21/c2bcdd5906101a30244eaffc1b6e6ce71a31bd0742a01eb89e660ebfac2d/pillow-12.2.0.tar.gz", hash = "sha256:a830b1a40919539d07806aa58e1b114df53ddd43213d9c8b75847eee6c0182b5", size = 46987819, upload-time = "2026-04-01T14:46:17.687Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/4a/01/53d10cf0dbad820a8db274d259a37ba50b88b24768ddccec07355382d5ad/pillow-12.2.0-cp313-cp313-ios_13_0_arm64_iphoneos.whl", hash = "sha256:8297651f5b5679c19968abefd6bb84d95fe30ef712eb1b2d9b2d31ca61267f4c", size = 4100837, upload-time = "2026-04-01T14:43:41.506Z" }, + { url = "https://files.pythonhosted.org/packages/0f/98/f3a6657ecb698c937f6c76ee564882945f29b79bad496abcba0e84659ec5/pillow-12.2.0-cp313-cp313-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:50d8520da2a6ce0af445fa6d648c4273c3eeefbc32d7ce049f22e8b5c3daecc2", size = 4176528, upload-time = "2026-04-01T14:43:43.773Z" }, + { url = "https://files.pythonhosted.org/packages/69/bc/8986948f05e3ea490b8442ea1c1d4d990b24a7e43d8a51b2c7d8b1dced36/pillow-12.2.0-cp313-cp313-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:766cef22385fa1091258ad7e6216792b156dc16d8d3fa607e7545b2b72061f1c", size = 3640401, upload-time = "2026-04-01T14:43:45.87Z" }, + { url = "https://files.pythonhosted.org/packages/34/46/6c717baadcd62bc8ed51d238d521ab651eaa74838291bda1f86fe1f864c9/pillow-12.2.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:5d2fd0fa6b5d9d1de415060363433f28da8b1526c1c129020435e186794b3795", size = 5308094, upload-time = "2026-04-01T14:43:48.438Z" }, + { url = "https://files.pythonhosted.org/packages/71/43/905a14a8b17fdb1ccb58d282454490662d2cb89a6bfec26af6d3520da5ec/pillow-12.2.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:56b25336f502b6ed02e889f4ece894a72612fe885889a6e8c4c80239ff6e5f5f", size = 4695402, upload-time = "2026-04-01T14:43:51.292Z" }, + { url = "https://files.pythonhosted.org/packages/73/dd/42107efcb777b16fa0393317eac58f5b5cf30e8392e266e76e51cff28c3d/pillow-12.2.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:f1c943e96e85df3d3478f7b691f229887e143f81fedab9b20205349ab04d73ed", size = 6280005, upload-time = "2026-04-01T14:43:54.242Z" }, + { url = "https://files.pythonhosted.org/packages/a8/68/b93e09e5e8549019e61acf49f65b1a8530765a7f812c77a7461bca7e4494/pillow-12.2.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:03f6fab9219220f041c74aeaa2939ff0062bd5c364ba9ce037197f4c6d498cd9", size = 8090669, upload-time = "2026-04-01T14:43:57.335Z" }, + { url = "https://files.pythonhosted.org/packages/4b/6e/3ccb54ce8ec4ddd1accd2d89004308b7b0b21c4ac3d20fa70af4760a4330/pillow-12.2.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5cdfebd752ec52bf5bb4e35d9c64b40826bc5b40a13df7c3cda20a2c03a0f5ed", size = 6395194, upload-time = "2026-04-01T14:43:59.864Z" }, + { url = "https://files.pythonhosted.org/packages/67/ee/21d4e8536afd1a328f01b359b4d3997b291ffd35a237c877b331c1c3b71c/pillow-12.2.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:eedf4b74eda2b5a4b2b2fb4c006d6295df3bf29e459e198c90ea48e130dc75c3", size = 7082423, upload-time = "2026-04-01T14:44:02.74Z" }, + { url = "https://files.pythonhosted.org/packages/78/5f/e9f86ab0146464e8c133fe85df987ed9e77e08b29d8d35f9f9f4d6f917ba/pillow-12.2.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:00a2865911330191c0b818c59103b58a5e697cae67042366970a6b6f1b20b7f9", size = 6505667, upload-time = "2026-04-01T14:44:05.381Z" }, + { url = "https://files.pythonhosted.org/packages/ed/1e/409007f56a2fdce61584fd3acbc2bbc259857d555196cedcadc68c015c82/pillow-12.2.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:1e1757442ed87f4912397c6d35a0db6a7b52592156014706f17658ff58bbf795", size = 7208580, upload-time = "2026-04-01T14:44:08.39Z" }, + { url = "https://files.pythonhosted.org/packages/23/c4/7349421080b12fb35414607b8871e9534546c128a11965fd4a7002ccfbee/pillow-12.2.0-cp313-cp313-win32.whl", hash = "sha256:144748b3af2d1b358d41286056d0003f47cb339b8c43a9ea42f5fea4d8c66b6e", size = 6375896, upload-time = "2026-04-01T14:44:11.197Z" }, + { url = "https://files.pythonhosted.org/packages/3f/82/8a3739a5e470b3c6cbb1d21d315800d8e16bff503d1f16b03a4ec3212786/pillow-12.2.0-cp313-cp313-win_amd64.whl", hash = "sha256:390ede346628ccc626e5730107cde16c42d3836b89662a115a921f28440e6a3b", size = 7081266, upload-time = "2026-04-01T14:44:13.947Z" }, + { url = "https://files.pythonhosted.org/packages/c3/25/f968f618a062574294592f668218f8af564830ccebdd1fa6200f598e65c5/pillow-12.2.0-cp313-cp313-win_arm64.whl", hash = "sha256:8023abc91fba39036dbce14a7d6535632f99c0b857807cbbbf21ecc9f4717f06", size = 2463508, upload-time = "2026-04-01T14:44:16.312Z" }, + { url = "https://files.pythonhosted.org/packages/4d/a4/b342930964e3cb4dce5038ae34b0eab4653334995336cd486c5a8c25a00c/pillow-12.2.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:042db20a421b9bafecc4b84a8b6e444686bd9d836c7fd24542db3e7df7baad9b", size = 5309927, upload-time = "2026-04-01T14:44:18.89Z" }, + { url = "https://files.pythonhosted.org/packages/9f/de/23198e0a65a9cf06123f5435a5d95cea62a635697f8f03d134d3f3a96151/pillow-12.2.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:dd025009355c926a84a612fecf58bb315a3f6814b17ead51a8e48d3823d9087f", size = 4698624, upload-time = "2026-04-01T14:44:21.115Z" }, + { url = "https://files.pythonhosted.org/packages/01/a6/1265e977f17d93ea37aa28aa81bad4fa597933879fac2520d24e021c8da3/pillow-12.2.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:88ddbc66737e277852913bd1e07c150cc7bb124539f94c4e2df5344494e0a612", size = 6321252, upload-time = "2026-04-01T14:44:23.663Z" }, + { url = "https://files.pythonhosted.org/packages/3c/83/5982eb4a285967baa70340320be9f88e57665a387e3a53a7f0db8231a0cd/pillow-12.2.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d362d1878f00c142b7e1a16e6e5e780f02be8195123f164edf7eddd911eefe7c", size = 8126550, upload-time = "2026-04-01T14:44:26.772Z" }, + { url = "https://files.pythonhosted.org/packages/4e/48/6ffc514adce69f6050d0753b1a18fd920fce8cac87620d5a31231b04bfc5/pillow-12.2.0-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2c727a6d53cb0018aadd8018c2b938376af27914a68a492f59dfcaca650d5eea", size = 6433114, upload-time = "2026-04-01T14:44:29.615Z" }, + { url = "https://files.pythonhosted.org/packages/36/a3/f9a77144231fb8d40ee27107b4463e205fa4677e2ca2548e14da5cf18dce/pillow-12.2.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:efd8c21c98c5cc60653bcb311bef2ce0401642b7ce9d09e03a7da87c878289d4", size = 7115667, upload-time = "2026-04-01T14:44:32.773Z" }, + { url = "https://files.pythonhosted.org/packages/c1/fc/ac4ee3041e7d5a565e1c4fd72a113f03b6394cc72ab7089d27608f8aaccb/pillow-12.2.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:9f08483a632889536b8139663db60f6724bfcb443c96f1b18855860d7d5c0fd4", size = 6538966, upload-time = "2026-04-01T14:44:35.252Z" }, + { url = "https://files.pythonhosted.org/packages/c0/a8/27fb307055087f3668f6d0a8ccb636e7431d56ed0750e07a60547b1e083e/pillow-12.2.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:dac8d77255a37e81a2efcbd1fc05f1c15ee82200e6c240d7e127e25e365c39ea", size = 7238241, upload-time = "2026-04-01T14:44:37.875Z" }, + { url = "https://files.pythonhosted.org/packages/ad/4b/926ab182c07fccae9fcb120043464e1ff1564775ec8864f21a0ebce6ac25/pillow-12.2.0-cp313-cp313t-win32.whl", hash = "sha256:ee3120ae9dff32f121610bb08e4313be87e03efeadfc6c0d18f89127e24d0c24", size = 6379592, upload-time = "2026-04-01T14:44:40.336Z" }, + { url = "https://files.pythonhosted.org/packages/c2/c4/f9e476451a098181b30050cc4c9a3556b64c02cf6497ea421ac047e89e4b/pillow-12.2.0-cp313-cp313t-win_amd64.whl", hash = "sha256:325ca0528c6788d2a6c3d40e3568639398137346c3d6e66bb61db96b96511c98", size = 7085542, upload-time = "2026-04-01T14:44:43.251Z" }, + { url = "https://files.pythonhosted.org/packages/00/a4/285f12aeacbe2d6dc36c407dfbbe9e96d4a80b0fb710a337f6d2ad978c75/pillow-12.2.0-cp313-cp313t-win_arm64.whl", hash = "sha256:2e5a76d03a6c6dcef67edabda7a52494afa4035021a79c8558e14af25313d453", size = 2465765, upload-time = "2026-04-01T14:44:45.996Z" }, + { url = "https://files.pythonhosted.org/packages/bf/98/4595daa2365416a86cb0d495248a393dfc84e96d62ad080c8546256cb9c0/pillow-12.2.0-cp314-cp314-ios_13_0_arm64_iphoneos.whl", hash = "sha256:3adc9215e8be0448ed6e814966ecf3d9952f0ea40eb14e89a102b87f450660d8", size = 4100848, upload-time = "2026-04-01T14:44:48.48Z" }, + { url = "https://files.pythonhosted.org/packages/0b/79/40184d464cf89f6663e18dfcf7ca21aae2491fff1a16127681bf1fa9b8cf/pillow-12.2.0-cp314-cp314-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:6a9adfc6d24b10f89588096364cc726174118c62130c817c2837c60cf08a392b", size = 4176515, upload-time = "2026-04-01T14:44:51.353Z" }, + { url = "https://files.pythonhosted.org/packages/b0/63/703f86fd4c422a9cf722833670f4f71418fb116b2853ff7da722ea43f184/pillow-12.2.0-cp314-cp314-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:6a6e67ea2e6feda684ed370f9a1c52e7a243631c025ba42149a2cc5934dec295", size = 3640159, upload-time = "2026-04-01T14:44:53.588Z" }, + { url = "https://files.pythonhosted.org/packages/71/e0/fb22f797187d0be2270f83500aab851536101b254bfa1eae10795709d283/pillow-12.2.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:2bb4a8d594eacdfc59d9e5ad972aa8afdd48d584ffd5f13a937a664c3e7db0ed", size = 5312185, upload-time = "2026-04-01T14:44:56.039Z" }, + { url = "https://files.pythonhosted.org/packages/ba/8c/1a9e46228571de18f8e28f16fabdfc20212a5d019f3e3303452b3f0a580d/pillow-12.2.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:80b2da48193b2f33ed0c32c38140f9d3186583ce7d516526d462645fd98660ae", size = 4695386, upload-time = "2026-04-01T14:44:58.663Z" }, + { url = "https://files.pythonhosted.org/packages/70/62/98f6b7f0c88b9addd0e87c217ded307b36be024d4ff8869a812b241d1345/pillow-12.2.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:22db17c68434de69d8ecfc2fe821569195c0c373b25cccb9cbdacf2c6e53c601", size = 6280384, upload-time = "2026-04-01T14:45:01.5Z" }, + { url = "https://files.pythonhosted.org/packages/5e/03/688747d2e91cfbe0e64f316cd2e8005698f76ada3130d0194664174fa5de/pillow-12.2.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:7b14cc0106cd9aecda615dd6903840a058b4700fcb817687d0ee4fc8b6e389be", size = 8091599, upload-time = "2026-04-01T14:45:04.5Z" }, + { url = "https://files.pythonhosted.org/packages/f6/35/577e22b936fcdd66537329b33af0b4ccfefaeabd8aec04b266528cddb33c/pillow-12.2.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8cbeb542b2ebc6fcdacabf8aca8c1a97c9b3ad3927d46b8723f9d4f033288a0f", size = 6396021, upload-time = "2026-04-01T14:45:07.117Z" }, + { url = "https://files.pythonhosted.org/packages/11/8d/d2532ad2a603ca2b93ad9f5135732124e57811d0168155852f37fbce2458/pillow-12.2.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4bfd07bc812fbd20395212969e41931001fd59eb55a60658b0e5710872e95286", size = 7083360, upload-time = "2026-04-01T14:45:09.763Z" }, + { url = "https://files.pythonhosted.org/packages/5e/26/d325f9f56c7e039034897e7380e9cc202b1e368bfd04d4cbe6a441f02885/pillow-12.2.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:9aba9a17b623ef750a4d11b742cbafffeb48a869821252b30ee21b5e91392c50", size = 6507628, upload-time = "2026-04-01T14:45:12.378Z" }, + { url = "https://files.pythonhosted.org/packages/5f/f7/769d5632ffb0988f1c5e7660b3e731e30f7f8ec4318e94d0a5d674eb65a4/pillow-12.2.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:deede7c263feb25dba4e82ea23058a235dcc2fe1f6021025dc71f2b618e26104", size = 7209321, upload-time = "2026-04-01T14:45:15.122Z" }, + { url = "https://files.pythonhosted.org/packages/6a/7a/c253e3c645cd47f1aceea6a8bacdba9991bf45bb7dfe927f7c893e89c93c/pillow-12.2.0-cp314-cp314-win32.whl", hash = "sha256:632ff19b2778e43162304d50da0181ce24ac5bb8180122cbe1bf4673428328c7", size = 6479723, upload-time = "2026-04-01T14:45:17.797Z" }, + { url = "https://files.pythonhosted.org/packages/cd/8b/601e6566b957ca50e28725cb6c355c59c2c8609751efbecd980db44e0349/pillow-12.2.0-cp314-cp314-win_amd64.whl", hash = "sha256:4e6c62e9d237e9b65fac06857d511e90d8461a32adcc1b9065ea0c0fa3a28150", size = 7217400, upload-time = "2026-04-01T14:45:20.529Z" }, + { url = "https://files.pythonhosted.org/packages/d6/94/220e46c73065c3e2951bb91c11a1fb636c8c9ad427ac3ce7d7f3359b9b2f/pillow-12.2.0-cp314-cp314-win_arm64.whl", hash = "sha256:b1c1fbd8a5a1af3412a0810d060a78b5136ec0836c8a4ef9aa11807f2a22f4e1", size = 2554835, upload-time = "2026-04-01T14:45:23.162Z" }, + { url = "https://files.pythonhosted.org/packages/b6/ab/1b426a3974cb0e7da5c29ccff4807871d48110933a57207b5a676cccc155/pillow-12.2.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:57850958fe9c751670e49b2cecf6294acc99e562531f4bd317fa5ddee2068463", size = 5314225, upload-time = "2026-04-01T14:45:25.637Z" }, + { url = "https://files.pythonhosted.org/packages/19/1e/dce46f371be2438eecfee2a1960ee2a243bbe5e961890146d2dee1ff0f12/pillow-12.2.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:d5d38f1411c0ed9f97bcb49b7bd59b6b7c314e0e27420e34d99d844b9ce3b6f3", size = 4698541, upload-time = "2026-04-01T14:45:28.355Z" }, + { url = "https://files.pythonhosted.org/packages/55/c3/7fbecf70adb3a0c33b77a300dc52e424dc22ad8cdc06557a2e49523b703d/pillow-12.2.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:5c0a9f29ca8e79f09de89293f82fc9b0270bb4af1d58bc98f540cc4aedf03166", size = 6322251, upload-time = "2026-04-01T14:45:30.924Z" }, + { url = "https://files.pythonhosted.org/packages/1c/3c/7fbc17cfb7e4fe0ef1642e0abc17fc6c94c9f7a16be41498e12e2ba60408/pillow-12.2.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1610dd6c61621ae1cf811bef44d77e149ce3f7b95afe66a4512f8c59f25d9ebe", size = 8127807, upload-time = "2026-04-01T14:45:33.908Z" }, + { url = "https://files.pythonhosted.org/packages/ff/c3/a8ae14d6defd2e448493ff512fae903b1e9bd40b72efb6ec55ce0048c8ce/pillow-12.2.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0a34329707af4f73cf1782a36cd2289c0368880654a2c11f027bcee9052d35dd", size = 6433935, upload-time = "2026-04-01T14:45:36.623Z" }, + { url = "https://files.pythonhosted.org/packages/6e/32/2880fb3a074847ac159d8f902cb43278a61e85f681661e7419e6596803ed/pillow-12.2.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8e9c4f5b3c546fa3458a29ab22646c1c6c787ea8f5ef51300e5a60300736905e", size = 7116720, upload-time = "2026-04-01T14:45:39.258Z" }, + { url = "https://files.pythonhosted.org/packages/46/87/495cc9c30e0129501643f24d320076f4cc54f718341df18cc70ec94c44e1/pillow-12.2.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:fb043ee2f06b41473269765c2feae53fc2e2fbf96e5e22ca94fb5ad677856f06", size = 6540498, upload-time = "2026-04-01T14:45:41.879Z" }, + { url = "https://files.pythonhosted.org/packages/18/53/773f5edca692009d883a72211b60fdaf8871cbef075eaa9d577f0a2f989e/pillow-12.2.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:f278f034eb75b4e8a13a54a876cc4a5ab39173d2cdd93a638e1b467fc545ac43", size = 7239413, upload-time = "2026-04-01T14:45:44.705Z" }, + { url = "https://files.pythonhosted.org/packages/c9/e4/4b64a97d71b2a83158134abbb2f5bd3f8a2ea691361282f010998f339ec7/pillow-12.2.0-cp314-cp314t-win32.whl", hash = "sha256:6bb77b2dcb06b20f9f4b4a8454caa581cd4dd0643a08bacf821216a16d9c8354", size = 6482084, upload-time = "2026-04-01T14:45:47.568Z" }, + { url = "https://files.pythonhosted.org/packages/ba/13/306d275efd3a3453f72114b7431c877d10b1154014c1ebbedd067770d629/pillow-12.2.0-cp314-cp314t-win_amd64.whl", hash = "sha256:6562ace0d3fb5f20ed7290f1f929cae41b25ae29528f2af1722966a0a02e2aa1", size = 7225152, upload-time = "2026-04-01T14:45:50.032Z" }, + { url = "https://files.pythonhosted.org/packages/ff/6e/cf826fae916b8658848d7b9f38d88da6396895c676e8086fc0988073aaf8/pillow-12.2.0-cp314-cp314t-win_arm64.whl", hash = "sha256:aa88ccfe4e32d362816319ed727a004423aab09c5cea43c01a4b435643fa34eb", size = 2556579, upload-time = "2026-04-01T14:45:52.529Z" }, +] + +[[package]] +name = "platformdirs" +version = "4.9.6" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/9f/4a/0883b8e3802965322523f0b200ecf33d31f10991d0401162f4b23c698b42/platformdirs-4.9.6.tar.gz", hash = "sha256:3bfa75b0ad0db84096ae777218481852c0ebc6c727b3168c1b9e0118e458cf0a", size = 29400, upload-time = "2026-04-09T00:04:10.812Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/75/a6/a0a304dc33b49145b21f4808d763822111e67d1c3a32b524a1baf947b6e1/platformdirs-4.9.6-py3-none-any.whl", hash = "sha256:e61adb1d5e5cb3441b4b7710bea7e4c12250ca49439228cc1021c00dcfac0917", size = 21348, upload-time = "2026-04-09T00:04:09.463Z" }, +] + +[[package]] +name = "prometheus-client" +version = "0.25.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/1b/fb/d9aa83ffe43ce1f19e557c0971d04b90561b0cfd50762aafb01968285553/prometheus_client-0.25.0.tar.gz", hash = "sha256:5e373b75c31afb3c86f1a52fa1ad470c9aace18082d39ec0d2f918d11cc9ba28", size = 86035, upload-time = "2026-04-09T19:53:42.359Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/8d/9b/d4b1e644385499c8346fa9b622a3f030dce14cd6ef8a1871c221a17a67e7/prometheus_client-0.25.0-py3-none-any.whl", hash = "sha256:d5aec89e349a6ec230805d0df882f3807f74fd6c1a2fa86864e3c2279059fed1", size = 64154, upload-time = "2026-04-09T19:53:41.324Z" }, +] + +[[package]] +name = "prompt-toolkit" +version = "3.0.52" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "wcwidth" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/a1/96/06e01a7b38dce6fe1db213e061a4602dd6032a8a97ef6c1a862537732421/prompt_toolkit-3.0.52.tar.gz", hash = "sha256:28cde192929c8e7321de85de1ddbe736f1375148b02f2e17edd840042b1be855", size = 434198, upload-time = "2025-08-27T15:24:02.057Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/84/03/0d3ce49e2505ae70cf43bc5bb3033955d2fc9f932163e84dc0779cc47f48/prompt_toolkit-3.0.52-py3-none-any.whl", hash = "sha256:9aac639a3bbd33284347de5ad8d68ecc044b91a762dc39b7c21095fcd6a19955", size = 391431, upload-time = "2025-08-27T15:23:59.498Z" }, +] + +[[package]] +name = "propcache" +version = "0.4.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/9e/da/e9fc233cf63743258bff22b3dfa7ea5baef7b5bc324af47a0ad89b8ffc6f/propcache-0.4.1.tar.gz", hash = "sha256:f48107a8c637e80362555f37ecf49abe20370e557cc4ab374f04ec4423c97c3d", size = 46442, upload-time = "2025-10-08T19:49:02.291Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/bf/df/6d9c1b6ac12b003837dde8a10231a7344512186e87b36e855bef32241942/propcache-0.4.1-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:43eedf29202c08550aac1d14e0ee619b0430aaef78f85864c1a892294fbc28cf", size = 77750, upload-time = "2025-10-08T19:47:07.648Z" }, + { url = "https://files.pythonhosted.org/packages/8b/e8/677a0025e8a2acf07d3418a2e7ba529c9c33caf09d3c1f25513023c1db56/propcache-0.4.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:d62cdfcfd89ccb8de04e0eda998535c406bf5e060ffd56be6c586cbcc05b3311", size = 44780, upload-time = "2025-10-08T19:47:08.851Z" }, + { url = "https://files.pythonhosted.org/packages/89/a4/92380f7ca60f99ebae761936bc48a72a639e8a47b29050615eef757cb2a7/propcache-0.4.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:cae65ad55793da34db5f54e4029b89d3b9b9490d8abe1b4c7ab5d4b8ec7ebf74", size = 46308, upload-time = "2025-10-08T19:47:09.982Z" }, + { url = "https://files.pythonhosted.org/packages/2d/48/c5ac64dee5262044348d1d78a5f85dd1a57464a60d30daee946699963eb3/propcache-0.4.1-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:333ddb9031d2704a301ee3e506dc46b1fe5f294ec198ed6435ad5b6a085facfe", size = 208182, upload-time = "2025-10-08T19:47:11.319Z" }, + { url = "https://files.pythonhosted.org/packages/c6/0c/cd762dd011a9287389a6a3eb43aa30207bde253610cca06824aeabfe9653/propcache-0.4.1-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:fd0858c20f078a32cf55f7e81473d96dcf3b93fd2ccdb3d40fdf54b8573df3af", size = 211215, upload-time = "2025-10-08T19:47:13.146Z" }, + { url = "https://files.pythonhosted.org/packages/30/3e/49861e90233ba36890ae0ca4c660e95df565b2cd15d4a68556ab5865974e/propcache-0.4.1-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:678ae89ebc632c5c204c794f8dab2837c5f159aeb59e6ed0539500400577298c", size = 218112, upload-time = "2025-10-08T19:47:14.913Z" }, + { url = "https://files.pythonhosted.org/packages/f1/8b/544bc867e24e1bd48f3118cecd3b05c694e160a168478fa28770f22fd094/propcache-0.4.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d472aeb4fbf9865e0c6d622d7f4d54a4e101a89715d8904282bb5f9a2f476c3f", size = 204442, upload-time = "2025-10-08T19:47:16.277Z" }, + { url = "https://files.pythonhosted.org/packages/50/a6/4282772fd016a76d3e5c0df58380a5ea64900afd836cec2c2f662d1b9bb3/propcache-0.4.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:4d3df5fa7e36b3225954fba85589da77a0fe6a53e3976de39caf04a0db4c36f1", size = 199398, upload-time = "2025-10-08T19:47:17.962Z" }, + { url = "https://files.pythonhosted.org/packages/3e/ec/d8a7cd406ee1ddb705db2139f8a10a8a427100347bd698e7014351c7af09/propcache-0.4.1-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:ee17f18d2498f2673e432faaa71698032b0127ebf23ae5974eeaf806c279df24", size = 196920, upload-time = "2025-10-08T19:47:19.355Z" }, + { url = "https://files.pythonhosted.org/packages/f6/6c/f38ab64af3764f431e359f8baf9e0a21013e24329e8b85d2da32e8ed07ca/propcache-0.4.1-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:580e97762b950f993ae618e167e7be9256b8353c2dcd8b99ec100eb50f5286aa", size = 203748, upload-time = "2025-10-08T19:47:21.338Z" }, + { url = "https://files.pythonhosted.org/packages/d6/e3/fa846bd70f6534d647886621388f0a265254d30e3ce47e5c8e6e27dbf153/propcache-0.4.1-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:501d20b891688eb8e7aa903021f0b72d5a55db40ffaab27edefd1027caaafa61", size = 205877, upload-time = "2025-10-08T19:47:23.059Z" }, + { url = "https://files.pythonhosted.org/packages/e2/39/8163fc6f3133fea7b5f2827e8eba2029a0277ab2c5beee6c1db7b10fc23d/propcache-0.4.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:9a0bd56e5b100aef69bd8562b74b46254e7c8812918d3baa700c8a8009b0af66", size = 199437, upload-time = "2025-10-08T19:47:24.445Z" }, + { url = "https://files.pythonhosted.org/packages/93/89/caa9089970ca49c7c01662bd0eeedfe85494e863e8043565aeb6472ce8fe/propcache-0.4.1-cp313-cp313-win32.whl", hash = "sha256:bcc9aaa5d80322bc2fb24bb7accb4a30f81e90ab8d6ba187aec0744bc302ad81", size = 37586, upload-time = "2025-10-08T19:47:25.736Z" }, + { url = "https://files.pythonhosted.org/packages/f5/ab/f76ec3c3627c883215b5c8080debb4394ef5a7a29be811f786415fc1e6fd/propcache-0.4.1-cp313-cp313-win_amd64.whl", hash = "sha256:381914df18634f5494334d201e98245c0596067504b9372d8cf93f4bb23e025e", size = 40790, upload-time = "2025-10-08T19:47:26.847Z" }, + { url = "https://files.pythonhosted.org/packages/59/1b/e71ae98235f8e2ba5004d8cb19765a74877abf189bc53fc0c80d799e56c3/propcache-0.4.1-cp313-cp313-win_arm64.whl", hash = "sha256:8873eb4460fd55333ea49b7d189749ecf6e55bf85080f11b1c4530ed3034cba1", size = 37158, upload-time = "2025-10-08T19:47:27.961Z" }, + { url = "https://files.pythonhosted.org/packages/83/ce/a31bbdfc24ee0dcbba458c8175ed26089cf109a55bbe7b7640ed2470cfe9/propcache-0.4.1-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:92d1935ee1f8d7442da9c0c4fa7ac20d07e94064184811b685f5c4fada64553b", size = 81451, upload-time = "2025-10-08T19:47:29.445Z" }, + { url = "https://files.pythonhosted.org/packages/25/9c/442a45a470a68456e710d96cacd3573ef26a1d0a60067e6a7d5e655621ed/propcache-0.4.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:473c61b39e1460d386479b9b2f337da492042447c9b685f28be4f74d3529e566", size = 46374, upload-time = "2025-10-08T19:47:30.579Z" }, + { url = "https://files.pythonhosted.org/packages/f4/bf/b1d5e21dbc3b2e889ea4327044fb16312a736d97640fb8b6aa3f9c7b3b65/propcache-0.4.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:c0ef0aaafc66fbd87842a3fe3902fd889825646bc21149eafe47be6072725835", size = 48396, upload-time = "2025-10-08T19:47:31.79Z" }, + { url = "https://files.pythonhosted.org/packages/f4/04/5b4c54a103d480e978d3c8a76073502b18db0c4bc17ab91b3cb5092ad949/propcache-0.4.1-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f95393b4d66bfae908c3ca8d169d5f79cd65636ae15b5e7a4f6e67af675adb0e", size = 275950, upload-time = "2025-10-08T19:47:33.481Z" }, + { url = "https://files.pythonhosted.org/packages/b4/c1/86f846827fb969c4b78b0af79bba1d1ea2156492e1b83dea8b8a6ae27395/propcache-0.4.1-cp313-cp313t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:c07fda85708bc48578467e85099645167a955ba093be0a2dcba962195676e859", size = 273856, upload-time = "2025-10-08T19:47:34.906Z" }, + { url = "https://files.pythonhosted.org/packages/36/1d/fc272a63c8d3bbad6878c336c7a7dea15e8f2d23a544bda43205dfa83ada/propcache-0.4.1-cp313-cp313t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:af223b406d6d000830c6f65f1e6431783fc3f713ba3e6cc8c024d5ee96170a4b", size = 280420, upload-time = "2025-10-08T19:47:36.338Z" }, + { url = "https://files.pythonhosted.org/packages/07/0c/01f2219d39f7e53d52e5173bcb09c976609ba30209912a0680adfb8c593a/propcache-0.4.1-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a78372c932c90ee474559c5ddfffd718238e8673c340dc21fe45c5b8b54559a0", size = 263254, upload-time = "2025-10-08T19:47:37.692Z" }, + { url = "https://files.pythonhosted.org/packages/2d/18/cd28081658ce597898f0c4d174d4d0f3c5b6d4dc27ffafeef835c95eb359/propcache-0.4.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:564d9f0d4d9509e1a870c920a89b2fec951b44bf5ba7d537a9e7c1ccec2c18af", size = 261205, upload-time = "2025-10-08T19:47:39.659Z" }, + { url = "https://files.pythonhosted.org/packages/7a/71/1f9e22eb8b8316701c2a19fa1f388c8a3185082607da8e406a803c9b954e/propcache-0.4.1-cp313-cp313t-musllinux_1_2_armv7l.whl", hash = "sha256:17612831fda0138059cc5546f4d12a2aacfb9e47068c06af35c400ba58ba7393", size = 247873, upload-time = "2025-10-08T19:47:41.084Z" }, + { url = "https://files.pythonhosted.org/packages/4a/65/3d4b61f36af2b4eddba9def857959f1016a51066b4f1ce348e0cf7881f58/propcache-0.4.1-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:41a89040cb10bd345b3c1a873b2bf36413d48da1def52f268a055f7398514874", size = 262739, upload-time = "2025-10-08T19:47:42.51Z" }, + { url = "https://files.pythonhosted.org/packages/2a/42/26746ab087faa77c1c68079b228810436ccd9a5ce9ac85e2b7307195fd06/propcache-0.4.1-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:e35b88984e7fa64aacecea39236cee32dd9bd8c55f57ba8a75cf2399553f9bd7", size = 263514, upload-time = "2025-10-08T19:47:43.927Z" }, + { url = "https://files.pythonhosted.org/packages/94/13/630690fe201f5502d2403dd3cfd451ed8858fe3c738ee88d095ad2ff407b/propcache-0.4.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:6f8b465489f927b0df505cbe26ffbeed4d6d8a2bbc61ce90eb074ff129ef0ab1", size = 257781, upload-time = "2025-10-08T19:47:45.448Z" }, + { url = "https://files.pythonhosted.org/packages/92/f7/1d4ec5841505f423469efbfc381d64b7b467438cd5a4bbcbb063f3b73d27/propcache-0.4.1-cp313-cp313t-win32.whl", hash = "sha256:2ad890caa1d928c7c2965b48f3a3815c853180831d0e5503d35cf00c472f4717", size = 41396, upload-time = "2025-10-08T19:47:47.202Z" }, + { url = "https://files.pythonhosted.org/packages/48/f0/615c30622316496d2cbbc29f5985f7777d3ada70f23370608c1d3e081c1f/propcache-0.4.1-cp313-cp313t-win_amd64.whl", hash = "sha256:f7ee0e597f495cf415bcbd3da3caa3bd7e816b74d0d52b8145954c5e6fd3ff37", size = 44897, upload-time = "2025-10-08T19:47:48.336Z" }, + { url = "https://files.pythonhosted.org/packages/fd/ca/6002e46eccbe0e33dcd4069ef32f7f1c9e243736e07adca37ae8c4830ec3/propcache-0.4.1-cp313-cp313t-win_arm64.whl", hash = "sha256:929d7cbe1f01bb7baffb33dc14eb5691c95831450a26354cd210a8155170c93a", size = 39789, upload-time = "2025-10-08T19:47:49.876Z" }, + { url = "https://files.pythonhosted.org/packages/8e/5c/bca52d654a896f831b8256683457ceddd490ec18d9ec50e97dfd8fc726a8/propcache-0.4.1-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:3f7124c9d820ba5548d431afb4632301acf965db49e666aa21c305cbe8c6de12", size = 78152, upload-time = "2025-10-08T19:47:51.051Z" }, + { url = "https://files.pythonhosted.org/packages/65/9b/03b04e7d82a5f54fb16113d839f5ea1ede58a61e90edf515f6577c66fa8f/propcache-0.4.1-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:c0d4b719b7da33599dfe3b22d3db1ef789210a0597bc650b7cee9c77c2be8c5c", size = 44869, upload-time = "2025-10-08T19:47:52.594Z" }, + { url = "https://files.pythonhosted.org/packages/b2/fa/89a8ef0468d5833a23fff277b143d0573897cf75bd56670a6d28126c7d68/propcache-0.4.1-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:9f302f4783709a78240ebc311b793f123328716a60911d667e0c036bc5dcbded", size = 46596, upload-time = "2025-10-08T19:47:54.073Z" }, + { url = "https://files.pythonhosted.org/packages/86/bd/47816020d337f4a746edc42fe8d53669965138f39ee117414c7d7a340cfe/propcache-0.4.1-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c80ee5802e3fb9ea37938e7eecc307fb984837091d5fd262bb37238b1ae97641", size = 206981, upload-time = "2025-10-08T19:47:55.715Z" }, + { url = "https://files.pythonhosted.org/packages/df/f6/c5fa1357cc9748510ee55f37173eb31bfde6d94e98ccd9e6f033f2fc06e1/propcache-0.4.1-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ed5a841e8bb29a55fb8159ed526b26adc5bdd7e8bd7bf793ce647cb08656cdf4", size = 211490, upload-time = "2025-10-08T19:47:57.499Z" }, + { url = "https://files.pythonhosted.org/packages/80/1e/e5889652a7c4a3846683401a48f0f2e5083ce0ec1a8a5221d8058fbd1adf/propcache-0.4.1-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:55c72fd6ea2da4c318e74ffdf93c4fe4e926051133657459131a95c846d16d44", size = 215371, upload-time = "2025-10-08T19:47:59.317Z" }, + { url = "https://files.pythonhosted.org/packages/b2/f2/889ad4b2408f72fe1a4f6a19491177b30ea7bf1a0fd5f17050ca08cfc882/propcache-0.4.1-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8326e144341460402713f91df60ade3c999d601e7eb5ff8f6f7862d54de0610d", size = 201424, upload-time = "2025-10-08T19:48:00.67Z" }, + { url = "https://files.pythonhosted.org/packages/27/73/033d63069b57b0812c8bd19f311faebeceb6ba31b8f32b73432d12a0b826/propcache-0.4.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:060b16ae65bc098da7f6d25bf359f1f31f688384858204fe5d652979e0015e5b", size = 197566, upload-time = "2025-10-08T19:48:02.604Z" }, + { url = "https://files.pythonhosted.org/packages/dc/89/ce24f3dc182630b4e07aa6d15f0ff4b14ed4b9955fae95a0b54c58d66c05/propcache-0.4.1-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:89eb3fa9524f7bec9de6e83cf3faed9d79bffa560672c118a96a171a6f55831e", size = 193130, upload-time = "2025-10-08T19:48:04.499Z" }, + { url = "https://files.pythonhosted.org/packages/a9/24/ef0d5fd1a811fb5c609278d0209c9f10c35f20581fcc16f818da959fc5b4/propcache-0.4.1-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:dee69d7015dc235f526fe80a9c90d65eb0039103fe565776250881731f06349f", size = 202625, upload-time = "2025-10-08T19:48:06.213Z" }, + { url = "https://files.pythonhosted.org/packages/f5/02/98ec20ff5546f68d673df2f7a69e8c0d076b5abd05ca882dc7ee3a83653d/propcache-0.4.1-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:5558992a00dfd54ccbc64a32726a3357ec93825a418a401f5cc67df0ac5d9e49", size = 204209, upload-time = "2025-10-08T19:48:08.432Z" }, + { url = "https://files.pythonhosted.org/packages/a0/87/492694f76759b15f0467a2a93ab68d32859672b646aa8a04ce4864e7932d/propcache-0.4.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:c9b822a577f560fbd9554812526831712c1436d2c046cedee4c3796d3543b144", size = 197797, upload-time = "2025-10-08T19:48:09.968Z" }, + { url = "https://files.pythonhosted.org/packages/ee/36/66367de3575db1d2d3f3d177432bd14ee577a39d3f5d1b3d5df8afe3b6e2/propcache-0.4.1-cp314-cp314-win32.whl", hash = "sha256:ab4c29b49d560fe48b696cdcb127dd36e0bc2472548f3bf56cc5cb3da2b2984f", size = 38140, upload-time = "2025-10-08T19:48:11.232Z" }, + { url = "https://files.pythonhosted.org/packages/0c/2a/a758b47de253636e1b8aef181c0b4f4f204bf0dd964914fb2af90a95b49b/propcache-0.4.1-cp314-cp314-win_amd64.whl", hash = "sha256:5a103c3eb905fcea0ab98be99c3a9a5ab2de60228aa5aceedc614c0281cf6153", size = 41257, upload-time = "2025-10-08T19:48:12.707Z" }, + { url = "https://files.pythonhosted.org/packages/34/5e/63bd5896c3fec12edcbd6f12508d4890d23c265df28c74b175e1ef9f4f3b/propcache-0.4.1-cp314-cp314-win_arm64.whl", hash = "sha256:74c1fb26515153e482e00177a1ad654721bf9207da8a494a0c05e797ad27b992", size = 38097, upload-time = "2025-10-08T19:48:13.923Z" }, + { url = "https://files.pythonhosted.org/packages/99/85/9ff785d787ccf9bbb3f3106f79884a130951436f58392000231b4c737c80/propcache-0.4.1-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:824e908bce90fb2743bd6b59db36eb4f45cd350a39637c9f73b1c1ea66f5b75f", size = 81455, upload-time = "2025-10-08T19:48:15.16Z" }, + { url = "https://files.pythonhosted.org/packages/90/85/2431c10c8e7ddb1445c1f7c4b54d886e8ad20e3c6307e7218f05922cad67/propcache-0.4.1-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:c2b5e7db5328427c57c8e8831abda175421b709672f6cfc3d630c3b7e2146393", size = 46372, upload-time = "2025-10-08T19:48:16.424Z" }, + { url = "https://files.pythonhosted.org/packages/01/20/b0972d902472da9bcb683fa595099911f4d2e86e5683bcc45de60dd05dc3/propcache-0.4.1-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:6f6ff873ed40292cd4969ef5310179afd5db59fdf055897e282485043fc80ad0", size = 48411, upload-time = "2025-10-08T19:48:17.577Z" }, + { url = "https://files.pythonhosted.org/packages/e2/e3/7dc89f4f21e8f99bad3d5ddb3a3389afcf9da4ac69e3deb2dcdc96e74169/propcache-0.4.1-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:49a2dc67c154db2c1463013594c458881a069fcf98940e61a0569016a583020a", size = 275712, upload-time = "2025-10-08T19:48:18.901Z" }, + { url = "https://files.pythonhosted.org/packages/20/67/89800c8352489b21a8047c773067644e3897f02ecbbd610f4d46b7f08612/propcache-0.4.1-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:005f08e6a0529984491e37d8dbc3dd86f84bd78a8ceb5fa9a021f4c48d4984be", size = 273557, upload-time = "2025-10-08T19:48:20.762Z" }, + { url = "https://files.pythonhosted.org/packages/e2/a1/b52b055c766a54ce6d9c16d9aca0cad8059acd9637cdf8aa0222f4a026ef/propcache-0.4.1-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:5c3310452e0d31390da9035c348633b43d7e7feb2e37be252be6da45abd1abcc", size = 280015, upload-time = "2025-10-08T19:48:22.592Z" }, + { url = "https://files.pythonhosted.org/packages/48/c8/33cee30bd890672c63743049f3c9e4be087e6780906bfc3ec58528be59c1/propcache-0.4.1-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4c3c70630930447f9ef1caac7728c8ad1c56bc5015338b20fed0d08ea2480b3a", size = 262880, upload-time = "2025-10-08T19:48:23.947Z" }, + { url = "https://files.pythonhosted.org/packages/0c/b1/8f08a143b204b418285c88b83d00edbd61afbc2c6415ffafc8905da7038b/propcache-0.4.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:8e57061305815dfc910a3634dcf584f08168a8836e6999983569f51a8544cd89", size = 260938, upload-time = "2025-10-08T19:48:25.656Z" }, + { url = "https://files.pythonhosted.org/packages/cf/12/96e4664c82ca2f31e1c8dff86afb867348979eb78d3cb8546a680287a1e9/propcache-0.4.1-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:521a463429ef54143092c11a77e04056dd00636f72e8c45b70aaa3140d639726", size = 247641, upload-time = "2025-10-08T19:48:27.207Z" }, + { url = "https://files.pythonhosted.org/packages/18/ed/e7a9cfca28133386ba52278136d42209d3125db08d0a6395f0cba0c0285c/propcache-0.4.1-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:120c964da3fdc75e3731aa392527136d4ad35868cc556fd09bb6d09172d9a367", size = 262510, upload-time = "2025-10-08T19:48:28.65Z" }, + { url = "https://files.pythonhosted.org/packages/f5/76/16d8bf65e8845dd62b4e2b57444ab81f07f40caa5652b8969b87ddcf2ef6/propcache-0.4.1-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:d8f353eb14ee3441ee844ade4277d560cdd68288838673273b978e3d6d2c8f36", size = 263161, upload-time = "2025-10-08T19:48:30.133Z" }, + { url = "https://files.pythonhosted.org/packages/e7/70/c99e9edb5d91d5ad8a49fa3c1e8285ba64f1476782fed10ab251ff413ba1/propcache-0.4.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:ab2943be7c652f09638800905ee1bab2c544e537edb57d527997a24c13dc1455", size = 257393, upload-time = "2025-10-08T19:48:31.567Z" }, + { url = "https://files.pythonhosted.org/packages/08/02/87b25304249a35c0915d236575bc3574a323f60b47939a2262b77632a3ee/propcache-0.4.1-cp314-cp314t-win32.whl", hash = "sha256:05674a162469f31358c30bcaa8883cb7829fa3110bf9c0991fe27d7896c42d85", size = 42546, upload-time = "2025-10-08T19:48:32.872Z" }, + { url = "https://files.pythonhosted.org/packages/cb/ef/3c6ecf8b317aa982f309835e8f96987466123c6e596646d4e6a1dfcd080f/propcache-0.4.1-cp314-cp314t-win_amd64.whl", hash = "sha256:990f6b3e2a27d683cb7602ed6c86f15ee6b43b1194736f9baaeb93d0016633b1", size = 46259, upload-time = "2025-10-08T19:48:34.226Z" }, + { url = "https://files.pythonhosted.org/packages/c4/2d/346e946d4951f37eca1e4f55be0f0174c52cd70720f84029b02f296f4a38/propcache-0.4.1-cp314-cp314t-win_arm64.whl", hash = "sha256:ecef2343af4cc68e05131e45024ba34f6095821988a9d0a02aa7c73fcc448aa9", size = 40428, upload-time = "2025-10-08T19:48:35.441Z" }, + { url = "https://files.pythonhosted.org/packages/5b/5a/bc7b4a4ef808fa59a816c17b20c4bef6884daebbdf627ff2a161da67da19/propcache-0.4.1-py3-none-any.whl", hash = "sha256:af2a6052aeb6cf17d3e46ee169099044fd8224cbaf75c76a2ef596e8163e2237", size = 13305, upload-time = "2025-10-08T19:49:00.792Z" }, +] + +[[package]] +name = "protobuf" +version = "7.34.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/6b/6b/a0e95cad1ad7cc3f2c6821fcab91671bd5b78bd42afb357bb4765f29bc41/protobuf-7.34.1.tar.gz", hash = "sha256:9ce42245e704cc5027be797c1db1eb93184d44d1cdd71811fb2d9b25ad541280", size = 454708, upload-time = "2026-03-20T17:34:47.036Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ec/11/3325d41e6ee15bf1125654301211247b042563bcc898784351252549a8ad/protobuf-7.34.1-cp310-abi3-macosx_10_9_universal2.whl", hash = "sha256:d8b2cc79c4d8f62b293ad9b11ec3aebce9af481fa73e64556969f7345ebf9fc7", size = 429247, upload-time = "2026-03-20T17:34:37.024Z" }, + { url = "https://files.pythonhosted.org/packages/eb/9d/aa69df2724ff63efa6f72307b483ce0827f4347cc6d6df24b59e26659fef/protobuf-7.34.1-cp310-abi3-manylinux2014_aarch64.whl", hash = "sha256:5185e0e948d07abe94bb76ec9b8416b604cfe5da6f871d67aad30cbf24c3110b", size = 325753, upload-time = "2026-03-20T17:34:38.751Z" }, + { url = "https://files.pythonhosted.org/packages/92/e8/d174c91fd48e50101943f042b09af9029064810b734e4160bbe282fa1caa/protobuf-7.34.1-cp310-abi3-manylinux2014_s390x.whl", hash = "sha256:403b093a6e28a960372b44e5eb081775c9b056e816a8029c61231743d63f881a", size = 340198, upload-time = "2026-03-20T17:34:39.871Z" }, + { url = "https://files.pythonhosted.org/packages/53/1b/3b431694a4dc6d37b9f653f0c64b0a0d9ec074ee810710c0c3da21d67ba7/protobuf-7.34.1-cp310-abi3-manylinux2014_x86_64.whl", hash = "sha256:8ff40ce8cd688f7265326b38d5a1bed9bfdf5e6723d49961432f83e21d5713e4", size = 324267, upload-time = "2026-03-20T17:34:41.1Z" }, + { url = "https://files.pythonhosted.org/packages/85/29/64de04a0ac142fb685fd09999bc3d337943fb386f3a0ec57f92fd8203f97/protobuf-7.34.1-cp310-abi3-win32.whl", hash = "sha256:34b84ce27680df7cca9f231043ada0daa55d0c44a2ddfaa58ec1d0d89d8bf60a", size = 426628, upload-time = "2026-03-20T17:34:42.536Z" }, + { url = "https://files.pythonhosted.org/packages/4d/87/cb5e585192a22b8bd457df5a2c16a75ea0db9674c3a0a39fc9347d84e075/protobuf-7.34.1-cp310-abi3-win_amd64.whl", hash = "sha256:e97b55646e6ce5cbb0954a8c28cd39a5869b59090dfaa7df4598a7fba869468c", size = 437901, upload-time = "2026-03-20T17:34:44.112Z" }, + { url = "https://files.pythonhosted.org/packages/88/95/608f665226bca68b736b79e457fded9a2a38c4f4379a4a7614303d9db3bc/protobuf-7.34.1-py3-none-any.whl", hash = "sha256:bb3812cd53aefea2b028ef42bd780f5b96407247f20c6ef7c679807e9d188f11", size = 170715, upload-time = "2026-03-20T17:34:45.384Z" }, +] + +[[package]] +name = "psutil" +version = "7.2.2" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/aa/c6/d1ddf4abb55e93cebc4f2ed8b5d6dbad109ecb8d63748dd2b20ab5e57ebe/psutil-7.2.2.tar.gz", hash = "sha256:0746f5f8d406af344fd547f1c8daa5f5c33dbc293bb8d6a16d80b4bb88f59372", size = 493740, upload-time = "2026-01-28T18:14:54.428Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/51/08/510cbdb69c25a96f4ae523f733cdc963ae654904e8db864c07585ef99875/psutil-7.2.2-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:2edccc433cbfa046b980b0df0171cd25bcaeb3a68fe9022db0979e7aa74a826b", size = 130595, upload-time = "2026-01-28T18:14:57.293Z" }, + { url = "https://files.pythonhosted.org/packages/d6/f5/97baea3fe7a5a9af7436301f85490905379b1c6f2dd51fe3ecf24b4c5fbf/psutil-7.2.2-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:e78c8603dcd9a04c7364f1a3e670cea95d51ee865e4efb3556a3a63adef958ea", size = 131082, upload-time = "2026-01-28T18:14:59.732Z" }, + { url = "https://files.pythonhosted.org/packages/37/d6/246513fbf9fa174af531f28412297dd05241d97a75911ac8febefa1a53c6/psutil-7.2.2-cp313-cp313t-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1a571f2330c966c62aeda00dd24620425d4b0cc86881c89861fbc04549e5dc63", size = 181476, upload-time = "2026-01-28T18:15:01.884Z" }, + { url = "https://files.pythonhosted.org/packages/b8/b5/9182c9af3836cca61696dabe4fd1304e17bc56cb62f17439e1154f225dd3/psutil-7.2.2-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:917e891983ca3c1887b4ef36447b1e0873e70c933afc831c6b6da078ba474312", size = 184062, upload-time = "2026-01-28T18:15:04.436Z" }, + { url = "https://files.pythonhosted.org/packages/16/ba/0756dca669f5a9300d0cbcbfae9a4c30e446dfc7440ffe43ded5724bfd93/psutil-7.2.2-cp313-cp313t-win_amd64.whl", hash = "sha256:ab486563df44c17f5173621c7b198955bd6b613fb87c71c161f827d3fb149a9b", size = 139893, upload-time = "2026-01-28T18:15:06.378Z" }, + { url = "https://files.pythonhosted.org/packages/1c/61/8fa0e26f33623b49949346de05ec1ddaad02ed8ba64af45f40a147dbfa97/psutil-7.2.2-cp313-cp313t-win_arm64.whl", hash = "sha256:ae0aefdd8796a7737eccea863f80f81e468a1e4cf14d926bd9b6f5f2d5f90ca9", size = 135589, upload-time = "2026-01-28T18:15:08.03Z" }, + { url = "https://files.pythonhosted.org/packages/81/69/ef179ab5ca24f32acc1dac0c247fd6a13b501fd5534dbae0e05a1c48b66d/psutil-7.2.2-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:eed63d3b4d62449571547b60578c5b2c4bcccc5387148db46e0c2313dad0ee00", size = 130664, upload-time = "2026-01-28T18:15:09.469Z" }, + { url = "https://files.pythonhosted.org/packages/7b/64/665248b557a236d3fa9efc378d60d95ef56dd0a490c2cd37dafc7660d4a9/psutil-7.2.2-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:7b6d09433a10592ce39b13d7be5a54fbac1d1228ed29abc880fb23df7cb694c9", size = 131087, upload-time = "2026-01-28T18:15:11.724Z" }, + { url = "https://files.pythonhosted.org/packages/d5/2e/e6782744700d6759ebce3043dcfa661fb61e2fb752b91cdeae9af12c2178/psutil-7.2.2-cp314-cp314t-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1fa4ecf83bcdf6e6c8f4449aff98eefb5d0604bf88cb883d7da3d8d2d909546a", size = 182383, upload-time = "2026-01-28T18:15:13.445Z" }, + { url = "https://files.pythonhosted.org/packages/57/49/0a41cefd10cb7505cdc04dab3eacf24c0c2cb158a998b8c7b1d27ee2c1f5/psutil-7.2.2-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e452c464a02e7dc7822a05d25db4cde564444a67e58539a00f929c51eddda0cf", size = 185210, upload-time = "2026-01-28T18:15:16.002Z" }, + { url = "https://files.pythonhosted.org/packages/dd/2c/ff9bfb544f283ba5f83ba725a3c5fec6d6b10b8f27ac1dc641c473dc390d/psutil-7.2.2-cp314-cp314t-win_amd64.whl", hash = "sha256:c7663d4e37f13e884d13994247449e9f8f574bc4655d509c3b95e9ec9e2b9dc1", size = 141228, upload-time = "2026-01-28T18:15:18.385Z" }, + { url = "https://files.pythonhosted.org/packages/f2/fc/f8d9c31db14fcec13748d373e668bc3bed94d9077dbc17fb0eebc073233c/psutil-7.2.2-cp314-cp314t-win_arm64.whl", hash = "sha256:11fe5a4f613759764e79c65cf11ebdf26e33d6dd34336f8a337aa2996d71c841", size = 136284, upload-time = "2026-01-28T18:15:19.912Z" }, + { url = "https://files.pythonhosted.org/packages/e7/36/5ee6e05c9bd427237b11b3937ad82bb8ad2752d72c6969314590dd0c2f6e/psutil-7.2.2-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:ed0cace939114f62738d808fdcecd4c869222507e266e574799e9c0faa17d486", size = 129090, upload-time = "2026-01-28T18:15:22.168Z" }, + { url = "https://files.pythonhosted.org/packages/80/c4/f5af4c1ca8c1eeb2e92ccca14ce8effdeec651d5ab6053c589b074eda6e1/psutil-7.2.2-cp36-abi3-macosx_11_0_arm64.whl", hash = "sha256:1a7b04c10f32cc88ab39cbf606e117fd74721c831c98a27dc04578deb0c16979", size = 129859, upload-time = "2026-01-28T18:15:23.795Z" }, + { url = "https://files.pythonhosted.org/packages/b5/70/5d8df3b09e25bce090399cf48e452d25c935ab72dad19406c77f4e828045/psutil-7.2.2-cp36-abi3-manylinux2010_x86_64.manylinux_2_12_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:076a2d2f923fd4821644f5ba89f059523da90dc9014e85f8e45a5774ca5bc6f9", size = 155560, upload-time = "2026-01-28T18:15:25.976Z" }, + { url = "https://files.pythonhosted.org/packages/63/65/37648c0c158dc222aba51c089eb3bdfa238e621674dc42d48706e639204f/psutil-7.2.2-cp36-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b0726cecd84f9474419d67252add4ac0cd9811b04d61123054b9fb6f57df6e9e", size = 156997, upload-time = "2026-01-28T18:15:27.794Z" }, + { url = "https://files.pythonhosted.org/packages/8e/13/125093eadae863ce03c6ffdbae9929430d116a246ef69866dad94da3bfbc/psutil-7.2.2-cp36-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:fd04ef36b4a6d599bbdb225dd1d3f51e00105f6d48a28f006da7f9822f2606d8", size = 148972, upload-time = "2026-01-28T18:15:29.342Z" }, + { url = "https://files.pythonhosted.org/packages/04/78/0acd37ca84ce3ddffaa92ef0f571e073faa6d8ff1f0559ab1272188ea2be/psutil-7.2.2-cp36-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:b58fabe35e80b264a4e3bb23e6b96f9e45a3df7fb7eed419ac0e5947c61e47cc", size = 148266, upload-time = "2026-01-28T18:15:31.597Z" }, + { url = "https://files.pythonhosted.org/packages/b4/90/e2159492b5426be0c1fef7acba807a03511f97c5f86b3caeda6ad92351a7/psutil-7.2.2-cp37-abi3-win_amd64.whl", hash = "sha256:eb7e81434c8d223ec4a219b5fc1c47d0417b12be7ea866e24fb5ad6e84b3d988", size = 137737, upload-time = "2026-01-28T18:15:33.849Z" }, + { url = "https://files.pythonhosted.org/packages/8c/c7/7bb2e321574b10df20cbde462a94e2b71d05f9bbda251ef27d104668306a/psutil-7.2.2-cp37-abi3-win_arm64.whl", hash = "sha256:8c233660f575a5a89e6d4cb65d9f938126312bca76d8fe087b947b3a1aaac9ee", size = 134617, upload-time = "2026-01-28T18:15:36.514Z" }, +] + +[[package]] +name = "ptyprocess" +version = "0.7.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/20/e5/16ff212c1e452235a90aeb09066144d0c5a6a8c0834397e03f5224495c4e/ptyprocess-0.7.0.tar.gz", hash = "sha256:5c5d0a3b48ceee0b48485e0c26037c0acd7d29765ca3fbb5cb3831d347423220", size = 70762, upload-time = "2020-12-28T15:15:30.155Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/22/a6/858897256d0deac81a172289110f31629fc4cee19b6f01283303e18c8db3/ptyprocess-0.7.0-py2.py3-none-any.whl", hash = "sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35", size = 13993, upload-time = "2020-12-28T15:15:28.35Z" }, +] + +[[package]] +name = "pure-eval" +version = "0.2.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/cd/05/0a34433a064256a578f1783a10da6df098ceaa4a57bbeaa96a6c0352786b/pure_eval-0.2.3.tar.gz", hash = "sha256:5f4e983f40564c576c7c8635ae88db5956bb2229d7e9237d03b3c0b0190eaf42", size = 19752, upload-time = "2024-07-21T12:58:21.801Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/8e/37/efad0257dc6e593a18957422533ff0f87ede7c9c6ea010a2177d738fb82f/pure_eval-0.2.3-py3-none-any.whl", hash = "sha256:1db8e35b67b3d218d818ae653e27f06c3aa420901fa7b081ca98cbedc874e0d0", size = 11842, upload-time = "2024-07-21T12:58:20.04Z" }, +] + +[[package]] +name = "pyarrow" +version = "24.0.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/91/13/13e1069b351bdc3881266e11147ffccf687505dbb0ea74036237f5d454a5/pyarrow-24.0.0.tar.gz", hash = "sha256:85fe721a14dd823aca09127acbb06c3ca723efbd436c004f16bca601b04dcc83", size = 1180261, upload-time = "2026-04-21T10:51:25.837Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/6f/d3/a1abf004482026ddc17f4503db227787fa3cfe41ec5091ff20e4fea55e57/pyarrow-24.0.0-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:02b001b3ed4723caa44f6cd1af2d5c86aa2cf9971dacc2ffa55b21237713dfba", size = 34976759, upload-time = "2026-04-21T10:48:07.258Z" }, + { url = "https://files.pythonhosted.org/packages/4f/4a/34f0a36d28a2dd32225301b79daad44e243dc1a2bb77d43b60749be255c4/pyarrow-24.0.0-cp313-cp313-macosx_12_0_x86_64.whl", hash = "sha256:04920d6a71aabd08a0417709efce97d45ea8e6fb733d9ca9ecffb13c67839f68", size = 36658471, upload-time = "2026-04-21T10:48:13.347Z" }, + { url = "https://files.pythonhosted.org/packages/1f/78/543b94712ae8bb1a6023bcc1acf1a740fbff8286747c289cd9468fced2a5/pyarrow-24.0.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:a964266397740257f16f7bb2e4f08a0c81454004beab8ff59dd531b73610e9f2", size = 45675981, upload-time = "2026-04-21T10:48:20.201Z" }, + { url = "https://files.pythonhosted.org/packages/84/9f/8fb7c222b100d314137fa40ec050de56cd8c6d957d1cfff685ce72f15b17/pyarrow-24.0.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:6f066b179d68c413374294bc1735f68475457c933258df594443bb9d88ddc2a0", size = 48859172, upload-time = "2026-04-21T10:48:27.541Z" }, + { url = "https://files.pythonhosted.org/packages/a7/d3/1ea72538e6c8b3b475ed78d1049a2c518e655761ea50fe1171fc855fcab7/pyarrow-24.0.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:1183baeb14c5f587b1ec52831e665718ce632caab84b7cd6b85fd44f96114495", size = 49385733, upload-time = "2026-04-21T10:48:34.7Z" }, + { url = "https://files.pythonhosted.org/packages/c3/be/c3d8b06a1ba35f2260f8e1f771abbee7d5e345c0937aab90675706b1690a/pyarrow-24.0.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:806f24b4085453c197a5078218d1ee08783ebbba271badd153d1ae22a3ee804f", size = 51934335, upload-time = "2026-04-21T10:48:42.099Z" }, + { url = "https://files.pythonhosted.org/packages/9c/62/89e07a1e7329d2cde3e3c6994ba0839a24977a2beda8be6005ea3d860b99/pyarrow-24.0.0-cp313-cp313-win_amd64.whl", hash = "sha256:e4505fc6583f7b05ab854934896bcac8253b04ac1171a77dfb73efef92076d91", size = 27271748, upload-time = "2026-04-21T10:49:42.532Z" }, + { url = "https://files.pythonhosted.org/packages/17/1a/cff3a59f80b5b1658549d46611b67163f65e0664431c076ad728bf9d5af4/pyarrow-24.0.0-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:1a4e45017efbf115032e4475ee876d525e0e36c742214fbe405332480ecd6275", size = 35238554, upload-time = "2026-04-21T10:48:48.526Z" }, + { url = "https://files.pythonhosted.org/packages/a8/99/cce0f42a327bfef2c420fb6078a3eb834826e5d6697bf3009fe11d2ad051/pyarrow-24.0.0-cp313-cp313t-macosx_12_0_x86_64.whl", hash = "sha256:7986f1fa71cee060ad00758bcc79d3a93bab8559bf978fab9e53472a2e25a17b", size = 36782301, upload-time = "2026-04-21T10:48:55.181Z" }, + { url = "https://files.pythonhosted.org/packages/2a/66/8e560d5ff6793ca29aca213c53eec0dd482dd46cb93b2819e5aab52e4252/pyarrow-24.0.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:d3e0b61e8efb24ed38898e5cdc5fffa9124be480008d401a1f8071500494ae42", size = 45721929, upload-time = "2026-04-21T10:49:03.676Z" }, + { url = "https://files.pythonhosted.org/packages/27/0c/a26e25505d030716e078d9f16eb74973cbf0b33b672884e9f9da1c83b871/pyarrow-24.0.0-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:55a3bc1e3df3b5567b7d27ef551b2283f0c68a5e86f1cd56abc569da4f31335b", size = 48825365, upload-time = "2026-04-21T10:49:11.714Z" }, + { url = "https://files.pythonhosted.org/packages/5f/eb/771f9ecb0c65e73fe9dccdd1717901b9594f08c4515d000c7c62df573811/pyarrow-24.0.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:641f795b361874ac9da5294f8f443dfdbee355cf2bd9e3b8d97aaac2306b9b37", size = 49451819, upload-time = "2026-04-21T10:49:21.474Z" }, + { url = "https://files.pythonhosted.org/packages/48/da/61ae89a88732f5a785646f3ec6125dbb640fa98a540eb2b9889caa561403/pyarrow-24.0.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:8adc8e6ce5fccf5dc707046ae4914fd537def529709cc0d285d37a7f9cd442ca", size = 51909252, upload-time = "2026-04-21T10:49:31.164Z" }, + { url = "https://files.pythonhosted.org/packages/cb/1a/8dd5cafab7b66573fa91c03d06d213356ad4edd71813aa75e08ce2b3a844/pyarrow-24.0.0-cp313-cp313t-win_amd64.whl", hash = "sha256:9b18371ad2f44044b81a8d23bc2d8a9b6a6226dca775e8e16cfee640473d6c5d", size = 27388127, upload-time = "2026-04-21T10:49:37.334Z" }, + { url = "https://files.pythonhosted.org/packages/ad/80/d022a34ff05d2cbedd8ccf841fc1f532ecfa9eb5ed1711b56d0e0ea71fc9/pyarrow-24.0.0-cp314-cp314-macosx_12_0_arm64.whl", hash = "sha256:1cc9057f0319e26333b357e17f3c2c022f1a83739b48a88b25bfd5fa2dc18838", size = 35007997, upload-time = "2026-04-21T10:49:48.796Z" }, + { url = "https://files.pythonhosted.org/packages/1a/ff/f01485fda6f4e5d441afb8dd5e7681e4db18826c1e271852f5d3957d6a80/pyarrow-24.0.0-cp314-cp314-macosx_12_0_x86_64.whl", hash = "sha256:e6f1278ee4785b6db21229374a1c9e54ec7c549de5d1efc9630b6207de7e170b", size = 36678720, upload-time = "2026-04-21T10:49:55.858Z" }, + { url = "https://files.pythonhosted.org/packages/9e/c2/2d2d5fea814237923f71b36495211f20b43a1576f9a4d6da7e751a64ec6f/pyarrow-24.0.0-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:adbbedc55506cbdabb830890444fb856bfb0060c46c6f8026c6c2f2cf86ae795", size = 45741852, upload-time = "2026-04-21T10:50:04.624Z" }, + { url = "https://files.pythonhosted.org/packages/8e/3a/28ba9c1c1ebdbb5f1b94dfebb46f207e52e6a554b7fe4132540fde29a3a0/pyarrow-24.0.0-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:ae8a1145af31d903fa9bb166824d7abe9b4681a000b0159c9fb99c11bc11ad26", size = 48889852, upload-time = "2026-04-21T10:50:12.293Z" }, + { url = "https://files.pythonhosted.org/packages/df/51/4a389acfd31dca009f8fb82d7f510bb4130f2b3a8e18cf00194d0687d8ac/pyarrow-24.0.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:d7027eba1df3b2069e2e8d80f644fa0918b68c46432af3d088ddd390d063ecde", size = 49445207, upload-time = "2026-04-21T10:50:20.677Z" }, + { url = "https://files.pythonhosted.org/packages/19/4b/0bab2b23d2ae901b1b9a03c0efd4b2d070256f8ce3fc43f6e58c167b2081/pyarrow-24.0.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:e56a1ffe9bf7b727432b89104cc0849c21582949dd7bdcb34f17b2001a351a76", size = 51954117, upload-time = "2026-04-21T10:50:29.14Z" }, + { url = "https://files.pythonhosted.org/packages/29/88/f4e9145da0417b3d2c12035a8492b35ff4a3dbc653e614fcfb51d9dedb38/pyarrow-24.0.0-cp314-cp314-win_amd64.whl", hash = "sha256:38be1808cdd068605b787e6ca9119b27eb275a0234e50212c3492331680c3b1e", size = 28001155, upload-time = "2026-04-21T10:51:22.337Z" }, + { url = "https://files.pythonhosted.org/packages/79/4f/46a49a63f43526da895b1a45bbb51d5baf8e4d77159f8528fc3e5490007f/pyarrow-24.0.0-cp314-cp314t-macosx_12_0_arm64.whl", hash = "sha256:418e48ce50a45a6a6c73c454677203a9c75c966cb1e92ca3370959185f197a05", size = 35250387, upload-time = "2026-04-21T10:50:35.552Z" }, + { url = "https://files.pythonhosted.org/packages/a0/da/d5e0cd5ef00796922404806d5f00325cdadc3441ce2c13fe7115f2df9a64/pyarrow-24.0.0-cp314-cp314t-macosx_12_0_x86_64.whl", hash = "sha256:2f16197705a230a78270cdd4ea8a1d57e86b2fdcbc34a1f6aebc72e65c986f9a", size = 36797102, upload-time = "2026-04-21T10:50:42.417Z" }, + { url = "https://files.pythonhosted.org/packages/34/c7/5904145b0a593a05236c882933d439b5720f0a145381179063722fbfc123/pyarrow-24.0.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:fb24ac194bfc5e86839d7dcd52092ee31e5fe6733fe11f5e3b06ef0812b20072", size = 45745118, upload-time = "2026-04-21T10:50:49.324Z" }, + { url = "https://files.pythonhosted.org/packages/13/d3/cca42fe166d1c6e4d5b80e530b7949104d10e17508a90ae202dac205ce2a/pyarrow-24.0.0-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:9700ebd9a51f5895ce75ff4ac4b3c47a7d4b42bc618be8e713e5d56bacf5f931", size = 48844765, upload-time = "2026-04-21T10:50:55.579Z" }, + { url = "https://files.pythonhosted.org/packages/b0/49/942c3b79878ba928324d1e17c274ed84581db8c0a749b24bcf4cbdf15bd3/pyarrow-24.0.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:d8ddd2768da81d3ee08cfea9b597f4abb4e8e1dc8ae7e204b608d23a0d3ab699", size = 49471890, upload-time = "2026-04-21T10:51:02.439Z" }, + { url = "https://files.pythonhosted.org/packages/76/97/ff71431000a75d84135a1ace5ca4ba11726a231a8007bbb320a4c54075d5/pyarrow-24.0.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:61a3d7eaa97a14768b542f3d284dc6400dd2470d9f080708b13cd46b6ae18136", size = 51932250, upload-time = "2026-04-21T10:51:10.576Z" }, + { url = "https://files.pythonhosted.org/packages/51/be/6f79d55816d5c22557cf27533543d5d70dfe692adfbee4b99f2760674f38/pyarrow-24.0.0-cp314-cp314t-win_amd64.whl", hash = "sha256:c91d00057f23b8d353039520dc3a6c09d8608164c692e9f59a175a42b2ae0c19", size = 28131282, upload-time = "2026-04-21T10:51:16.815Z" }, +] + +[[package]] +name = "pycparser" +version = "3.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/1b/7d/92392ff7815c21062bea51aa7b87d45576f649f16458d78b7cf94b9ab2e6/pycparser-3.0.tar.gz", hash = "sha256:600f49d217304a5902ac3c37e1281c9fe94e4d0489de643a9504c5cdfdfc6b29", size = 103492, upload-time = "2026-01-21T14:26:51.89Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/0c/c3/44f3fbbfa403ea2a7c779186dc20772604442dde72947e7d01069cbe98e3/pycparser-3.0-py3-none-any.whl", hash = "sha256:b727414169a36b7d524c1c3e31839a521725078d7b2ff038656844266160a992", size = 48172, upload-time = "2026-01-21T14:26:50.693Z" }, +] + +[[package]] +name = "pycrdt" +version = "0.12.50" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "anyio" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/5d/bd/6e049694ad7fed0baf45a62629ff2c7aa1c26e0581a4d4987e0fd39fe951/pycrdt-0.12.50.tar.gz", hash = "sha256:506d4bc00d7d566de4018dca52998ab7cf97c787363bc59440d3a3bb3336d1a0", size = 84528, upload-time = "2026-03-16T09:39:15.924Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/d1/ea/cdc543c51971c513f3b23c34d17ae672dd2fab40977b8d94344c6e8099be/pycrdt-0.12.50-cp313-cp313-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:f75c95335cacc459dbb3c4e55afbd231f8befd333c617ffad1bbe348018021de", size = 1721432, upload-time = "2026-03-16T09:38:16.738Z" }, + { url = "https://files.pythonhosted.org/packages/b6/30/cde0c58cdfb0f2e4d523443637b11b9bb5963024f5f3cd9e889b8195eab4/pycrdt-0.12.50-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3842dff93946c1b46ea8c508f7d79f07f0a8c54fe8f8e83e6cbb1f9f35a62899", size = 944575, upload-time = "2026-03-16T09:38:18.574Z" }, + { url = "https://files.pythonhosted.org/packages/df/a8/b36e98bca96b9c9b3d554ce6984128dff076a47cb350462efb122a09613e/pycrdt-0.12.50-cp313-cp313-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:fba48534acb7ba22a975c38ff531178d25a01c29d5d4ec2ecfe1c45754cde181", size = 962165, upload-time = "2026-03-16T09:38:20.112Z" }, + { url = "https://files.pythonhosted.org/packages/69/c2/38e0055416466feb9b33cfc96a95c3bd3985cdb547fdc0e556d8903e074f/pycrdt-0.12.50-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:a5ca809926c3e08965b201b277c26d319c47078ba4b22178976f0455b351155b", size = 1135011, upload-time = "2026-03-16T09:38:21.952Z" }, + { url = "https://files.pythonhosted.org/packages/fe/5f/9597d1b2fcd8f1bff78308352dc8568012e1e2c2ef44a0e5ca11cd04aa81/pycrdt-0.12.50-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:bffe6a7e6a59ea1c74a53a0ffa2fee27ff54e454cff333ef952922535c7c8ffa", size = 987535, upload-time = "2026-03-16T09:38:23.459Z" }, + { url = "https://files.pythonhosted.org/packages/ed/f8/882da205925f147610ca790304a025232c164256421655e19cd9eabfca06/pycrdt-0.12.50-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:688d8cb017a729719be8f9ecf488daba24781c05a1635f725ca257aa9a90acfd", size = 956238, upload-time = "2026-03-16T09:38:25.473Z" }, + { url = "https://files.pythonhosted.org/packages/78/91/6cf0db29eebdafe8d3a27ec0a9ece583acab0959d0de22968fcc43f51d75/pycrdt-0.12.50-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:71f9dfc24636dc9789246dae4c8db39f5b9b419c1a1f6f53b782ae22e8febbef", size = 1046621, upload-time = "2026-03-16T09:38:27.349Z" }, + { url = "https://files.pythonhosted.org/packages/b4/40/f1e79a74c12439a595f1986a403e08a35abedce5929c4f464be5f2ec8109/pycrdt-0.12.50-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a33635da609afe467e4ae644766416454535161ec7e1427294a59ed8a5e80015", size = 1121675, upload-time = "2026-03-16T09:38:28.991Z" }, + { url = "https://files.pythonhosted.org/packages/03/7f/b966b7c489e306070eef305b3f591e7ce7a34ee445cb55d1b8fd4fa6e338/pycrdt-0.12.50-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:f48c78ef3710c033d07d5de326362826eda8fa941859f06c146007d6122b3bb4", size = 1235939, upload-time = "2026-03-16T09:38:30.721Z" }, + { url = "https://files.pythonhosted.org/packages/c7/7a/0a4a74c68349ee72c3e92baad0cb9fbc6a94f2c122a228489357b8ad3507/pycrdt-0.12.50-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:c714c1582b804bd296f9b8530353bfab54386a145164e9693443e38b23392d69", size = 1222964, upload-time = "2026-03-16T09:38:32.323Z" }, + { url = "https://files.pythonhosted.org/packages/be/54/c96b470ebc5eaf355beeb8ffaf0235976e3e1fb9d4bc8a1169138c7e5063/pycrdt-0.12.50-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:d88a146090d9d6fc64687574c6014b26be1673b8b54b450fca23f115068c2852", size = 1165811, upload-time = "2026-03-16T09:38:34.05Z" }, + { url = "https://files.pythonhosted.org/packages/78/e4/070a16212142bda9cb585571066e1aa48ffcdc2ffb3540759d96dcebd141/pycrdt-0.12.50-cp313-cp313-win32.whl", hash = "sha256:a149f0f080f19b1c9a5614885e134ebbe159ee8add9fce96b81fcb3ea261df94", size = 695256, upload-time = "2026-03-16T09:38:35.705Z" }, + { url = "https://files.pythonhosted.org/packages/03/63/e0beaeabc4bb32901cff77ac9bc0edfa1b2e81a739cc5cd3990896759f94/pycrdt-0.12.50-cp313-cp313-win_amd64.whl", hash = "sha256:96db3bff011f0f85e2c95ad3337abf9553dc08d2cafb2bba6ee4b30b53a585d0", size = 748447, upload-time = "2026-03-16T09:38:37.483Z" }, + { url = "https://files.pythonhosted.org/packages/f7/5d/ae92c859ec5ee4f63d2df3702ce7a782cb054d1cef9a72d17b15a0f787f9/pycrdt-0.12.50-cp314-cp314-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:382cf259b848db979f2cc8f37c8b1c20c46de8df10142383e8502c8eb40589ba", size = 1720667, upload-time = "2026-03-16T09:38:39.222Z" }, + { url = "https://files.pythonhosted.org/packages/f9/d7/03d5a6d806eec5cc880d17d88a2f8868bd3ddf20aea988ce9238d433cfb4/pycrdt-0.12.50-cp314-cp314-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:022450e769b8ec37027504602f3dcfc4171d0d27ebe0f04c28d9eb5a3641fdff", size = 946541, upload-time = "2026-03-16T09:38:40.918Z" }, + { url = "https://files.pythonhosted.org/packages/9a/af/4700d71886afeb406b5b6d16d36dbd15fd0d3caa37af60894aca75dc8f3e/pycrdt-0.12.50-cp314-cp314-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:41c5470f1fe5426e81986664e786508935d00050f061a5eb341af596c67c0bc7", size = 960844, upload-time = "2026-03-16T09:38:42.605Z" }, + { url = "https://files.pythonhosted.org/packages/e9/95/b3640697e6e7dd6675e8fb41c95fba89d84cf435249ed0b8c310ae7eaa10/pycrdt-0.12.50-cp314-cp314-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:bccb80466c7bcaafa1591cdd44b4f4302993324dd09b16a1c4b05f6153a0a458", size = 1136447, upload-time = "2026-03-16T09:38:44.254Z" }, + { url = "https://files.pythonhosted.org/packages/f8/50/fec4bf7fdd8b82e295be28c890a856a2d80e94d4d49098e660bb2c4520bd/pycrdt-0.12.50-cp314-cp314-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b7b2061ad56d4305fce05ddfa269a662e1137997494f74f3f0633052f8beccd4", size = 986746, upload-time = "2026-03-16T09:38:45.88Z" }, + { url = "https://files.pythonhosted.org/packages/70/40/3f82b3bc35adc4ad194a2a397d0518892516e2c40663035401eca05d9bec/pycrdt-0.12.50-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0b1d6a3aa808e3996cec15c2ec7d1613c39d872627eb1953877d21720e91b002", size = 957198, upload-time = "2026-03-16T09:38:47.609Z" }, + { url = "https://files.pythonhosted.org/packages/5e/5c/dfd19e979812e455add5942857a08ce2c28547fb68824dda44d4eb83c08b/pycrdt-0.12.50-cp314-cp314-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:8ba83048dc394e8c0d0edf5fdee073eba5d566f372bd3cc24dc8f0f4c24a36d4", size = 1048567, upload-time = "2026-03-16T09:38:49.882Z" }, + { url = "https://files.pythonhosted.org/packages/ac/02/153f511fb0f0dd32d889aede169ea0eda52d62935728b685b6815425ce9d/pycrdt-0.12.50-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:8fbf1f7b6c8200193b602ed3307b526a9cf3db7acb63191632f77d071fb595ec", size = 1122383, upload-time = "2026-03-16T09:38:51.581Z" }, + { url = "https://files.pythonhosted.org/packages/ea/fa/3fcdb4502ced4b7795516acbb12997ec7aaf726187e360494182f533a1a1/pycrdt-0.12.50-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:6776ad64c8a6b270683cdecd1327289587160228401af454f570a9d971eec9a3", size = 1235274, upload-time = "2026-03-16T09:38:53.598Z" }, + { url = "https://files.pythonhosted.org/packages/69/e9/1a50a55b2b2424646e61b648a1bee42f73c1830479cb8095df428bb56b2a/pycrdt-0.12.50-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:f4218a1e568f9b33fd676adc1d3a92fdf4c1c5b6ec3c885f227db7b7fb680b3b", size = 1224841, upload-time = "2026-03-16T09:38:55.528Z" }, + { url = "https://files.pythonhosted.org/packages/a4/62/bd919a4cf7265b4b01c2365820a5423dbe9744880a83a680339a1bf34875/pycrdt-0.12.50-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:cde948e70e3e246638e5cd8b0156c714961fba41cd44374e7c5066e797e8ec3f", size = 1168590, upload-time = "2026-03-16T09:38:57.478Z" }, + { url = "https://files.pythonhosted.org/packages/d0/b3/d0b97dbaf7c60c6e3f6d5c9ae2cd8cca3655d8fa397c41c24c44d92dc8d2/pycrdt-0.12.50-cp314-cp314-win32.whl", hash = "sha256:1d42d7f29c1e8459cd80aefd37595e8c7062817f48c59c5e5568401527718d19", size = 694709, upload-time = "2026-03-16T09:38:59.68Z" }, + { url = "https://files.pythonhosted.org/packages/72/fc/acdb8c238f9f4a6c2757b7c2cfdb39aa3c779ac465e0b6c6862c564e6350/pycrdt-0.12.50-cp314-cp314-win_amd64.whl", hash = "sha256:a4d294295120e33fef32d51e1a7a92eab444d20c07d5bde55a5a75afe58a5d41", size = 747251, upload-time = "2026-03-16T09:39:01.435Z" }, +] + +[[package]] +name = "pycrdt-store" +version = "0.1.3" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "anyio" }, + { name = "pycrdt" }, + { name = "sqlite-anyio" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/35/61/dfecafdc0c23f56d5bacc67de620b77a68f86085df21a8007628d6045248/pycrdt_store-0.1.3.tar.gz", hash = "sha256:12a0e263b2c07eb18bbe7203c828b88ba953cb93094ad37d22aeb6c619df2ef0", size = 14847, upload-time = "2025-12-11T13:29:11.454Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/b9/2d/85a1b3d6e65048c0553e0d06e21b235610ff4db0ea94cbae1bd34de385d7/pycrdt_store-0.1.3-py3-none-any.whl", hash = "sha256:2e74afc856c162706d178d23d57fd3706accbe79d849e73dd413646a7025afba", size = 11948, upload-time = "2025-12-11T13:29:10.522Z" }, +] + +[[package]] +name = "pycrdt-websocket" +version = "0.16.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "anyio" }, + { name = "pycrdt" }, + { name = "pycrdt-store" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/4f/91/a412af8792af22e7e67a7424e7b6c64baada4897777fed885a2cb825155d/pycrdt_websocket-0.16.0.tar.gz", hash = "sha256:89d4d830f41028c55cc9877635f73f94f49131ca73ffac7353d0be421150d0fd", size = 23152, upload-time = "2025-06-11T07:15:54.298Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f4/b7/a1dd4d149fa6279f321bd7dacab66ac31e728fbae175a7d75cf8211b1f30/pycrdt_websocket-0.16.0-py3-none-any.whl", hash = "sha256:4b9ffe47c40867b7e637922680e93471fd801b6e8d6c9f6aa688fd2a17351141", size = 14568, upload-time = "2025-06-11T07:15:52.364Z" }, +] + +[[package]] +name = "pydantic" +version = "2.13.3" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "annotated-types" }, + { name = "pydantic-core" }, + { name = "typing-extensions" }, + { name = "typing-inspection" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/d9/e4/40d09941a2cebcb20609b86a559817d5b9291c49dd6f8c87e5feffbe703a/pydantic-2.13.3.tar.gz", hash = "sha256:af09e9d1d09f4e7fe37145c1f577e1d61ceb9a41924bf0094a36506285d0a84d", size = 844068, upload-time = "2026-04-20T14:46:43.632Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f3/0a/fd7d723f8f8153418fb40cf9c940e82004fce7e987026b08a68a36dd3fe7/pydantic-2.13.3-py3-none-any.whl", hash = "sha256:6db14ac8dfc9a1e57f87ea2c0de670c251240f43cb0c30a5130e9720dc612927", size = 471981, upload-time = "2026-04-20T14:46:41.402Z" }, +] + +[[package]] +name = "pydantic-core" +version = "2.46.3" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "typing-extensions" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/2a/ef/f7abb56c49382a246fd2ce9c799691e3c3e7175ec74b14d99e798bcddb1a/pydantic_core-2.46.3.tar.gz", hash = "sha256:41c178f65b8c29807239d47e6050262eb6bf84eb695e41101e62e38df4a5bc2c", size = 471412, upload-time = "2026-04-20T14:40:56.672Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/9b/3c/9b5e8eb9821936d065439c3b0fb1490ffa64163bfe7e1595985a47896073/pydantic_core-2.46.3-cp313-cp313-macosx_10_12_x86_64.whl", hash = "sha256:12bc98de041458b80c86c56b24df1d23832f3e166cbaff011f25d187f5c62c37", size = 2102109, upload-time = "2026-04-20T14:41:24.219Z" }, + { url = "https://files.pythonhosted.org/packages/91/97/1c41d1f5a19f241d8069f1e249853bcce378cdb76eec8ab636d7bc426280/pydantic_core-2.46.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:85348b8f89d2c3508b65b16c3c33a4da22b8215138d8b996912bb1532868885f", size = 1951820, upload-time = "2026-04-20T14:42:14.236Z" }, + { url = "https://files.pythonhosted.org/packages/30/b4/d03a7ae14571bc2b6b3c7b122441154720619afe9a336fa3a95434df5e2f/pydantic_core-2.46.3-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1105677a6df914b1fb71a81b96c8cce7726857e1717d86001f29be06a25ee6f8", size = 1977785, upload-time = "2026-04-20T14:42:31.648Z" }, + { url = "https://files.pythonhosted.org/packages/ae/0c/4086f808834b59e3c8f1aa26df8f4b6d998cdcf354a143d18ef41529d1fe/pydantic_core-2.46.3-cp313-cp313-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:87082cd65669a33adeba5470769e9704c7cf026cc30afb9cc77fd865578ebaad", size = 2062761, upload-time = "2026-04-20T14:40:37.093Z" }, + { url = "https://files.pythonhosted.org/packages/fa/71/a649be5a5064c2df0db06e0a512c2281134ed2fcc981f52a657936a7527c/pydantic_core-2.46.3-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:60e5f66e12c4f5212d08522963380eaaeac5ebd795826cfd19b2dfb0c7a52b9c", size = 2232989, upload-time = "2026-04-20T14:42:59.254Z" }, + { url = "https://files.pythonhosted.org/packages/a2/84/7756e75763e810b3a710f4724441d1ecc5883b94aacb07ca71c5fb5cfb69/pydantic_core-2.46.3-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b6cdf19bf84128d5e7c37e8a73a0c5c10d51103a650ac585d42dd6ae233f2b7f", size = 2303975, upload-time = "2026-04-20T14:41:32.287Z" }, + { url = "https://files.pythonhosted.org/packages/6c/35/68a762e0c1e31f35fa0dac733cbd9f5b118042853698de9509c8e5bf128b/pydantic_core-2.46.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:031bb17f4885a43773c8c763089499f242aee2ea85cf17154168775dccdecf35", size = 2095325, upload-time = "2026-04-20T14:42:47.685Z" }, + { url = "https://files.pythonhosted.org/packages/77/bf/1bf8c9a8e91836c926eae5e3e51dce009bf495a60ca56060689d3df3f340/pydantic_core-2.46.3-cp313-cp313-manylinux_2_31_riscv64.whl", hash = "sha256:bcf2a8b2982a6673693eae7348ef3d8cf3979c1d63b54fca7c397a635cc68687", size = 2133368, upload-time = "2026-04-20T14:41:22.766Z" }, + { url = "https://files.pythonhosted.org/packages/e5/50/87d818d6bab915984995157ceb2380f5aac4e563dddbed6b56f0ed057aba/pydantic_core-2.46.3-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:28e8cf2f52d72ced402a137145923a762cbb5081e48b34312f7a0c8f55928ec3", size = 2173908, upload-time = "2026-04-20T14:42:52.044Z" }, + { url = "https://files.pythonhosted.org/packages/91/88/a311fb306d0bd6185db41fa14ae888fb81d0baf648a761ae760d30819d33/pydantic_core-2.46.3-cp313-cp313-musllinux_1_1_aarch64.whl", hash = "sha256:17eaface65d9fc5abb940003020309c1bf7a211f5f608d7870297c367e6f9022", size = 2186422, upload-time = "2026-04-20T14:43:29.55Z" }, + { url = "https://files.pythonhosted.org/packages/8f/79/28fd0d81508525ab2054fef7c77a638c8b5b0afcbbaeee493cf7c3fef7e1/pydantic_core-2.46.3-cp313-cp313-musllinux_1_1_armv7l.whl", hash = "sha256:93fd339f23408a07e98950a89644f92c54d8729719a40b30c0a30bb9ebc55d23", size = 2332709, upload-time = "2026-04-20T14:42:16.134Z" }, + { url = "https://files.pythonhosted.org/packages/b3/21/795bf5fe5c0f379308b8ef19c50dedab2e7711dbc8d0c2acf08f1c7daa05/pydantic_core-2.46.3-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:23cbdb3aaa74dfe0837975dbf69b469753bbde8eacace524519ffdb6b6e89eb7", size = 2372428, upload-time = "2026-04-20T14:41:10.974Z" }, + { url = "https://files.pythonhosted.org/packages/45/b3/ed14c659cbe7605e3ef063077680a64680aec81eb1a04763a05190d49b7f/pydantic_core-2.46.3-cp313-cp313-win32.whl", hash = "sha256:610eda2e3838f401105e6326ca304f5da1e15393ae25dacae5c5c63f2c275b13", size = 1965601, upload-time = "2026-04-20T14:41:42.128Z" }, + { url = "https://files.pythonhosted.org/packages/ef/bb/adb70d9a762ddd002d723fbf1bd492244d37da41e3af7b74ad212609027e/pydantic_core-2.46.3-cp313-cp313-win_amd64.whl", hash = "sha256:68cc7866ed863db34351294187f9b729964c371ba33e31c26f478471c52e1ed0", size = 2071517, upload-time = "2026-04-20T14:43:36.096Z" }, + { url = "https://files.pythonhosted.org/packages/52/eb/66faefabebfe68bd7788339c9c9127231e680b11906368c67ce112fdb47f/pydantic_core-2.46.3-cp313-cp313-win_arm64.whl", hash = "sha256:f64b5537ac62b231572879cd08ec05600308636a5d63bcbdb15063a466977bec", size = 2035802, upload-time = "2026-04-20T14:43:38.507Z" }, + { url = "https://files.pythonhosted.org/packages/7f/db/a7bcb4940183fda36022cd18ba8dd12f2dff40740ec7b58ce7457befa416/pydantic_core-2.46.3-cp314-cp314-macosx_10_12_x86_64.whl", hash = "sha256:afa3aa644f74e290cdede48a7b0bee37d1c35e71b05105f6b340d484af536d9b", size = 2097614, upload-time = "2026-04-20T14:44:38.374Z" }, + { url = "https://files.pythonhosted.org/packages/24/35/e4066358a22e3e99519db370494c7528f5a2aa1367370e80e27e20283543/pydantic_core-2.46.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:ced3310e51aa425f7f77da8bbbb5212616655bedbe82c70944320bc1dbe5e018", size = 1951896, upload-time = "2026-04-20T14:40:53.996Z" }, + { url = "https://files.pythonhosted.org/packages/87/92/37cf4049d1636996e4b888c05a501f40a43ff218983a551d57f9d5e14f0d/pydantic_core-2.46.3-cp314-cp314-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e29908922ce9da1a30b4da490bd1d3d82c01dcfdf864d2a74aacee674d0bfa34", size = 1979314, upload-time = "2026-04-20T14:41:49.446Z" }, + { url = "https://files.pythonhosted.org/packages/d8/36/9ff4d676dfbdfb2d591cf43f3d90ded01e15b1404fd101180ed2d62a2fd3/pydantic_core-2.46.3-cp314-cp314-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:0c9ff69140423eea8ed2d5477df3ba037f671f5e897d206d921bc9fdc39613e7", size = 2056133, upload-time = "2026-04-20T14:42:23.574Z" }, + { url = "https://files.pythonhosted.org/packages/bc/f0/405b442a4d7ba855b06eec8b2bf9c617d43b8432d099dfdc7bf999293495/pydantic_core-2.46.3-cp314-cp314-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b675ab0a0d5b1c8fdb81195dc5bcefea3f3c240871cdd7ff9a2de8aa50772eb2", size = 2228726, upload-time = "2026-04-20T14:44:22.816Z" }, + { url = "https://files.pythonhosted.org/packages/e7/f8/65cd92dd5a0bd89ba277a98ecbfaf6fc36bbd3300973c7a4b826d6ab1391/pydantic_core-2.46.3-cp314-cp314-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:0087084960f209a9a4af50ecd1fb063d9ad3658c07bb81a7a53f452dacbfb2ba", size = 2301214, upload-time = "2026-04-20T14:44:48.792Z" }, + { url = "https://files.pythonhosted.org/packages/fd/86/ef96a4c6e79e7a2d0410826a68fbc0eccc0fd44aa733be199d5fcac3bb87/pydantic_core-2.46.3-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ed42e6cc8e1b0e2b9b96e2276bad70ae625d10d6d524aed0c93de974ae029f9f", size = 2099927, upload-time = "2026-04-20T14:41:40.196Z" }, + { url = "https://files.pythonhosted.org/packages/6d/53/269caf30e0096e0a8a8f929d1982a27b3879872cca2d917d17c2f9fdf4fe/pydantic_core-2.46.3-cp314-cp314-manylinux_2_31_riscv64.whl", hash = "sha256:f1771ce258afb3e4201e67d154edbbae712a76a6081079fe247c2f53c6322c22", size = 2128789, upload-time = "2026-04-20T14:41:15.868Z" }, + { url = "https://files.pythonhosted.org/packages/00/b0/1a6d9b6a587e118482910c244a1c5acf4d192604174132efd12bf0ac486f/pydantic_core-2.46.3-cp314-cp314-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:a7610b6a5242a6c736d8ad47fd5fff87fcfe8f833b281b1c409c3d6835d9227f", size = 2173815, upload-time = "2026-04-20T14:44:25.152Z" }, + { url = "https://files.pythonhosted.org/packages/87/56/e7e00d4041a7e62b5a40815590114db3b535bf3ca0bf4dca9f16cef25246/pydantic_core-2.46.3-cp314-cp314-musllinux_1_1_aarch64.whl", hash = "sha256:ff5e7783bcc5476e1db448bf268f11cb257b1c276d3e89f00b5727be86dd0127", size = 2181608, upload-time = "2026-04-20T14:41:28.933Z" }, + { url = "https://files.pythonhosted.org/packages/e8/22/4bd23c3d41f7c185d60808a1de83c76cf5aeabf792f6c636a55c3b1ec7f9/pydantic_core-2.46.3-cp314-cp314-musllinux_1_1_armv7l.whl", hash = "sha256:9d2e32edcc143bc01e95300671915d9ca052d4f745aa0a49c48d4803f8a85f2c", size = 2326968, upload-time = "2026-04-20T14:42:03.962Z" }, + { url = "https://files.pythonhosted.org/packages/24/ac/66cd45129e3915e5ade3b292cb3bc7fd537f58f8f8dbdaba6170f7cabb74/pydantic_core-2.46.3-cp314-cp314-musllinux_1_1_x86_64.whl", hash = "sha256:6e42d83d1c6b87fa56b521479cff237e626a292f3b31b6345c15a99121b454c1", size = 2369842, upload-time = "2026-04-20T14:41:35.52Z" }, + { url = "https://files.pythonhosted.org/packages/a2/51/dd4248abb84113615473aa20d5545b7c4cd73c8644003b5259686f93996c/pydantic_core-2.46.3-cp314-cp314-win32.whl", hash = "sha256:07bc6d2a28c3adb4f7c6ae46aa4f2d2929af127f587ed44057af50bf1ce0f505", size = 1959661, upload-time = "2026-04-20T14:41:00.042Z" }, + { url = "https://files.pythonhosted.org/packages/20/eb/59980e5f1ae54a3b86372bd9f0fa373ea2d402e8cdcd3459334430f91e91/pydantic_core-2.46.3-cp314-cp314-win_amd64.whl", hash = "sha256:8940562319bc621da30714617e6a7eaa6b98c84e8c685bcdc02d7ed5e7c7c44e", size = 2071686, upload-time = "2026-04-20T14:43:16.471Z" }, + { url = "https://files.pythonhosted.org/packages/8c/db/1cf77e5247047dfee34bc01fa9bca134854f528c8eb053e144298893d370/pydantic_core-2.46.3-cp314-cp314-win_arm64.whl", hash = "sha256:5dcbbcf4d22210ced8f837c96db941bdb078f419543472aca5d9a0bb7cddc7df", size = 2026907, upload-time = "2026-04-20T14:43:31.732Z" }, + { url = "https://files.pythonhosted.org/packages/57/c0/b3df9f6a543276eadba0a48487b082ca1f201745329d97dbfa287034a230/pydantic_core-2.46.3-cp314-cp314t-macosx_10_12_x86_64.whl", hash = "sha256:d0fe3dce1e836e418f912c1ad91c73357d03e556a4d286f441bf34fed2dbeecf", size = 2095047, upload-time = "2026-04-20T14:42:37.982Z" }, + { url = "https://files.pythonhosted.org/packages/66/57/886a938073b97556c168fd99e1a7305bb363cd30a6d2c76086bf0587b32a/pydantic_core-2.46.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:9ce92e58abc722dac1bf835a6798a60b294e48eb0e625ec9fd994b932ac5feee", size = 1934329, upload-time = "2026-04-20T14:43:49.655Z" }, + { url = "https://files.pythonhosted.org/packages/0b/7c/b42eaa5c34b13b07ecb51da21761297a9b8eb43044c864a035999998f328/pydantic_core-2.46.3-cp314-cp314t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a03e6467f0f5ab796a486146d1b887b2dc5e5f9b3288898c1b1c3ad974e53e4a", size = 1974847, upload-time = "2026-04-20T14:42:10.737Z" }, + { url = "https://files.pythonhosted.org/packages/e6/9b/92b42db6543e7de4f99ae977101a2967b63122d4b6cf7773812da2d7d5b5/pydantic_core-2.46.3-cp314-cp314t-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:2798b6ba041b9d70acfb9071a2ea13c8456dd1e6a5555798e41ba7b0790e329c", size = 2041742, upload-time = "2026-04-20T14:40:44.262Z" }, + { url = "https://files.pythonhosted.org/packages/0f/19/46fbe1efabb5aa2834b43b9454e70f9a83ad9c338c1291e48bdc4fecf167/pydantic_core-2.46.3-cp314-cp314t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9be3e221bdc6d69abf294dcf7aff6af19c31a5cdcc8f0aa3b14be29df4bd03b1", size = 2236235, upload-time = "2026-04-20T14:41:27.307Z" }, + { url = "https://files.pythonhosted.org/packages/77/da/b3f95bc009ad60ec53120f5d16c6faa8cabdbe8a20d83849a1f2b8728148/pydantic_core-2.46.3-cp314-cp314t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:f13936129ce841f2a5ddf6f126fea3c43cd128807b5a59588c37cf10178c2e64", size = 2282633, upload-time = "2026-04-20T14:44:33.271Z" }, + { url = "https://files.pythonhosted.org/packages/cc/6e/401336117722e28f32fb8220df676769d28ebdf08f2f4469646d404c43a3/pydantic_core-2.46.3-cp314-cp314t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:28b5f2ef03416facccb1c6ef744c69793175fd27e44ef15669201601cf423acb", size = 2109679, upload-time = "2026-04-20T14:44:41.065Z" }, + { url = "https://files.pythonhosted.org/packages/fc/53/b289f9bc8756a32fe718c46f55afaeaf8d489ee18d1a1e7be1db73f42cc4/pydantic_core-2.46.3-cp314-cp314t-manylinux_2_31_riscv64.whl", hash = "sha256:830d1247d77ad23852314f069e9d7ddafeec5f684baf9d7e7065ed46a049c4e6", size = 2108342, upload-time = "2026-04-20T14:42:50.144Z" }, + { url = "https://files.pythonhosted.org/packages/10/5b/8292fc7c1f9111f1b2b7c1b0dcf1179edcd014fc3ea4517499f50b829d71/pydantic_core-2.46.3-cp314-cp314t-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:d0793c90c1a3c74966e7975eaef3ed30ebdff3260a0f815a62a22adc17e4c01c", size = 2157208, upload-time = "2026-04-20T14:42:08.133Z" }, + { url = "https://files.pythonhosted.org/packages/2b/9e/f80044e9ec07580f057a89fc131f78dda7a58751ddf52bbe05eaf31db50f/pydantic_core-2.46.3-cp314-cp314t-musllinux_1_1_aarch64.whl", hash = "sha256:d2d0aead851b66f5245ec0c4fb2612ef457f8bbafefdf65a2bf9d6bac6140f47", size = 2167237, upload-time = "2026-04-20T14:42:25.412Z" }, + { url = "https://files.pythonhosted.org/packages/f8/84/6781a1b037f3b96be9227edbd1101f6d3946746056231bf4ac48cdff1a8d/pydantic_core-2.46.3-cp314-cp314t-musllinux_1_1_armv7l.whl", hash = "sha256:2f40e4246676beb31c5ce77c38a55ca4e465c6b38d11ea1bd935420568e0b1ab", size = 2312540, upload-time = "2026-04-20T14:40:40.313Z" }, + { url = "https://files.pythonhosted.org/packages/3e/db/19c0839feeb728e7df03255581f198dfdf1c2aeb1e174a8420b63c5252e5/pydantic_core-2.46.3-cp314-cp314t-musllinux_1_1_x86_64.whl", hash = "sha256:cf489cf8986c543939aeee17a09c04d6ffb43bfef8ca16fcbcc5cfdcbed24dba", size = 2369556, upload-time = "2026-04-20T14:41:09.427Z" }, + { url = "https://files.pythonhosted.org/packages/e0/15/3228774cb7cd45f5f721ddf1b2242747f4eb834d0c491f0c02d606f09fed/pydantic_core-2.46.3-cp314-cp314t-win32.whl", hash = "sha256:ffe0883b56cfc05798bf994164d2b2ff03efe2d22022a2bb080f3b626176dd56", size = 1949756, upload-time = "2026-04-20T14:41:25.717Z" }, + { url = "https://files.pythonhosted.org/packages/b8/2a/c79cf53fd91e5a87e30d481809f52f9a60dd221e39de66455cf04deaad37/pydantic_core-2.46.3-cp314-cp314t-win_amd64.whl", hash = "sha256:706d9d0ce9cf4593d07270d8e9f53b161f90c57d315aeec4fb4fd7a8b10240d8", size = 2051305, upload-time = "2026-04-20T14:43:18.627Z" }, + { url = "https://files.pythonhosted.org/packages/0b/db/d8182a7f1d9343a032265aae186eb063fe26ca4c40f256b21e8da4498e89/pydantic_core-2.46.3-cp314-cp314t-win_arm64.whl", hash = "sha256:77706aeb41df6a76568434701e0917da10692da28cb69d5fb6919ce5fdb07374", size = 2026310, upload-time = "2026-04-20T14:41:01.778Z" }, +] + +[[package]] +name = "pydantic-settings" +version = "2.14.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "pydantic" }, + { name = "python-dotenv" }, + { name = "typing-inspection" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/42/98/c8345dccdc31de4228c039a98f6467a941e39558da41c1744fbe29fa5666/pydantic_settings-2.14.0.tar.gz", hash = "sha256:24285fd4b0e0c06507dd9fdfd331ee23794305352aaec8fc4eb92d4047aeb67d", size = 235709, upload-time = "2026-04-20T13:37:40.293Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/01/dd/bebff3040138f00ae8a102d426b27349b9a49acc310fcae7f92112d867e3/pydantic_settings-2.14.0-py3-none-any.whl", hash = "sha256:fc8d5d692eb7092e43c8647c1c35a3ecd00e040fcf02ed86f4cb5458ca62182e", size = 60940, upload-time = "2026-04-20T13:37:38.586Z" }, +] + +[[package]] +name = "pygments" +version = "2.20.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/c3/b2/bc9c9196916376152d655522fdcebac55e66de6603a76a02bca1b6414f6c/pygments-2.20.0.tar.gz", hash = "sha256:6757cd03768053ff99f3039c1a36d6c0aa0b263438fcab17520b30a303a82b5f", size = 4955991, upload-time = "2026-03-29T13:29:33.898Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f4/7e/a72dd26f3b0f4f2bf1dd8923c85f7ceb43172af56d63c7383eb62b332364/pygments-2.20.0-py3-none-any.whl", hash = "sha256:81a9e26dd42fd28a23a2d169d86d7ac03b46e2f8b59ed4698fb4785f946d0176", size = 1231151, upload-time = "2026-03-29T13:29:30.038Z" }, +] + +[[package]] +name = "pyjwt" +version = "2.12.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/c2/27/a3b6e5bf6ff856d2509292e95c8f57f0df7017cf5394921fc4e4ef40308a/pyjwt-2.12.1.tar.gz", hash = "sha256:c74a7a2adf861c04d002db713dd85f84beb242228e671280bf709d765b03672b", size = 102564, upload-time = "2026-03-13T19:27:37.25Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e5/7a/8dd906bd22e79e47397a61742927f6747fe93242ef86645ee9092e610244/pyjwt-2.12.1-py3-none-any.whl", hash = "sha256:28ca37c070cad8ba8cd9790cd940535d40274d22f80ab87f3ac6a713e6e8454c", size = 29726, upload-time = "2026-03-13T19:27:35.677Z" }, +] + +[package.optional-dependencies] +crypto = [ + { name = "cryptography" }, +] + +[[package]] +name = "pyparsing" +version = "3.3.2" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/f3/91/9c6ee907786a473bf81c5f53cf703ba0957b23ab84c264080fb5a450416f/pyparsing-3.3.2.tar.gz", hash = "sha256:c777f4d763f140633dcb6d8a3eda953bf7a214dc4eff598413c070bcdc117cbc", size = 6851574, upload-time = "2026-01-21T03:57:59.36Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/10/bd/c038d7cc38edc1aa5bf91ab8068b63d4308c66c4c8bb3cbba7dfbc049f9c/pyparsing-3.3.2-py3-none-any.whl", hash = "sha256:850ba148bd908d7e2411587e247a1e4f0327839c40e2e5e6d05a007ecc69911d", size = 122781, upload-time = "2026-01-21T03:57:55.912Z" }, +] + +[[package]] +name = "python-dateutil" +version = "2.9.0.post0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "six" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/66/c0/0c8b6ad9f17a802ee498c46e004a0eb49bc148f2fd230864601a86dcf6db/python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3", size = 342432, upload-time = "2024-03-01T18:36:20.211Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427", size = 229892, upload-time = "2024-03-01T18:36:18.57Z" }, +] + +[[package]] +name = "python-dotenv" +version = "1.2.2" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/82/ed/0301aeeac3e5353ef3d94b6ec08bbcabd04a72018415dcb29e588514bba8/python_dotenv-1.2.2.tar.gz", hash = "sha256:2c371a91fbd7ba082c2c1dc1f8bf89ca22564a087c2c287cd9b662adde799cf3", size = 50135, upload-time = "2026-03-01T16:00:26.196Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/0b/d7/1959b9648791274998a9c3526f6d0ec8fd2233e4d4acce81bbae76b44b2a/python_dotenv-1.2.2-py3-none-any.whl", hash = "sha256:1d8214789a24de455a8b8bd8ae6fe3c6b69a5e3d64aa8a8e5d68e694bbcb285a", size = 22101, upload-time = "2026-03-01T16:00:25.09Z" }, +] + +[[package]] +name = "python-json-logger" +version = "4.1.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/f7/ff/3cc9165fd44106973cd7ac9facb674a65ed853494592541d339bdc9a30eb/python_json_logger-4.1.0.tar.gz", hash = "sha256:b396b9e3ed782b09ff9d6e4f1683d46c83ad0d35d2e407c09a9ebbf038f88195", size = 17573, upload-time = "2026-03-29T04:39:56.805Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/27/be/0631a861af4d1c875f096c07d34e9a63639560a717130e7a87cbc82b7e3f/python_json_logger-4.1.0-py3-none-any.whl", hash = "sha256:132994765cf75bf44554be9aa49b06ef2345d23661a96720262716438141b6b2", size = 15021, upload-time = "2026-03-29T04:39:55.266Z" }, +] + +[[package]] +name = "python-multipart" +version = "0.0.27" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/69/9b/f23807317a113dc36e74e75eb265a02dd1a4d9082abc3c1064acd22997c4/python_multipart-0.0.27.tar.gz", hash = "sha256:9870a6a8c5a20a5bf4f07c017bd1489006ff8836cff097b6933355ee2b49b602", size = 44043, upload-time = "2026-04-27T10:51:26.649Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/99/78/4126abcbdbd3c559d43e0db7f7b9173fc6befe45d39a2856cc0b8ec2a5a6/python_multipart-0.0.27-py3-none-any.whl", hash = "sha256:6fccfad17a27334bd0193681b369f476eda3409f17381a2d65aa7df3f7275645", size = 29254, upload-time = "2026-04-27T10:51:24.997Z" }, +] + +[[package]] +name = "pywin32" +version = "311" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/a5/be/3fd5de0979fcb3994bfee0d65ed8ca9506a8a1260651b86174f6a86f52b3/pywin32-311-cp313-cp313-win32.whl", hash = "sha256:f95ba5a847cba10dd8c4d8fefa9f2a6cf283b8b88ed6178fa8a6c1ab16054d0d", size = 8705700, upload-time = "2025-07-14T20:13:26.471Z" }, + { url = "https://files.pythonhosted.org/packages/e3/28/e0a1909523c6890208295a29e05c2adb2126364e289826c0a8bc7297bd5c/pywin32-311-cp313-cp313-win_amd64.whl", hash = "sha256:718a38f7e5b058e76aee1c56ddd06908116d35147e133427e59a3983f703a20d", size = 9494700, upload-time = "2025-07-14T20:13:28.243Z" }, + { url = "https://files.pythonhosted.org/packages/04/bf/90339ac0f55726dce7d794e6d79a18a91265bdf3aa70b6b9ca52f35e022a/pywin32-311-cp313-cp313-win_arm64.whl", hash = "sha256:7b4075d959648406202d92a2310cb990fea19b535c7f4a78d3f5e10b926eeb8a", size = 8709318, upload-time = "2025-07-14T20:13:30.348Z" }, + { url = "https://files.pythonhosted.org/packages/c9/31/097f2e132c4f16d99a22bfb777e0fd88bd8e1c634304e102f313af69ace5/pywin32-311-cp314-cp314-win32.whl", hash = "sha256:b7a2c10b93f8986666d0c803ee19b5990885872a7de910fc460f9b0c2fbf92ee", size = 8840714, upload-time = "2025-07-14T20:13:32.449Z" }, + { url = "https://files.pythonhosted.org/packages/90/4b/07c77d8ba0e01349358082713400435347df8426208171ce297da32c313d/pywin32-311-cp314-cp314-win_amd64.whl", hash = "sha256:3aca44c046bd2ed8c90de9cb8427f581c479e594e99b5c0bb19b29c10fd6cb87", size = 9656800, upload-time = "2025-07-14T20:13:34.312Z" }, + { url = "https://files.pythonhosted.org/packages/c0/d2/21af5c535501a7233e734b8af901574572da66fcc254cb35d0609c9080dd/pywin32-311-cp314-cp314-win_arm64.whl", hash = "sha256:a508e2d9025764a8270f93111a970e1d0fbfc33f4153b388bb649b7eec4f9b42", size = 8932540, upload-time = "2025-07-14T20:13:36.379Z" }, +] + +[[package]] +name = "pywinpty" +version = "3.0.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/f7/54/37c7370ba91f579235049dc26cd2c5e657d2a943e01820844ffc81f32176/pywinpty-3.0.3.tar.gz", hash = "sha256:523441dc34d231fb361b4b00f8c99d3f16de02f5005fd544a0183112bcc22412", size = 31309, upload-time = "2026-02-04T21:51:09.524Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e5/cb/58d6ed3fd429c96a90ef01ac9a617af10a6d41469219c25e7dc162abbb71/pywinpty-3.0.3-cp313-cp313-win_amd64.whl", hash = "sha256:9c91dbb026050c77bdcef964e63a4f10f01a639113c4d3658332614544c467ab", size = 2112686, upload-time = "2026-02-04T21:52:03.035Z" }, + { url = "https://files.pythonhosted.org/packages/fd/50/724ed5c38c504d4e58a88a072776a1e880d970789deaeb2b9f7bd9a5141a/pywinpty-3.0.3-cp313-cp313-win_arm64.whl", hash = "sha256:fe1f7911805127c94cf51f89ab14096c6f91ffdcacf993d2da6082b2142a2523", size = 234591, upload-time = "2026-02-04T21:52:29.821Z" }, + { url = "https://files.pythonhosted.org/packages/f7/ad/90a110538696b12b39fd8758a06d70ded899308198ad2305ac68e361126e/pywinpty-3.0.3-cp313-cp313t-win_amd64.whl", hash = "sha256:3f07a6cf1c1d470d284e614733c3d0f726d2c85e78508ea10a403140c3c0c18a", size = 2112360, upload-time = "2026-02-04T21:55:33.397Z" }, + { url = "https://files.pythonhosted.org/packages/44/0f/7ffa221757a220402bc79fda44044c3f2cc57338d878ab7d622add6f4581/pywinpty-3.0.3-cp313-cp313t-win_arm64.whl", hash = "sha256:15c7c0b6f8e9d87aabbaff76468dabf6e6121332c40fc1d83548d02a9d6a3759", size = 233107, upload-time = "2026-02-04T21:51:45.455Z" }, + { url = "https://files.pythonhosted.org/packages/28/88/2ff917caff61e55f38bcdb27de06ee30597881b2cae44fbba7627be015c4/pywinpty-3.0.3-cp314-cp314-win_amd64.whl", hash = "sha256:d4b6b7b0fe0cdcd02e956bd57cfe9f4e5a06514eecf3b5ae174da4f951b58be9", size = 2113282, upload-time = "2026-02-04T21:52:08.188Z" }, + { url = "https://files.pythonhosted.org/packages/63/32/40a775343ace542cc43ece3f1d1fce454021521ecac41c4c4573081c2336/pywinpty-3.0.3-cp314-cp314-win_arm64.whl", hash = "sha256:34789d685fc0d547ce0c8a65e5a70e56f77d732fa6e03c8f74fefb8cbb252019", size = 234207, upload-time = "2026-02-04T21:51:58.687Z" }, + { url = "https://files.pythonhosted.org/packages/8d/54/5d5e52f4cb75028104ca6faf36c10f9692389b1986d34471663b4ebebd6d/pywinpty-3.0.3-cp314-cp314t-win_amd64.whl", hash = "sha256:0c37e224a47a971d1a6e08649a1714dac4f63c11920780977829ed5c8cadead1", size = 2112910, upload-time = "2026-02-04T21:52:30.976Z" }, + { url = "https://files.pythonhosted.org/packages/0a/44/dcd184824e21d4620b06c7db9fbb15c3ad0a0f1fa2e6de79969fb82647ec/pywinpty-3.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:c4e9c3dff7d86ba81937438d5819f19f385a39d8f592d4e8af67148ceb4f6ab5", size = 233425, upload-time = "2026-02-04T21:51:56.754Z" }, +] + +[[package]] +name = "pyyaml" +version = "6.0.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/05/8e/961c0007c59b8dd7729d542c61a4d537767a59645b82a0b521206e1e25c2/pyyaml-6.0.3.tar.gz", hash = "sha256:d76623373421df22fb4cf8817020cbb7ef15c725b9d5e45f17e189bfc384190f", size = 130960, upload-time = "2025-09-25T21:33:16.546Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/d1/11/0fd08f8192109f7169db964b5707a2f1e8b745d4e239b784a5a1dd80d1db/pyyaml-6.0.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:8da9669d359f02c0b91ccc01cac4a67f16afec0dac22c2ad09f46bee0697eba8", size = 181669, upload-time = "2025-09-25T21:32:23.673Z" }, + { url = "https://files.pythonhosted.org/packages/b1/16/95309993f1d3748cd644e02e38b75d50cbc0d9561d21f390a76242ce073f/pyyaml-6.0.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:2283a07e2c21a2aa78d9c4442724ec1eb15f5e42a723b99cb3d822d48f5f7ad1", size = 173252, upload-time = "2025-09-25T21:32:25.149Z" }, + { url = "https://files.pythonhosted.org/packages/50/31/b20f376d3f810b9b2371e72ef5adb33879b25edb7a6d072cb7ca0c486398/pyyaml-6.0.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ee2922902c45ae8ccada2c5b501ab86c36525b883eff4255313a253a3160861c", size = 767081, upload-time = "2025-09-25T21:32:26.575Z" }, + { url = "https://files.pythonhosted.org/packages/49/1e/a55ca81e949270d5d4432fbbd19dfea5321eda7c41a849d443dc92fd1ff7/pyyaml-6.0.3-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a33284e20b78bd4a18c8c2282d549d10bc8408a2a7ff57653c0cf0b9be0afce5", size = 841159, upload-time = "2025-09-25T21:32:27.727Z" }, + { url = "https://files.pythonhosted.org/packages/74/27/e5b8f34d02d9995b80abcef563ea1f8b56d20134d8f4e5e81733b1feceb2/pyyaml-6.0.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0f29edc409a6392443abf94b9cf89ce99889a1dd5376d94316ae5145dfedd5d6", size = 801626, upload-time = "2025-09-25T21:32:28.878Z" }, + { url = "https://files.pythonhosted.org/packages/f9/11/ba845c23988798f40e52ba45f34849aa8a1f2d4af4b798588010792ebad6/pyyaml-6.0.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:f7057c9a337546edc7973c0d3ba84ddcdf0daa14533c2065749c9075001090e6", size = 753613, upload-time = "2025-09-25T21:32:30.178Z" }, + { url = "https://files.pythonhosted.org/packages/3d/e0/7966e1a7bfc0a45bf0a7fb6b98ea03fc9b8d84fa7f2229e9659680b69ee3/pyyaml-6.0.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:eda16858a3cab07b80edaf74336ece1f986ba330fdb8ee0d6c0d68fe82bc96be", size = 794115, upload-time = "2025-09-25T21:32:31.353Z" }, + { url = "https://files.pythonhosted.org/packages/de/94/980b50a6531b3019e45ddeada0626d45fa85cbe22300844a7983285bed3b/pyyaml-6.0.3-cp313-cp313-win32.whl", hash = "sha256:d0eae10f8159e8fdad514efdc92d74fd8d682c933a6dd088030f3834bc8e6b26", size = 137427, upload-time = "2025-09-25T21:32:32.58Z" }, + { url = "https://files.pythonhosted.org/packages/97/c9/39d5b874e8b28845e4ec2202b5da735d0199dbe5b8fb85f91398814a9a46/pyyaml-6.0.3-cp313-cp313-win_amd64.whl", hash = "sha256:79005a0d97d5ddabfeeea4cf676af11e647e41d81c9a7722a193022accdb6b7c", size = 154090, upload-time = "2025-09-25T21:32:33.659Z" }, + { url = "https://files.pythonhosted.org/packages/73/e8/2bdf3ca2090f68bb3d75b44da7bbc71843b19c9f2b9cb9b0f4ab7a5a4329/pyyaml-6.0.3-cp313-cp313-win_arm64.whl", hash = "sha256:5498cd1645aa724a7c71c8f378eb29ebe23da2fc0d7a08071d89469bf1d2defb", size = 140246, upload-time = "2025-09-25T21:32:34.663Z" }, + { url = "https://files.pythonhosted.org/packages/9d/8c/f4bd7f6465179953d3ac9bc44ac1a8a3e6122cf8ada906b4f96c60172d43/pyyaml-6.0.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:8d1fab6bb153a416f9aeb4b8763bc0f22a5586065f86f7664fc23339fc1c1fac", size = 181814, upload-time = "2025-09-25T21:32:35.712Z" }, + { url = "https://files.pythonhosted.org/packages/bd/9c/4d95bb87eb2063d20db7b60faa3840c1b18025517ae857371c4dd55a6b3a/pyyaml-6.0.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:34d5fcd24b8445fadc33f9cf348c1047101756fd760b4dacb5c3e99755703310", size = 173809, upload-time = "2025-09-25T21:32:36.789Z" }, + { url = "https://files.pythonhosted.org/packages/92/b5/47e807c2623074914e29dabd16cbbdd4bf5e9b2db9f8090fa64411fc5382/pyyaml-6.0.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:501a031947e3a9025ed4405a168e6ef5ae3126c59f90ce0cd6f2bfc477be31b7", size = 766454, upload-time = "2025-09-25T21:32:37.966Z" }, + { url = "https://files.pythonhosted.org/packages/02/9e/e5e9b168be58564121efb3de6859c452fccde0ab093d8438905899a3a483/pyyaml-6.0.3-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:b3bc83488de33889877a0f2543ade9f70c67d66d9ebb4ac959502e12de895788", size = 836355, upload-time = "2025-09-25T21:32:39.178Z" }, + { url = "https://files.pythonhosted.org/packages/88/f9/16491d7ed2a919954993e48aa941b200f38040928474c9e85ea9e64222c3/pyyaml-6.0.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c458b6d084f9b935061bc36216e8a69a7e293a2f1e68bf956dcd9e6cbcd143f5", size = 794175, upload-time = "2025-09-25T21:32:40.865Z" }, + { url = "https://files.pythonhosted.org/packages/dd/3f/5989debef34dc6397317802b527dbbafb2b4760878a53d4166579111411e/pyyaml-6.0.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:7c6610def4f163542a622a73fb39f534f8c101d690126992300bf3207eab9764", size = 755228, upload-time = "2025-09-25T21:32:42.084Z" }, + { url = "https://files.pythonhosted.org/packages/d7/ce/af88a49043cd2e265be63d083fc75b27b6ed062f5f9fd6cdc223ad62f03e/pyyaml-6.0.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:5190d403f121660ce8d1d2c1bb2ef1bd05b5f68533fc5c2ea899bd15f4399b35", size = 789194, upload-time = "2025-09-25T21:32:43.362Z" }, + { url = "https://files.pythonhosted.org/packages/23/20/bb6982b26a40bb43951265ba29d4c246ef0ff59c9fdcdf0ed04e0687de4d/pyyaml-6.0.3-cp314-cp314-win_amd64.whl", hash = "sha256:4a2e8cebe2ff6ab7d1050ecd59c25d4c8bd7e6f400f5f82b96557ac0abafd0ac", size = 156429, upload-time = "2025-09-25T21:32:57.844Z" }, + { url = "https://files.pythonhosted.org/packages/f4/f4/a4541072bb9422c8a883ab55255f918fa378ecf083f5b85e87fc2b4eda1b/pyyaml-6.0.3-cp314-cp314-win_arm64.whl", hash = "sha256:93dda82c9c22deb0a405ea4dc5f2d0cda384168e466364dec6255b293923b2f3", size = 143912, upload-time = "2025-09-25T21:32:59.247Z" }, + { url = "https://files.pythonhosted.org/packages/7c/f9/07dd09ae774e4616edf6cda684ee78f97777bdd15847253637a6f052a62f/pyyaml-6.0.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:02893d100e99e03eda1c8fd5c441d8c60103fd175728e23e431db1b589cf5ab3", size = 189108, upload-time = "2025-09-25T21:32:44.377Z" }, + { url = "https://files.pythonhosted.org/packages/4e/78/8d08c9fb7ce09ad8c38ad533c1191cf27f7ae1effe5bb9400a46d9437fcf/pyyaml-6.0.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:c1ff362665ae507275af2853520967820d9124984e0f7466736aea23d8611fba", size = 183641, upload-time = "2025-09-25T21:32:45.407Z" }, + { url = "https://files.pythonhosted.org/packages/7b/5b/3babb19104a46945cf816d047db2788bcaf8c94527a805610b0289a01c6b/pyyaml-6.0.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6adc77889b628398debc7b65c073bcb99c4a0237b248cacaf3fe8a557563ef6c", size = 831901, upload-time = "2025-09-25T21:32:48.83Z" }, + { url = "https://files.pythonhosted.org/packages/8b/cc/dff0684d8dc44da4d22a13f35f073d558c268780ce3c6ba1b87055bb0b87/pyyaml-6.0.3-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a80cb027f6b349846a3bf6d73b5e95e782175e52f22108cfa17876aaeff93702", size = 861132, upload-time = "2025-09-25T21:32:50.149Z" }, + { url = "https://files.pythonhosted.org/packages/b1/5e/f77dc6b9036943e285ba76b49e118d9ea929885becb0a29ba8a7c75e29fe/pyyaml-6.0.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:00c4bdeba853cc34e7dd471f16b4114f4162dc03e6b7afcc2128711f0eca823c", size = 839261, upload-time = "2025-09-25T21:32:51.808Z" }, + { url = "https://files.pythonhosted.org/packages/ce/88/a9db1376aa2a228197c58b37302f284b5617f56a5d959fd1763fb1675ce6/pyyaml-6.0.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:66e1674c3ef6f541c35191caae2d429b967b99e02040f5ba928632d9a7f0f065", size = 805272, upload-time = "2025-09-25T21:32:52.941Z" }, + { url = "https://files.pythonhosted.org/packages/da/92/1446574745d74df0c92e6aa4a7b0b3130706a4142b2d1a5869f2eaa423c6/pyyaml-6.0.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:16249ee61e95f858e83976573de0f5b2893b3677ba71c9dd36b9cf8be9ac6d65", size = 829923, upload-time = "2025-09-25T21:32:54.537Z" }, + { url = "https://files.pythonhosted.org/packages/f0/7a/1c7270340330e575b92f397352af856a8c06f230aa3e76f86b39d01b416a/pyyaml-6.0.3-cp314-cp314t-win_amd64.whl", hash = "sha256:4ad1906908f2f5ae4e5a8ddfce73c320c2a1429ec52eafd27138b7f1cbe341c9", size = 174062, upload-time = "2025-09-25T21:32:55.767Z" }, + { url = "https://files.pythonhosted.org/packages/f1/12/de94a39c2ef588c7e6455cfbe7343d3b2dc9d6b6b2f40c4c6565744c873d/pyyaml-6.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:ebc55a14a21cb14062aa4162f906cd962b28e2e9ea38f9b4391244cd8de4ae0b", size = 149341, upload-time = "2025-09-25T21:32:56.828Z" }, +] + +[[package]] +name = "pyzmq" +version = "27.1.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "cffi", marker = "implementation_name == 'pypy'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/04/0b/3c9baedbdf613ecaa7aa07027780b8867f57b6293b6ee50de316c9f3222b/pyzmq-27.1.0.tar.gz", hash = "sha256:ac0765e3d44455adb6ddbf4417dcce460fc40a05978c08efdf2948072f6db540", size = 281750, upload-time = "2025-09-08T23:10:18.157Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/92/e7/038aab64a946d535901103da16b953c8c9cc9c961dadcbf3609ed6428d23/pyzmq-27.1.0-cp312-abi3-macosx_10_15_universal2.whl", hash = "sha256:452631b640340c928fa343801b0d07eb0c3789a5ffa843f6e1a9cee0ba4eb4fc", size = 1306279, upload-time = "2025-09-08T23:08:03.807Z" }, + { url = "https://files.pythonhosted.org/packages/e8/5e/c3c49fdd0f535ef45eefcc16934648e9e59dace4a37ee88fc53f6cd8e641/pyzmq-27.1.0-cp312-abi3-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:1c179799b118e554b66da67d88ed66cd37a169f1f23b5d9f0a231b4e8d44a113", size = 895645, upload-time = "2025-09-08T23:08:05.301Z" }, + { url = "https://files.pythonhosted.org/packages/f8/e5/b0b2504cb4e903a74dcf1ebae157f9e20ebb6ea76095f6cfffea28c42ecd/pyzmq-27.1.0-cp312-abi3-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3837439b7f99e60312f0c926a6ad437b067356dc2bc2ec96eb395fd0fe804233", size = 652574, upload-time = "2025-09-08T23:08:06.828Z" }, + { url = "https://files.pythonhosted.org/packages/f8/9b/c108cdb55560eaf253f0cbdb61b29971e9fb34d9c3499b0e96e4e60ed8a5/pyzmq-27.1.0-cp312-abi3-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:43ad9a73e3da1fab5b0e7e13402f0b2fb934ae1c876c51d0afff0e7c052eca31", size = 840995, upload-time = "2025-09-08T23:08:08.396Z" }, + { url = "https://files.pythonhosted.org/packages/c2/bb/b79798ca177b9eb0825b4c9998c6af8cd2a7f15a6a1a4272c1d1a21d382f/pyzmq-27.1.0-cp312-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:0de3028d69d4cdc475bfe47a6128eb38d8bc0e8f4d69646adfbcd840facbac28", size = 1642070, upload-time = "2025-09-08T23:08:09.989Z" }, + { url = "https://files.pythonhosted.org/packages/9c/80/2df2e7977c4ede24c79ae39dcef3899bfc5f34d1ca7a5b24f182c9b7a9ca/pyzmq-27.1.0-cp312-abi3-musllinux_1_2_i686.whl", hash = "sha256:cf44a7763aea9298c0aa7dbf859f87ed7012de8bda0f3977b6fb1d96745df856", size = 2021121, upload-time = "2025-09-08T23:08:11.907Z" }, + { url = "https://files.pythonhosted.org/packages/46/bd/2d45ad24f5f5ae7e8d01525eb76786fa7557136555cac7d929880519e33a/pyzmq-27.1.0-cp312-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:f30f395a9e6fbca195400ce833c731e7b64c3919aa481af4d88c3759e0cb7496", size = 1878550, upload-time = "2025-09-08T23:08:13.513Z" }, + { url = "https://files.pythonhosted.org/packages/e6/2f/104c0a3c778d7c2ab8190e9db4f62f0b6957b53c9d87db77c284b69f33ea/pyzmq-27.1.0-cp312-abi3-win32.whl", hash = "sha256:250e5436a4ba13885494412b3da5d518cd0d3a278a1ae640e113c073a5f88edd", size = 559184, upload-time = "2025-09-08T23:08:15.163Z" }, + { url = "https://files.pythonhosted.org/packages/fc/7f/a21b20d577e4100c6a41795842028235998a643b1ad406a6d4163ea8f53e/pyzmq-27.1.0-cp312-abi3-win_amd64.whl", hash = "sha256:9ce490cf1d2ca2ad84733aa1d69ce6855372cb5ce9223802450c9b2a7cba0ccf", size = 619480, upload-time = "2025-09-08T23:08:17.192Z" }, + { url = "https://files.pythonhosted.org/packages/78/c2/c012beae5f76b72f007a9e91ee9401cb88c51d0f83c6257a03e785c81cc2/pyzmq-27.1.0-cp312-abi3-win_arm64.whl", hash = "sha256:75a2f36223f0d535a0c919e23615fc85a1e23b71f40c7eb43d7b1dedb4d8f15f", size = 552993, upload-time = "2025-09-08T23:08:18.926Z" }, + { url = "https://files.pythonhosted.org/packages/60/cb/84a13459c51da6cec1b7b1dc1a47e6db6da50b77ad7fd9c145842750a011/pyzmq-27.1.0-cp313-cp313-android_24_arm64_v8a.whl", hash = "sha256:93ad4b0855a664229559e45c8d23797ceac03183c7b6f5b4428152a6b06684a5", size = 1122436, upload-time = "2025-09-08T23:08:20.801Z" }, + { url = "https://files.pythonhosted.org/packages/dc/b6/94414759a69a26c3dd674570a81813c46a078767d931a6c70ad29fc585cb/pyzmq-27.1.0-cp313-cp313-android_24_x86_64.whl", hash = "sha256:fbb4f2400bfda24f12f009cba62ad5734148569ff4949b1b6ec3b519444342e6", size = 1156301, upload-time = "2025-09-08T23:08:22.47Z" }, + { url = "https://files.pythonhosted.org/packages/a5/ad/15906493fd40c316377fd8a8f6b1f93104f97a752667763c9b9c1b71d42d/pyzmq-27.1.0-cp313-cp313t-macosx_10_15_universal2.whl", hash = "sha256:e343d067f7b151cfe4eb3bb796a7752c9d369eed007b91231e817071d2c2fec7", size = 1341197, upload-time = "2025-09-08T23:08:24.286Z" }, + { url = "https://files.pythonhosted.org/packages/14/1d/d343f3ce13db53a54cb8946594e567410b2125394dafcc0268d8dda027e0/pyzmq-27.1.0-cp313-cp313t-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:08363b2011dec81c354d694bdecaef4770e0ae96b9afea70b3f47b973655cc05", size = 897275, upload-time = "2025-09-08T23:08:26.063Z" }, + { url = "https://files.pythonhosted.org/packages/69/2d/d83dd6d7ca929a2fc67d2c3005415cdf322af7751d773524809f9e585129/pyzmq-27.1.0-cp313-cp313t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d54530c8c8b5b8ddb3318f481297441af102517602b569146185fa10b63f4fa9", size = 660469, upload-time = "2025-09-08T23:08:27.623Z" }, + { url = "https://files.pythonhosted.org/packages/3e/cd/9822a7af117f4bc0f1952dbe9ef8358eb50a24928efd5edf54210b850259/pyzmq-27.1.0-cp313-cp313t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6f3afa12c392f0a44a2414056d730eebc33ec0926aae92b5ad5cf26ebb6cc128", size = 847961, upload-time = "2025-09-08T23:08:29.672Z" }, + { url = "https://files.pythonhosted.org/packages/9a/12/f003e824a19ed73be15542f172fd0ec4ad0b60cf37436652c93b9df7c585/pyzmq-27.1.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:c65047adafe573ff023b3187bb93faa583151627bc9c51fc4fb2c561ed689d39", size = 1650282, upload-time = "2025-09-08T23:08:31.349Z" }, + { url = "https://files.pythonhosted.org/packages/d5/4a/e82d788ed58e9a23995cee70dbc20c9aded3d13a92d30d57ec2291f1e8a3/pyzmq-27.1.0-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:90e6e9441c946a8b0a667356f7078d96411391a3b8f80980315455574177ec97", size = 2024468, upload-time = "2025-09-08T23:08:33.543Z" }, + { url = "https://files.pythonhosted.org/packages/d9/94/2da0a60841f757481e402b34bf4c8bf57fa54a5466b965de791b1e6f747d/pyzmq-27.1.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:add071b2d25f84e8189aaf0882d39a285b42fa3853016ebab234a5e78c7a43db", size = 1885394, upload-time = "2025-09-08T23:08:35.51Z" }, + { url = "https://files.pythonhosted.org/packages/4f/6f/55c10e2e49ad52d080dc24e37adb215e5b0d64990b57598abc2e3f01725b/pyzmq-27.1.0-cp313-cp313t-win32.whl", hash = "sha256:7ccc0700cfdf7bd487bea8d850ec38f204478681ea02a582a8da8171b7f90a1c", size = 574964, upload-time = "2025-09-08T23:08:37.178Z" }, + { url = "https://files.pythonhosted.org/packages/87/4d/2534970ba63dd7c522d8ca80fb92777f362c0f321900667c615e2067cb29/pyzmq-27.1.0-cp313-cp313t-win_amd64.whl", hash = "sha256:8085a9fba668216b9b4323be338ee5437a235fe275b9d1610e422ccc279733e2", size = 641029, upload-time = "2025-09-08T23:08:40.595Z" }, + { url = "https://files.pythonhosted.org/packages/f6/fa/f8aea7a28b0641f31d40dea42d7ef003fded31e184ef47db696bc74cd610/pyzmq-27.1.0-cp313-cp313t-win_arm64.whl", hash = "sha256:6bb54ca21bcfe361e445256c15eedf083f153811c37be87e0514934d6913061e", size = 561541, upload-time = "2025-09-08T23:08:42.668Z" }, + { url = "https://files.pythonhosted.org/packages/87/45/19efbb3000956e82d0331bafca5d9ac19ea2857722fa2caacefb6042f39d/pyzmq-27.1.0-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:ce980af330231615756acd5154f29813d553ea555485ae712c491cd483df6b7a", size = 1341197, upload-time = "2025-09-08T23:08:44.973Z" }, + { url = "https://files.pythonhosted.org/packages/48/43/d72ccdbf0d73d1343936296665826350cb1e825f92f2db9db3e61c2162a2/pyzmq-27.1.0-cp314-cp314t-manylinux2014_i686.manylinux_2_17_i686.whl", hash = "sha256:1779be8c549e54a1c38f805e56d2a2e5c009d26de10921d7d51cfd1c8d4632ea", size = 897175, upload-time = "2025-09-08T23:08:46.601Z" }, + { url = "https://files.pythonhosted.org/packages/2f/2e/a483f73a10b65a9ef0161e817321d39a770b2acf8bcf3004a28d90d14a94/pyzmq-27.1.0-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7200bb0f03345515df50d99d3db206a0a6bee1955fbb8c453c76f5bf0e08fb96", size = 660427, upload-time = "2025-09-08T23:08:48.187Z" }, + { url = "https://files.pythonhosted.org/packages/f5/d2/5f36552c2d3e5685abe60dfa56f91169f7a2d99bbaf67c5271022ab40863/pyzmq-27.1.0-cp314-cp314t-manylinux_2_26_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:01c0e07d558b06a60773744ea6251f769cd79a41a97d11b8bf4ab8f034b0424d", size = 847929, upload-time = "2025-09-08T23:08:49.76Z" }, + { url = "https://files.pythonhosted.org/packages/c4/2a/404b331f2b7bf3198e9945f75c4c521f0c6a3a23b51f7a4a401b94a13833/pyzmq-27.1.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:80d834abee71f65253c91540445d37c4c561e293ba6e741b992f20a105d69146", size = 1650193, upload-time = "2025-09-08T23:08:51.7Z" }, + { url = "https://files.pythonhosted.org/packages/1c/0b/f4107e33f62a5acf60e3ded67ed33d79b4ce18de432625ce2fc5093d6388/pyzmq-27.1.0-cp314-cp314t-musllinux_1_2_i686.whl", hash = "sha256:544b4e3b7198dde4a62b8ff6685e9802a9a1ebf47e77478a5eb88eca2a82f2fd", size = 2024388, upload-time = "2025-09-08T23:08:53.393Z" }, + { url = "https://files.pythonhosted.org/packages/0d/01/add31fe76512642fd6e40e3a3bd21f4b47e242c8ba33efb6809e37076d9b/pyzmq-27.1.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:cedc4c68178e59a4046f97eca31b148ddcf51e88677de1ef4e78cf06c5376c9a", size = 1885316, upload-time = "2025-09-08T23:08:55.702Z" }, + { url = "https://files.pythonhosted.org/packages/c4/59/a5f38970f9bf07cee96128de79590bb354917914a9be11272cfc7ff26af0/pyzmq-27.1.0-cp314-cp314t-win32.whl", hash = "sha256:1f0b2a577fd770aa6f053211a55d1c47901f4d537389a034c690291485e5fe92", size = 587472, upload-time = "2025-09-08T23:08:58.18Z" }, + { url = "https://files.pythonhosted.org/packages/70/d8/78b1bad170f93fcf5e3536e70e8fadac55030002275c9a29e8f5719185de/pyzmq-27.1.0-cp314-cp314t-win_amd64.whl", hash = "sha256:19c9468ae0437f8074af379e986c5d3d7d7bfe033506af442e8c879732bedbe0", size = 661401, upload-time = "2025-09-08T23:08:59.802Z" }, + { url = "https://files.pythonhosted.org/packages/81/d6/4bfbb40c9a0b42fc53c7cf442f6385db70b40f74a783130c5d0a5aa62228/pyzmq-27.1.0-cp314-cp314t-win_arm64.whl", hash = "sha256:dc5dbf68a7857b59473f7df42650c621d7e8923fb03fa74a526890f4d33cc4d7", size = 575170, upload-time = "2025-09-08T23:09:01.418Z" }, +] + +[[package]] +name = "referencing" +version = "0.37.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "attrs" }, + { name = "rpds-py" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/22/f5/df4e9027acead3ecc63e50fe1e36aca1523e1719559c499951bb4b53188f/referencing-0.37.0.tar.gz", hash = "sha256:44aefc3142c5b842538163acb373e24cce6632bd54bdb01b21ad5863489f50d8", size = 78036, upload-time = "2025-10-13T15:30:48.871Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/2c/58/ca301544e1fa93ed4f80d724bf5b194f6e4b945841c5bfd555878eea9fcb/referencing-0.37.0-py3-none-any.whl", hash = "sha256:381329a9f99628c9069361716891d34ad94af76e461dcb0335825aecc7692231", size = 26766, upload-time = "2025-10-13T15:30:47.625Z" }, +] + +[[package]] +name = "regex" +version = "2026.4.4" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/cb/0e/3a246dbf05666918bd3664d9d787f84a9108f6f43cc953a077e4a7dfdb7e/regex-2026.4.4.tar.gz", hash = "sha256:e08270659717f6973523ce3afbafa53515c4dc5dcad637dc215b6fd50f689423", size = 416000, upload-time = "2026-04-03T20:56:28.155Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/9d/83/c4373bc5f31f2cf4b66f9b7c31005bd87fe66f0dce17701f7db4ee79ee29/regex-2026.4.4-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:62f5519042c101762509b1d717b45a69c0139d60414b3c604b81328c01bd1943", size = 490273, upload-time = "2026-04-03T20:54:11.202Z" }, + { url = "https://files.pythonhosted.org/packages/46/f8/fe62afbcc3cf4ad4ac9adeaafd98aa747869ae12d3e8e2ac293d0593c435/regex-2026.4.4-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:3790ba9fb5dd76715a7afe34dbe603ba03f8820764b1dc929dd08106214ed031", size = 291954, upload-time = "2026-04-03T20:54:13.412Z" }, + { url = "https://files.pythonhosted.org/packages/5a/92/4712b9fe6a33d232eeb1c189484b80c6c4b8422b90e766e1195d6e758207/regex-2026.4.4-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:8fae3c6e795d7678963f2170152b0d892cf6aee9ee8afc8c45e6be38d5107fe7", size = 289487, upload-time = "2026-04-03T20:54:15.824Z" }, + { url = "https://files.pythonhosted.org/packages/88/2c/f83b93f85e01168f1070f045a42d4c937b69fdb8dd7ae82d307253f7e36e/regex-2026.4.4-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:298c3ec2d53225b3bf91142eb9691025bab610e0c0c51592dde149db679b3d17", size = 796646, upload-time = "2026-04-03T20:54:18.229Z" }, + { url = "https://files.pythonhosted.org/packages/df/55/61a2e17bf0c4dc57e11caf8dd11771280d8aaa361785f9e3bc40d653f4a7/regex-2026.4.4-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:e9638791082eaf5b3ac112c587518ee78e083a11c4b28012d8fe2a0f536dfb17", size = 865904, upload-time = "2026-04-03T20:54:20.019Z" }, + { url = "https://files.pythonhosted.org/packages/45/32/1ac8ed1b5a346b5993a3d256abe0a0f03b0b73c8cc88d928537368ac65b6/regex-2026.4.4-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:ae3e764bd4c5ff55035dc82a8d49acceb42a5298edf6eb2fc4d328ee5dd7afae", size = 912304, upload-time = "2026-04-03T20:54:22.403Z" }, + { url = "https://files.pythonhosted.org/packages/26/47/2ee5c613ab546f0eddebf9905d23e07beb933416b1246c2d8791d01979b4/regex-2026.4.4-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ffa81f81b80047ba89a3c69ae6a0f78d06f4a42ce5126b0eb2a0a10ad44e0b2e", size = 801126, upload-time = "2026-04-03T20:54:24.308Z" }, + { url = "https://files.pythonhosted.org/packages/75/cd/41dacd129ca9fd20bd7d02f83e0fad83e034ac8a084ec369c90f55ef37e2/regex-2026.4.4-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:f56ebf9d70305307a707911b88469213630aba821e77de7d603f9d2f0730687d", size = 776772, upload-time = "2026-04-03T20:54:26.319Z" }, + { url = "https://files.pythonhosted.org/packages/89/6d/5af0b588174cb5f46041fa7dd64d3fd5cd2fe51f18766703d1edc387f324/regex-2026.4.4-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:773d1dfd652bbffb09336abf890bfd64785c7463716bf766d0eb3bc19c8b7f27", size = 785228, upload-time = "2026-04-03T20:54:28.387Z" }, + { url = "https://files.pythonhosted.org/packages/b7/3b/f5a72b7045bd59575fc33bf1345f156fcfd5a8484aea6ad84b12c5a82114/regex-2026.4.4-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:d51d20befd5275d092cdffba57ded05f3c436317ee56466c8928ac32d960edaf", size = 860032, upload-time = "2026-04-03T20:54:30.641Z" }, + { url = "https://files.pythonhosted.org/packages/39/a4/72a317003d6fcd7a573584a85f59f525dfe8f67e355ca74eb6b53d66a5e2/regex-2026.4.4-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:0a51cdb3c1e9161154f976cb2bef9894bc063ac82f31b733087ffb8e880137d0", size = 765714, upload-time = "2026-04-03T20:54:32.789Z" }, + { url = "https://files.pythonhosted.org/packages/25/1e/5672e16f34dbbcb2560cc7e6a2fbb26dfa8b270711e730101da4423d3973/regex-2026.4.4-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:ae5266a82596114e41fb5302140e9630204c1b5f325c770bec654b95dd54b0aa", size = 852078, upload-time = "2026-04-03T20:54:34.546Z" }, + { url = "https://files.pythonhosted.org/packages/f7/0d/c813f0af7c6cc7ed7b9558bac2e5120b60ad0fa48f813e4d4bd55446f214/regex-2026.4.4-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:c882cd92ec68585e9c1cf36c447ec846c0d94edd706fe59e0c198e65822fd23b", size = 789181, upload-time = "2026-04-03T20:54:36.642Z" }, + { url = "https://files.pythonhosted.org/packages/ea/6d/a344608d1adbd2a95090ddd906cec09a11be0e6517e878d02a5123e0917f/regex-2026.4.4-cp313-cp313-win32.whl", hash = "sha256:05568c4fbf3cb4fa9e28e3af198c40d3237cf6041608a9022285fe567ec3ad62", size = 266690, upload-time = "2026-04-03T20:54:38.343Z" }, + { url = "https://files.pythonhosted.org/packages/31/07/54049f89b46235ca6f45cd6c88668a7050e77d4a15555e47dd40fde75263/regex-2026.4.4-cp313-cp313-win_amd64.whl", hash = "sha256:3384df51ed52db0bea967e21458ab0a414f67cdddfd94401688274e55147bb81", size = 277733, upload-time = "2026-04-03T20:54:40.11Z" }, + { url = "https://files.pythonhosted.org/packages/0e/21/61366a8e20f4d43fb597708cac7f0e2baadb491ecc9549b4980b2be27d16/regex-2026.4.4-cp313-cp313-win_arm64.whl", hash = "sha256:acd38177bd2c8e69a411d6521760806042e244d0ef94e2dd03ecdaa8a3c99427", size = 270565, upload-time = "2026-04-03T20:54:41.883Z" }, + { url = "https://files.pythonhosted.org/packages/f1/1e/3a2b9672433bef02f5d39aa1143ca2c08f311c1d041c464a42be9ae648dc/regex-2026.4.4-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:f94a11a9d05afcfcfa640e096319720a19cc0c9f7768e1a61fceee6a3afc6c7c", size = 494126, upload-time = "2026-04-03T20:54:43.602Z" }, + { url = "https://files.pythonhosted.org/packages/4e/4b/c132a4f4fe18ad3340d89fcb56235132b69559136036b845be3c073142ed/regex-2026.4.4-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:36bcb9d6d1307ab629edc553775baada2aefa5c50ccc0215fbfd2afcfff43141", size = 293882, upload-time = "2026-04-03T20:54:45.41Z" }, + { url = "https://files.pythonhosted.org/packages/f4/5f/eaa38092ce7a023656280f2341dbbd4ad5f05d780a70abba7bb4f4bea54c/regex-2026.4.4-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:261c015b3e2ed0919157046d768774ecde57f03d8fa4ba78d29793447f70e717", size = 292334, upload-time = "2026-04-03T20:54:47.051Z" }, + { url = "https://files.pythonhosted.org/packages/5f/f6/dd38146af1392dac33db7074ab331cec23cced3759167735c42c5460a243/regex-2026.4.4-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c228cf65b4a54583763645dcd73819b3b381ca8b4bb1b349dee1c135f4112c07", size = 811691, upload-time = "2026-04-03T20:54:49.074Z" }, + { url = "https://files.pythonhosted.org/packages/7a/f0/dc54c2e69f5eeec50601054998ec3690d5344277e782bd717e49867c1d29/regex-2026.4.4-cp313-cp313t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:dd2630faeb6876fb0c287f664d93ddce4d50cd46c6e88e60378c05c9047e08ca", size = 871227, upload-time = "2026-04-03T20:54:51.035Z" }, + { url = "https://files.pythonhosted.org/packages/a1/af/cb16bd5dc61621e27df919a4449bbb7e5a1034c34d307e0a706e9cc0f3e3/regex-2026.4.4-cp313-cp313t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:6a50ab11b7779b849472337191f3a043e27e17f71555f98d0092fa6d73364520", size = 917435, upload-time = "2026-04-03T20:54:52.994Z" }, + { url = "https://files.pythonhosted.org/packages/5c/71/8b260897f22996b666edd9402861668f45a2ca259f665ac029e6104a2d7d/regex-2026.4.4-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0734f63afe785138549fbe822a8cfeaccd1bae814c5057cc0ed5b9f2de4fc883", size = 816358, upload-time = "2026-04-03T20:54:54.884Z" }, + { url = "https://files.pythonhosted.org/packages/1c/60/775f7f72a510ef238254906c2f3d737fc80b16ca85f07d20e318d2eea894/regex-2026.4.4-cp313-cp313t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:c4ee50606cb1967db7e523224e05f32089101945f859928e65657a2cbb3d278b", size = 785549, upload-time = "2026-04-03T20:54:57.01Z" }, + { url = "https://files.pythonhosted.org/packages/58/42/34d289b3627c03cf381e44da534a0021664188fa49ba41513da0b4ec6776/regex-2026.4.4-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:6c1818f37be3ca02dcb76d63f2c7aaba4b0dc171b579796c6fbe00148dfec6b1", size = 801364, upload-time = "2026-04-03T20:54:58.981Z" }, + { url = "https://files.pythonhosted.org/packages/fc/20/f6ecf319b382a8f1ab529e898b222c3f30600fcede7834733c26279e7465/regex-2026.4.4-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:f5bfc2741d150d0be3e4a0401a5c22b06e60acb9aa4daa46d9e79a6dcd0f135b", size = 866221, upload-time = "2026-04-03T20:55:00.88Z" }, + { url = "https://files.pythonhosted.org/packages/92/6a/9f16d3609d549bd96d7a0b2aee1625d7512ba6a03efc01652149ef88e74d/regex-2026.4.4-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:504ffa8a03609a087cad81277a629b6ce884b51a24bd388a7980ad61748618ff", size = 772530, upload-time = "2026-04-03T20:55:03.213Z" }, + { url = "https://files.pythonhosted.org/packages/fa/f6/aa9768bc96a4c361ac96419fbaf2dcdc33970bb813df3ba9b09d5d7b6d96/regex-2026.4.4-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:70aadc6ff12e4b444586e57fc30771f86253f9f0045b29016b9605b4be5f7dfb", size = 856989, upload-time = "2026-04-03T20:55:05.087Z" }, + { url = "https://files.pythonhosted.org/packages/4d/b4/c671db3556be2473ae3e4bb7a297c518d281452871501221251ea4ecba57/regex-2026.4.4-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:f4f83781191007b6ef43b03debc35435f10cad9b96e16d147efe84a1d48bdde4", size = 803241, upload-time = "2026-04-03T20:55:07.162Z" }, + { url = "https://files.pythonhosted.org/packages/2a/5c/83e3b1d89fa4f6e5a1bc97b4abd4a9a97b3c1ac7854164f694f5f0ba98a0/regex-2026.4.4-cp313-cp313t-win32.whl", hash = "sha256:e014a797de43d1847df957c0a2a8e861d1c17547ee08467d1db2c370b7568baa", size = 269921, upload-time = "2026-04-03T20:55:09.62Z" }, + { url = "https://files.pythonhosted.org/packages/28/07/077c387121f42cdb4d92b1301133c0d93b5709d096d1669ab847dda9fe2e/regex-2026.4.4-cp313-cp313t-win_amd64.whl", hash = "sha256:b15b88b0d52b179712632832c1d6e58e5774f93717849a41096880442da41ab0", size = 281240, upload-time = "2026-04-03T20:55:11.521Z" }, + { url = "https://files.pythonhosted.org/packages/9d/22/ead4a4abc7c59a4d882662aa292ca02c8b617f30b6e163bc1728879e9353/regex-2026.4.4-cp313-cp313t-win_arm64.whl", hash = "sha256:586b89cdadf7d67bf86ae3342a4dcd2b8d70a832d90c18a0ae955105caf34dbe", size = 272440, upload-time = "2026-04-03T20:55:13.365Z" }, + { url = "https://files.pythonhosted.org/packages/f0/f5/ed97c2dc47b5fbd4b73c0d7d75f9ebc8eca139f2bbef476bba35f28c0a77/regex-2026.4.4-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:2da82d643fa698e5e5210e54af90181603d5853cf469f5eedf9bfc8f59b4b8c7", size = 490343, upload-time = "2026-04-03T20:55:15.241Z" }, + { url = "https://files.pythonhosted.org/packages/80/e9/de4828a7385ec166d673a5790ad06ac48cdaa98bc0960108dd4b9cc1aef7/regex-2026.4.4-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:54a1189ad9d9357760557c91103d5e421f0a2dabe68a5cdf9103d0dcf4e00752", size = 291909, upload-time = "2026-04-03T20:55:17.558Z" }, + { url = "https://files.pythonhosted.org/packages/b4/d6/5cfbfc97f3201a4d24b596a77957e092030dcc4205894bc035cedcfce62f/regex-2026.4.4-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:76d67d5afb1fe402d10a6403bae668d000441e2ab115191a804287d53b772951", size = 289692, upload-time = "2026-04-03T20:55:20.561Z" }, + { url = "https://files.pythonhosted.org/packages/8e/ac/f2212d9fd56fe897e36d0110ba30ba2d247bd6410c5bd98499c7e5a1e1f2/regex-2026.4.4-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e7cd3e4ee8d80447a83bbc9ab0c8459781fa77087f856c3e740d7763be0df27f", size = 796979, upload-time = "2026-04-03T20:55:22.56Z" }, + { url = "https://files.pythonhosted.org/packages/c9/e3/a016c12675fbac988a60c7e1c16e67823ff0bc016beb27bd7a001dbdabc6/regex-2026.4.4-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:2e19e18c568d2866d8b6a6dfad823db86193503f90823a8f66689315ba28fbe8", size = 866744, upload-time = "2026-04-03T20:55:24.646Z" }, + { url = "https://files.pythonhosted.org/packages/af/a4/0b90ca4cf17adc3cb43de80ec71018c37c88ad64987e8d0d481a95ca60b5/regex-2026.4.4-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:7698a6f38730fd1385d390d1ed07bb13dce39aa616aca6a6d89bea178464b9a4", size = 911613, upload-time = "2026-04-03T20:55:27.033Z" }, + { url = "https://files.pythonhosted.org/packages/8e/3b/2b3dac0b82d41ab43aa87c6ecde63d71189d03fe8854b8ca455a315edac3/regex-2026.4.4-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:173a66f3651cdb761018078e2d9487f4cf971232c990035ec0eb1cdc6bf929a9", size = 800551, upload-time = "2026-04-03T20:55:29.532Z" }, + { url = "https://files.pythonhosted.org/packages/25/fe/5365eb7aa0e753c4b5957815c321519ecab033c279c60e1b1ae2367fa810/regex-2026.4.4-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:fa7922bbb2cc84fa062d37723f199d4c0cd200245ce269c05db82d904db66b83", size = 776911, upload-time = "2026-04-03T20:55:31.526Z" }, + { url = "https://files.pythonhosted.org/packages/aa/b3/7fb0072156bba065e3b778a7bc7b0a6328212be5dd6a86fd207e0c4f2dab/regex-2026.4.4-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:59f67cd0a0acaf0e564c20bbd7f767286f23e91e2572c5703bf3e56ea7557edb", size = 785751, upload-time = "2026-04-03T20:55:33.797Z" }, + { url = "https://files.pythonhosted.org/packages/02/1a/9f83677eb699273e56e858f7bd95acdbee376d42f59e8bfca2fd80d79df3/regex-2026.4.4-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:475e50f3f73f73614f7cba5524d6de49dee269df00272a1b85e3d19f6d498465", size = 860484, upload-time = "2026-04-03T20:55:35.745Z" }, + { url = "https://files.pythonhosted.org/packages/3b/7a/93937507b61cfcff8b4c5857f1b452852b09f741daa9acae15c971d8554e/regex-2026.4.4-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:a1c0c7d67b64d85ac2e1879923bad2f08a08f3004055f2f406ef73c850114bd4", size = 765939, upload-time = "2026-04-03T20:55:37.972Z" }, + { url = "https://files.pythonhosted.org/packages/86/ea/81a7f968a351c6552b1670ead861e2a385be730ee28402233020c67f9e0f/regex-2026.4.4-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:1371c2ccbb744d66ee63631cc9ca12aa233d5749972626b68fe1a649dd98e566", size = 851417, upload-time = "2026-04-03T20:55:39.92Z" }, + { url = "https://files.pythonhosted.org/packages/4c/7e/323c18ce4b5b8f44517a36342961a0306e931e499febbd876bb149d900f0/regex-2026.4.4-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:59968142787042db793348a3f5b918cf24ced1f23247328530e063f89c128a95", size = 789056, upload-time = "2026-04-03T20:55:42.303Z" }, + { url = "https://files.pythonhosted.org/packages/c0/af/e7510f9b11b1913b0cd44eddb784b2d650b2af6515bfce4cffcc5bfd1d38/regex-2026.4.4-cp314-cp314-win32.whl", hash = "sha256:59efe72d37fd5a91e373e5146f187f921f365f4abc1249a5ab446a60f30dd5f8", size = 272130, upload-time = "2026-04-03T20:55:44.995Z" }, + { url = "https://files.pythonhosted.org/packages/9a/51/57dae534c915e2d3a21490e88836fa2ae79dde3b66255ecc0c0a155d2c10/regex-2026.4.4-cp314-cp314-win_amd64.whl", hash = "sha256:e0aab3ff447845049d676827d2ff714aab4f73f340e155b7de7458cf53baa5a4", size = 280992, upload-time = "2026-04-03T20:55:47.316Z" }, + { url = "https://files.pythonhosted.org/packages/0a/5e/abaf9f4c3792e34edb1434f06717fae2b07888d85cb5cec29f9204931bf8/regex-2026.4.4-cp314-cp314-win_arm64.whl", hash = "sha256:a7a5bb6aa0cf62208bb4fa079b0c756734f8ad0e333b425732e8609bd51ee22f", size = 273563, upload-time = "2026-04-03T20:55:49.273Z" }, + { url = "https://files.pythonhosted.org/packages/ff/06/35da85f9f217b9538b99cbb170738993bcc3b23784322decb77619f11502/regex-2026.4.4-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:97850d0638391bdc7d35dc1c1039974dcb921eaafa8cc935ae4d7f272b1d60b3", size = 494191, upload-time = "2026-04-03T20:55:51.258Z" }, + { url = "https://files.pythonhosted.org/packages/54/5b/1bc35f479eef8285c4baf88d8c002023efdeebb7b44a8735b36195486ae7/regex-2026.4.4-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:ee7337f88f2a580679f7bbfe69dc86c043954f9f9c541012f49abc554a962f2e", size = 293877, upload-time = "2026-04-03T20:55:53.214Z" }, + { url = "https://files.pythonhosted.org/packages/39/5b/f53b9ad17480b3ddd14c90da04bfb55ac6894b129e5dea87bcaf7d00e336/regex-2026.4.4-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:7429f4e6192c11d659900c0648ba8776243bf396ab95558b8c51a345afeddde6", size = 292410, upload-time = "2026-04-03T20:55:55.736Z" }, + { url = "https://files.pythonhosted.org/packages/bb/56/52377f59f60a7c51aa4161eecf0b6032c20b461805aca051250da435ffc9/regex-2026.4.4-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:dc4f10fbd5dd13dcf4265b4cc07d69ca70280742870c97ae10093e3d66000359", size = 811831, upload-time = "2026-04-03T20:55:57.802Z" }, + { url = "https://files.pythonhosted.org/packages/dd/63/8026310bf066f702a9c361f83a8c9658f3fe4edb349f9c1e5d5273b7c40c/regex-2026.4.4-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a152560af4f9742b96f3827090f866eeec5becd4765c8e0d3473d9d280e76a5a", size = 871199, upload-time = "2026-04-03T20:56:00.333Z" }, + { url = "https://files.pythonhosted.org/packages/20/9f/a514bbb00a466dbb506d43f187a04047f7be1505f10a9a15615ead5080ee/regex-2026.4.4-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:54170b3e95339f415d54651f97df3bff7434a663912f9358237941bbf9143f55", size = 917649, upload-time = "2026-04-03T20:56:02.445Z" }, + { url = "https://files.pythonhosted.org/packages/cb/6b/8399f68dd41a2030218839b9b18360d79b86d22b9fab5ef477c7f23ca67c/regex-2026.4.4-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:07f190d65f5a72dcb9cf7106bfc3d21e7a49dd2879eda2207b683f32165e4d99", size = 816388, upload-time = "2026-04-03T20:56:04.595Z" }, + { url = "https://files.pythonhosted.org/packages/1e/9c/103963f47c24339a483b05edd568594c2be486188f688c0170fd504b2948/regex-2026.4.4-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:9a2741ce5a29d3c84b0b94261ba630ab459a1b847a0d6beca7d62d188175c790", size = 785746, upload-time = "2026-04-03T20:56:07.13Z" }, + { url = "https://files.pythonhosted.org/packages/fa/ee/7f6054c0dec0cee3463c304405e4ff42e27cff05bf36fcb34be549ab17bd/regex-2026.4.4-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:b26c30df3a28fd9793113dac7385a4deb7294a06c0f760dd2b008bd49a9139bc", size = 801483, upload-time = "2026-04-03T20:56:09.365Z" }, + { url = "https://files.pythonhosted.org/packages/30/c2/51d3d941cf6070dc00c3338ecf138615fc3cce0421c3df6abe97a08af61a/regex-2026.4.4-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:421439d1bee44b19f4583ccf42670ca464ffb90e9fdc38d37f39d1ddd1e44f1f", size = 866331, upload-time = "2026-04-03T20:56:12.039Z" }, + { url = "https://files.pythonhosted.org/packages/16/e8/76d50dcc122ac33927d939f350eebcfe3dbcbda96913e03433fc36de5e63/regex-2026.4.4-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:b40379b53ecbc747fd9bdf4a0ea14eb8188ca1bd0f54f78893a39024b28f4863", size = 772673, upload-time = "2026-04-03T20:56:14.558Z" }, + { url = "https://files.pythonhosted.org/packages/a5/6e/5f6bf75e20ea6873d05ba4ec78378c375cbe08cdec571c83fbb01606e563/regex-2026.4.4-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:08c55c13d2eef54f73eeadc33146fb0baaa49e7335eb1aff6ae1324bf0ddbe4a", size = 857146, upload-time = "2026-04-03T20:56:16.663Z" }, + { url = "https://files.pythonhosted.org/packages/0b/33/3c76d9962949e487ebba353a18e89399f292287204ac8f2f4cfc3a51c233/regex-2026.4.4-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:9776b85f510062f5a75ef112afe5f494ef1635607bf1cc220c1391e9ac2f5e81", size = 803463, upload-time = "2026-04-03T20:56:18.923Z" }, + { url = "https://files.pythonhosted.org/packages/19/eb/ef32dcd2cb69b69bc0c3e55205bce94a7def48d495358946bc42186dcccc/regex-2026.4.4-cp314-cp314t-win32.whl", hash = "sha256:385edaebde5db5be103577afc8699fea73a0e36a734ba24870be7ffa61119d74", size = 275709, upload-time = "2026-04-03T20:56:20.996Z" }, + { url = "https://files.pythonhosted.org/packages/a0/86/c291bf740945acbf35ed7dbebf8e2eea2f3f78041f6bd7cdab80cb274dc0/regex-2026.4.4-cp314-cp314t-win_amd64.whl", hash = "sha256:5d354b18839328927832e2fa5f7c95b7a3ccc39e7a681529e1685898e6436d45", size = 285622, upload-time = "2026-04-03T20:56:23.641Z" }, + { url = "https://files.pythonhosted.org/packages/d5/e7/ec846d560ae6a597115153c02ca6138a7877a1748b2072d9521c10a93e58/regex-2026.4.4-cp314-cp314t-win_arm64.whl", hash = "sha256:af0384cb01a33600c49505c27c6c57ab0b27bf84a74e28524c92ca897ebdac9d", size = 275773, upload-time = "2026-04-03T20:56:26.07Z" }, +] + +[[package]] +name = "requests" +version = "2.33.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "certifi" }, + { name = "charset-normalizer" }, + { name = "idna" }, + { name = "urllib3" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/5f/a4/98b9c7c6428a668bf7e42ebb7c79d576a1c3c1e3ae2d47e674b468388871/requests-2.33.1.tar.gz", hash = "sha256:18817f8c57c6263968bc123d237e3b8b08ac046f5456bd1e307ee8f4250d3517", size = 134120, upload-time = "2026-03-30T16:09:15.531Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/d7/8e/7540e8a2036f79a125c1d2ebadf69ed7901608859186c856fa0388ef4197/requests-2.33.1-py3-none-any.whl", hash = "sha256:4e6d1ef462f3626a1f0a0a9c42dd93c63bad33f9f1c1937509b8c5c8718ab56a", size = 64947, upload-time = "2026-03-30T16:09:13.83Z" }, +] + +[[package]] +name = "rfc3339-validator" +version = "0.1.4" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "six" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/28/ea/a9387748e2d111c3c2b275ba970b735e04e15cdb1eb30693b6b5708c4dbd/rfc3339_validator-0.1.4.tar.gz", hash = "sha256:138a2abdf93304ad60530167e51d2dfb9549521a836871b88d7f4695d0022f6b", size = 5513, upload-time = "2021-05-12T16:37:54.178Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/7b/44/4e421b96b67b2daff264473f7465db72fbdf36a07e05494f50300cc7b0c6/rfc3339_validator-0.1.4-py2.py3-none-any.whl", hash = "sha256:24f6ec1eda14ef823da9e36ec7113124b39c04d50a4d3d3a3c2859577e7791fa", size = 3490, upload-time = "2021-05-12T16:37:52.536Z" }, +] + +[[package]] +name = "rfc3986-validator" +version = "0.1.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/da/88/f270de456dd7d11dcc808abfa291ecdd3f45ff44e3b549ffa01b126464d0/rfc3986_validator-0.1.1.tar.gz", hash = "sha256:3d44bde7921b3b9ec3ae4e3adca370438eccebc676456449b145d533b240d055", size = 6760, upload-time = "2019-10-28T16:00:19.144Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/9e/51/17023c0f8f1869d8806b979a2bffa3f861f26a3f1a66b094288323fba52f/rfc3986_validator-0.1.1-py2.py3-none-any.whl", hash = "sha256:2f235c432ef459970b4306369336b9d5dbdda31b510ca1e327636e01f528bfa9", size = 4242, upload-time = "2019-10-28T16:00:13.976Z" }, +] + +[[package]] +name = "rfc3987-syntax" +version = "1.1.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "lark" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/2c/06/37c1a5557acf449e8e406a830a05bf885ac47d33270aec454ef78675008d/rfc3987_syntax-1.1.0.tar.gz", hash = "sha256:717a62cbf33cffdd16dfa3a497d81ce48a660ea691b1ddd7be710c22f00b4a0d", size = 14239, upload-time = "2025-07-18T01:05:05.015Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/7e/71/44ce230e1b7fadd372515a97e32a83011f906ddded8d03e3c6aafbdedbb7/rfc3987_syntax-1.1.0-py3-none-any.whl", hash = "sha256:6c3d97604e4c5ce9f714898e05401a0445a641cfa276432b0a648c80856f6a3f", size = 8046, upload-time = "2025-07-18T01:05:03.843Z" }, +] + +[[package]] +name = "rich" +version = "15.0.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "markdown-it-py" }, + { name = "pygments" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/c0/8f/0722ca900cc807c13a6a0c696dacf35430f72e0ec571c4275d2371fca3e9/rich-15.0.0.tar.gz", hash = "sha256:edd07a4824c6b40189fb7ac9bc4c52536e9780fbbfbddf6f1e2502c31b068c36", size = 230680, upload-time = "2026-04-12T08:24:00.75Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/82/3b/64d4899d73f91ba49a8c18a8ff3f0ea8f1c1d75481760df8c68ef5235bf5/rich-15.0.0-py3-none-any.whl", hash = "sha256:33bd4ef74232fb73fe9279a257718407f169c09b78a87ad3d296f548e27de0bb", size = 310654, upload-time = "2026-04-12T08:24:02.83Z" }, +] + +[[package]] +name = "rpds-py" +version = "0.30.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/20/af/3f2f423103f1113b36230496629986e0ef7e199d2aa8392452b484b38ced/rpds_py-0.30.0.tar.gz", hash = "sha256:dd8ff7cf90014af0c0f787eea34794ebf6415242ee1d6fa91eaba725cc441e84", size = 69469, upload-time = "2025-11-30T20:24:38.837Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ed/dc/d61221eb88ff410de3c49143407f6f3147acf2538c86f2ab7ce65ae7d5f9/rpds_py-0.30.0-cp313-cp313-macosx_10_12_x86_64.whl", hash = "sha256:f83424d738204d9770830d35290ff3273fbb02b41f919870479fab14b9d303b2", size = 374887, upload-time = "2025-11-30T20:22:41.812Z" }, + { url = "https://files.pythonhosted.org/packages/fd/32/55fb50ae104061dbc564ef15cc43c013dc4a9f4527a1f4d99baddf56fe5f/rpds_py-0.30.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:e7536cd91353c5273434b4e003cbda89034d67e7710eab8761fd918ec6c69cf8", size = 358904, upload-time = "2025-11-30T20:22:43.479Z" }, + { url = "https://files.pythonhosted.org/packages/58/70/faed8186300e3b9bdd138d0273109784eea2396c68458ed580f885dfe7ad/rpds_py-0.30.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2771c6c15973347f50fece41fc447c054b7ac2ae0502388ce3b6738cd366e3d4", size = 389945, upload-time = "2025-11-30T20:22:44.819Z" }, + { url = "https://files.pythonhosted.org/packages/bd/a8/073cac3ed2c6387df38f71296d002ab43496a96b92c823e76f46b8af0543/rpds_py-0.30.0-cp313-cp313-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:0a59119fc6e3f460315fe9d08149f8102aa322299deaa5cab5b40092345c2136", size = 407783, upload-time = "2025-11-30T20:22:46.103Z" }, + { url = "https://files.pythonhosted.org/packages/77/57/5999eb8c58671f1c11eba084115e77a8899d6e694d2a18f69f0ba471ec8b/rpds_py-0.30.0-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:76fec018282b4ead0364022e3c54b60bf368b9d926877957a8624b58419169b7", size = 515021, upload-time = "2025-11-30T20:22:47.458Z" }, + { url = "https://files.pythonhosted.org/packages/e0/af/5ab4833eadc36c0a8ed2bc5c0de0493c04f6c06de223170bd0798ff98ced/rpds_py-0.30.0-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:692bef75a5525db97318e8cd061542b5a79812d711ea03dbc1f6f8dbb0c5f0d2", size = 414589, upload-time = "2025-11-30T20:22:48.872Z" }, + { url = "https://files.pythonhosted.org/packages/b7/de/f7192e12b21b9e9a68a6d0f249b4af3fdcdff8418be0767a627564afa1f1/rpds_py-0.30.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9027da1ce107104c50c81383cae773ef5c24d296dd11c99e2629dbd7967a20c6", size = 394025, upload-time = "2025-11-30T20:22:50.196Z" }, + { url = "https://files.pythonhosted.org/packages/91/c4/fc70cd0249496493500e7cc2de87504f5aa6509de1e88623431fec76d4b6/rpds_py-0.30.0-cp313-cp313-manylinux_2_31_riscv64.whl", hash = "sha256:9cf69cdda1f5968a30a359aba2f7f9aa648a9ce4b580d6826437f2b291cfc86e", size = 408895, upload-time = "2025-11-30T20:22:51.87Z" }, + { url = "https://files.pythonhosted.org/packages/58/95/d9275b05ab96556fefff73a385813eb66032e4c99f411d0795372d9abcea/rpds_py-0.30.0-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:a4796a717bf12b9da9d3ad002519a86063dcac8988b030e405704ef7d74d2d9d", size = 422799, upload-time = "2025-11-30T20:22:53.341Z" }, + { url = "https://files.pythonhosted.org/packages/06/c1/3088fc04b6624eb12a57eb814f0d4997a44b0d208d6cace713033ff1a6ba/rpds_py-0.30.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:5d4c2aa7c50ad4728a094ebd5eb46c452e9cb7edbfdb18f9e1221f597a73e1e7", size = 572731, upload-time = "2025-11-30T20:22:54.778Z" }, + { url = "https://files.pythonhosted.org/packages/d8/42/c612a833183b39774e8ac8fecae81263a68b9583ee343db33ab571a7ce55/rpds_py-0.30.0-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:ba81a9203d07805435eb06f536d95a266c21e5b2dfbf6517748ca40c98d19e31", size = 599027, upload-time = "2025-11-30T20:22:56.212Z" }, + { url = "https://files.pythonhosted.org/packages/5f/60/525a50f45b01d70005403ae0e25f43c0384369ad24ffe46e8d9068b50086/rpds_py-0.30.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:945dccface01af02675628334f7cf49c2af4c1c904748efc5cf7bbdf0b579f95", size = 563020, upload-time = "2025-11-30T20:22:58.2Z" }, + { url = "https://files.pythonhosted.org/packages/0b/5d/47c4655e9bcd5ca907148535c10e7d489044243cc9941c16ed7cd53be91d/rpds_py-0.30.0-cp313-cp313-win32.whl", hash = "sha256:b40fb160a2db369a194cb27943582b38f79fc4887291417685f3ad693c5a1d5d", size = 223139, upload-time = "2025-11-30T20:23:00.209Z" }, + { url = "https://files.pythonhosted.org/packages/f2/e1/485132437d20aa4d3e1d8b3fb5a5e65aa8139f1e097080c2a8443201742c/rpds_py-0.30.0-cp313-cp313-win_amd64.whl", hash = "sha256:806f36b1b605e2d6a72716f321f20036b9489d29c51c91f4dd29a3e3afb73b15", size = 240224, upload-time = "2025-11-30T20:23:02.008Z" }, + { url = "https://files.pythonhosted.org/packages/24/95/ffd128ed1146a153d928617b0ef673960130be0009c77d8fbf0abe306713/rpds_py-0.30.0-cp313-cp313-win_arm64.whl", hash = "sha256:d96c2086587c7c30d44f31f42eae4eac89b60dabbac18c7669be3700f13c3ce1", size = 230645, upload-time = "2025-11-30T20:23:03.43Z" }, + { url = "https://files.pythonhosted.org/packages/ff/1b/b10de890a0def2a319a2626334a7f0ae388215eb60914dbac8a3bae54435/rpds_py-0.30.0-cp313-cp313t-macosx_10_12_x86_64.whl", hash = "sha256:eb0b93f2e5c2189ee831ee43f156ed34e2a89a78a66b98cadad955972548be5a", size = 364443, upload-time = "2025-11-30T20:23:04.878Z" }, + { url = "https://files.pythonhosted.org/packages/0d/bf/27e39f5971dc4f305a4fb9c672ca06f290f7c4e261c568f3dea16a410d47/rpds_py-0.30.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:922e10f31f303c7c920da8981051ff6d8c1a56207dbdf330d9047f6d30b70e5e", size = 353375, upload-time = "2025-11-30T20:23:06.342Z" }, + { url = "https://files.pythonhosted.org/packages/40/58/442ada3bba6e8e6615fc00483135c14a7538d2ffac30e2d933ccf6852232/rpds_py-0.30.0-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cdc62c8286ba9bf7f47befdcea13ea0e26bf294bda99758fd90535cbaf408000", size = 383850, upload-time = "2025-11-30T20:23:07.825Z" }, + { url = "https://files.pythonhosted.org/packages/14/14/f59b0127409a33c6ef6f5c1ebd5ad8e32d7861c9c7adfa9a624fc3889f6c/rpds_py-0.30.0-cp313-cp313t-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:47f9a91efc418b54fb8190a6b4aa7813a23fb79c51f4bb84e418f5476c38b8db", size = 392812, upload-time = "2025-11-30T20:23:09.228Z" }, + { url = "https://files.pythonhosted.org/packages/b3/66/e0be3e162ac299b3a22527e8913767d869e6cc75c46bd844aa43fb81ab62/rpds_py-0.30.0-cp313-cp313t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1f3587eb9b17f3789ad50824084fa6f81921bbf9a795826570bda82cb3ed91f2", size = 517841, upload-time = "2025-11-30T20:23:11.186Z" }, + { url = "https://files.pythonhosted.org/packages/3d/55/fa3b9cf31d0c963ecf1ba777f7cf4b2a2c976795ac430d24a1f43d25a6ba/rpds_py-0.30.0-cp313-cp313t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:39c02563fc592411c2c61d26b6c5fe1e51eaa44a75aa2c8735ca88b0d9599daa", size = 408149, upload-time = "2025-11-30T20:23:12.864Z" }, + { url = "https://files.pythonhosted.org/packages/60/ca/780cf3b1a32b18c0f05c441958d3758f02544f1d613abf9488cd78876378/rpds_py-0.30.0-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:51a1234d8febafdfd33a42d97da7a43f5dcb120c1060e352a3fbc0c6d36e2083", size = 383843, upload-time = "2025-11-30T20:23:14.638Z" }, + { url = "https://files.pythonhosted.org/packages/82/86/d5f2e04f2aa6247c613da0c1dd87fcd08fa17107e858193566048a1e2f0a/rpds_py-0.30.0-cp313-cp313t-manylinux_2_31_riscv64.whl", hash = "sha256:eb2c4071ab598733724c08221091e8d80e89064cd472819285a9ab0f24bcedb9", size = 396507, upload-time = "2025-11-30T20:23:16.105Z" }, + { url = "https://files.pythonhosted.org/packages/4b/9a/453255d2f769fe44e07ea9785c8347edaf867f7026872e76c1ad9f7bed92/rpds_py-0.30.0-cp313-cp313t-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:6bdfdb946967d816e6adf9a3d8201bfad269c67efe6cefd7093ef959683c8de0", size = 414949, upload-time = "2025-11-30T20:23:17.539Z" }, + { url = "https://files.pythonhosted.org/packages/a3/31/622a86cdc0c45d6df0e9ccb6becdba5074735e7033c20e401a6d9d0e2ca0/rpds_py-0.30.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:c77afbd5f5250bf27bf516c7c4a016813eb2d3e116139aed0096940c5982da94", size = 565790, upload-time = "2025-11-30T20:23:19.029Z" }, + { url = "https://files.pythonhosted.org/packages/1c/5d/15bbf0fb4a3f58a3b1c67855ec1efcc4ceaef4e86644665fff03e1b66d8d/rpds_py-0.30.0-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:61046904275472a76c8c90c9ccee9013d70a6d0f73eecefd38c1ae7c39045a08", size = 590217, upload-time = "2025-11-30T20:23:20.885Z" }, + { url = "https://files.pythonhosted.org/packages/6d/61/21b8c41f68e60c8cc3b2e25644f0e3681926020f11d06ab0b78e3c6bbff1/rpds_py-0.30.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:4c5f36a861bc4b7da6516dbdf302c55313afa09b81931e8280361a4f6c9a2d27", size = 555806, upload-time = "2025-11-30T20:23:22.488Z" }, + { url = "https://files.pythonhosted.org/packages/f9/39/7e067bb06c31de48de3eb200f9fc7c58982a4d3db44b07e73963e10d3be9/rpds_py-0.30.0-cp313-cp313t-win32.whl", hash = "sha256:3d4a69de7a3e50ffc214ae16d79d8fbb0922972da0356dcf4d0fdca2878559c6", size = 211341, upload-time = "2025-11-30T20:23:24.449Z" }, + { url = "https://files.pythonhosted.org/packages/0a/4d/222ef0b46443cf4cf46764d9c630f3fe4abaa7245be9417e56e9f52b8f65/rpds_py-0.30.0-cp313-cp313t-win_amd64.whl", hash = "sha256:f14fc5df50a716f7ece6a80b6c78bb35ea2ca47c499e422aa4463455dd96d56d", size = 225768, upload-time = "2025-11-30T20:23:25.908Z" }, + { url = "https://files.pythonhosted.org/packages/86/81/dad16382ebbd3d0e0328776d8fd7ca94220e4fa0798d1dc5e7da48cb3201/rpds_py-0.30.0-cp314-cp314-macosx_10_12_x86_64.whl", hash = "sha256:68f19c879420aa08f61203801423f6cd5ac5f0ac4ac82a2368a9fcd6a9a075e0", size = 362099, upload-time = "2025-11-30T20:23:27.316Z" }, + { url = "https://files.pythonhosted.org/packages/2b/60/19f7884db5d5603edf3c6bce35408f45ad3e97e10007df0e17dd57af18f8/rpds_py-0.30.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:ec7c4490c672c1a0389d319b3a9cfcd098dcdc4783991553c332a15acf7249be", size = 353192, upload-time = "2025-11-30T20:23:29.151Z" }, + { url = "https://files.pythonhosted.org/packages/bf/c4/76eb0e1e72d1a9c4703c69607cec123c29028bff28ce41588792417098ac/rpds_py-0.30.0-cp314-cp314-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f251c812357a3fed308d684a5079ddfb9d933860fc6de89f2b7ab00da481e65f", size = 384080, upload-time = "2025-11-30T20:23:30.785Z" }, + { url = "https://files.pythonhosted.org/packages/72/87/87ea665e92f3298d1b26d78814721dc39ed8d2c74b86e83348d6b48a6f31/rpds_py-0.30.0-cp314-cp314-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:ac98b175585ecf4c0348fd7b29c3864bda53b805c773cbf7bfdaffc8070c976f", size = 394841, upload-time = "2025-11-30T20:23:32.209Z" }, + { url = "https://files.pythonhosted.org/packages/77/ad/7783a89ca0587c15dcbf139b4a8364a872a25f861bdb88ed99f9b0dec985/rpds_py-0.30.0-cp314-cp314-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:3e62880792319dbeb7eb866547f2e35973289e7d5696c6e295476448f5b63c87", size = 516670, upload-time = "2025-11-30T20:23:33.742Z" }, + { url = "https://files.pythonhosted.org/packages/5b/3c/2882bdac942bd2172f3da574eab16f309ae10a3925644e969536553cb4ee/rpds_py-0.30.0-cp314-cp314-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:4e7fc54e0900ab35d041b0601431b0a0eb495f0851a0639b6ef90f7741b39a18", size = 408005, upload-time = "2025-11-30T20:23:35.253Z" }, + { url = "https://files.pythonhosted.org/packages/ce/81/9a91c0111ce1758c92516a3e44776920b579d9a7c09b2b06b642d4de3f0f/rpds_py-0.30.0-cp314-cp314-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:47e77dc9822d3ad616c3d5759ea5631a75e5809d5a28707744ef79d7a1bcfcad", size = 382112, upload-time = "2025-11-30T20:23:36.842Z" }, + { url = "https://files.pythonhosted.org/packages/cf/8e/1da49d4a107027e5fbc64daeab96a0706361a2918da10cb41769244b805d/rpds_py-0.30.0-cp314-cp314-manylinux_2_31_riscv64.whl", hash = "sha256:b4dc1a6ff022ff85ecafef7979a2c6eb423430e05f1165d6688234e62ba99a07", size = 399049, upload-time = "2025-11-30T20:23:38.343Z" }, + { url = "https://files.pythonhosted.org/packages/df/5a/7ee239b1aa48a127570ec03becbb29c9d5a9eb092febbd1699d567cae859/rpds_py-0.30.0-cp314-cp314-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:4559c972db3a360808309e06a74628b95eaccbf961c335c8fe0d590cf587456f", size = 415661, upload-time = "2025-11-30T20:23:40.263Z" }, + { url = "https://files.pythonhosted.org/packages/70/ea/caa143cf6b772f823bc7929a45da1fa83569ee49b11d18d0ada7f5ee6fd6/rpds_py-0.30.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:0ed177ed9bded28f8deb6ab40c183cd1192aa0de40c12f38be4d59cd33cb5c65", size = 565606, upload-time = "2025-11-30T20:23:42.186Z" }, + { url = "https://files.pythonhosted.org/packages/64/91/ac20ba2d69303f961ad8cf55bf7dbdb4763f627291ba3d0d7d67333cced9/rpds_py-0.30.0-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:ad1fa8db769b76ea911cb4e10f049d80bf518c104f15b3edb2371cc65375c46f", size = 591126, upload-time = "2025-11-30T20:23:44.086Z" }, + { url = "https://files.pythonhosted.org/packages/21/20/7ff5f3c8b00c8a95f75985128c26ba44503fb35b8e0259d812766ea966c7/rpds_py-0.30.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:46e83c697b1f1c72b50e5ee5adb4353eef7406fb3f2043d64c33f20ad1c2fc53", size = 553371, upload-time = "2025-11-30T20:23:46.004Z" }, + { url = "https://files.pythonhosted.org/packages/72/c7/81dadd7b27c8ee391c132a6b192111ca58d866577ce2d9b0ca157552cce0/rpds_py-0.30.0-cp314-cp314-win32.whl", hash = "sha256:ee454b2a007d57363c2dfd5b6ca4a5d7e2c518938f8ed3b706e37e5d470801ed", size = 215298, upload-time = "2025-11-30T20:23:47.696Z" }, + { url = "https://files.pythonhosted.org/packages/3e/d2/1aaac33287e8cfb07aab2e6b8ac1deca62f6f65411344f1433c55e6f3eb8/rpds_py-0.30.0-cp314-cp314-win_amd64.whl", hash = "sha256:95f0802447ac2d10bcc69f6dc28fe95fdf17940367b21d34e34c737870758950", size = 228604, upload-time = "2025-11-30T20:23:49.501Z" }, + { url = "https://files.pythonhosted.org/packages/e8/95/ab005315818cc519ad074cb7784dae60d939163108bd2b394e60dc7b5461/rpds_py-0.30.0-cp314-cp314-win_arm64.whl", hash = "sha256:613aa4771c99f03346e54c3f038e4cc574ac09a3ddfb0e8878487335e96dead6", size = 222391, upload-time = "2025-11-30T20:23:50.96Z" }, + { url = "https://files.pythonhosted.org/packages/9e/68/154fe0194d83b973cdedcdcc88947a2752411165930182ae41d983dcefa6/rpds_py-0.30.0-cp314-cp314t-macosx_10_12_x86_64.whl", hash = "sha256:7e6ecfcb62edfd632e56983964e6884851786443739dbfe3582947e87274f7cb", size = 364868, upload-time = "2025-11-30T20:23:52.494Z" }, + { url = "https://files.pythonhosted.org/packages/83/69/8bbc8b07ec854d92a8b75668c24d2abcb1719ebf890f5604c61c9369a16f/rpds_py-0.30.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:a1d0bc22a7cdc173fedebb73ef81e07faef93692b8c1ad3733b67e31e1b6e1b8", size = 353747, upload-time = "2025-11-30T20:23:54.036Z" }, + { url = "https://files.pythonhosted.org/packages/ab/00/ba2e50183dbd9abcce9497fa5149c62b4ff3e22d338a30d690f9af970561/rpds_py-0.30.0-cp314-cp314t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0d08f00679177226c4cb8c5265012eea897c8ca3b93f429e546600c971bcbae7", size = 383795, upload-time = "2025-11-30T20:23:55.556Z" }, + { url = "https://files.pythonhosted.org/packages/05/6f/86f0272b84926bcb0e4c972262f54223e8ecc556b3224d281e6598fc9268/rpds_py-0.30.0-cp314-cp314t-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:5965af57d5848192c13534f90f9dd16464f3c37aaf166cc1da1cae1fd5a34898", size = 393330, upload-time = "2025-11-30T20:23:57.033Z" }, + { url = "https://files.pythonhosted.org/packages/cb/e9/0e02bb2e6dc63d212641da45df2b0bf29699d01715913e0d0f017ee29438/rpds_py-0.30.0-cp314-cp314t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9a4e86e34e9ab6b667c27f3211ca48f73dba7cd3d90f8d5b11be56e5dbc3fb4e", size = 518194, upload-time = "2025-11-30T20:23:58.637Z" }, + { url = "https://files.pythonhosted.org/packages/ee/ca/be7bca14cf21513bdf9c0606aba17d1f389ea2b6987035eb4f62bd923f25/rpds_py-0.30.0-cp314-cp314t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e5d3e6b26f2c785d65cc25ef1e5267ccbe1b069c5c21b8cc724efee290554419", size = 408340, upload-time = "2025-11-30T20:24:00.2Z" }, + { url = "https://files.pythonhosted.org/packages/c2/c7/736e00ebf39ed81d75544c0da6ef7b0998f8201b369acf842f9a90dc8fce/rpds_py-0.30.0-cp314-cp314t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:626a7433c34566535b6e56a1b39a7b17ba961e97ce3b80ec62e6f1312c025551", size = 383765, upload-time = "2025-11-30T20:24:01.759Z" }, + { url = "https://files.pythonhosted.org/packages/4a/3f/da50dfde9956aaf365c4adc9533b100008ed31aea635f2b8d7b627e25b49/rpds_py-0.30.0-cp314-cp314t-manylinux_2_31_riscv64.whl", hash = "sha256:acd7eb3f4471577b9b5a41baf02a978e8bdeb08b4b355273994f8b87032000a8", size = 396834, upload-time = "2025-11-30T20:24:03.687Z" }, + { url = "https://files.pythonhosted.org/packages/4e/00/34bcc2565b6020eab2623349efbdec810676ad571995911f1abdae62a3a0/rpds_py-0.30.0-cp314-cp314t-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:fe5fa731a1fa8a0a56b0977413f8cacac1768dad38d16b3a296712709476fbd5", size = 415470, upload-time = "2025-11-30T20:24:05.232Z" }, + { url = "https://files.pythonhosted.org/packages/8c/28/882e72b5b3e6f718d5453bd4d0d9cf8df36fddeb4ddbbab17869d5868616/rpds_py-0.30.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:74a3243a411126362712ee1524dfc90c650a503502f135d54d1b352bd01f2404", size = 565630, upload-time = "2025-11-30T20:24:06.878Z" }, + { url = "https://files.pythonhosted.org/packages/3b/97/04a65539c17692de5b85c6e293520fd01317fd878ea1995f0367d4532fb1/rpds_py-0.30.0-cp314-cp314t-musllinux_1_2_i686.whl", hash = "sha256:3e8eeb0544f2eb0d2581774be4c3410356eba189529a6b3e36bbbf9696175856", size = 591148, upload-time = "2025-11-30T20:24:08.445Z" }, + { url = "https://files.pythonhosted.org/packages/85/70/92482ccffb96f5441aab93e26c4d66489eb599efdcf96fad90c14bbfb976/rpds_py-0.30.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:dbd936cde57abfee19ab3213cf9c26be06d60750e60a8e4dd85d1ab12c8b1f40", size = 556030, upload-time = "2025-11-30T20:24:10.956Z" }, + { url = "https://files.pythonhosted.org/packages/20/53/7c7e784abfa500a2b6b583b147ee4bb5a2b3747a9166bab52fec4b5b5e7d/rpds_py-0.30.0-cp314-cp314t-win32.whl", hash = "sha256:dc824125c72246d924f7f796b4f63c1e9dc810c7d9e2355864b3c3a73d59ade0", size = 211570, upload-time = "2025-11-30T20:24:12.735Z" }, + { url = "https://files.pythonhosted.org/packages/d0/02/fa464cdfbe6b26e0600b62c528b72d8608f5cc49f96b8d6e38c95d60c676/rpds_py-0.30.0-cp314-cp314t-win_amd64.whl", hash = "sha256:27f4b0e92de5bfbc6f86e43959e6edd1425c33b5e69aab0984a72047f2bcf1e3", size = 226532, upload-time = "2025-11-30T20:24:14.634Z" }, +] + +[[package]] +name = "safetensors" +version = "0.7.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/29/9c/6e74567782559a63bd040a236edca26fd71bc7ba88de2ef35d75df3bca5e/safetensors-0.7.0.tar.gz", hash = "sha256:07663963b67e8bd9f0b8ad15bb9163606cd27cc5a1b96235a50d8369803b96b0", size = 200878, upload-time = "2025-11-19T15:18:43.199Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/fa/47/aef6c06649039accf914afef490268e1067ed82be62bcfa5b7e886ad15e8/safetensors-0.7.0-cp38-abi3-macosx_10_12_x86_64.whl", hash = "sha256:c82f4d474cf725255d9e6acf17252991c3c8aac038d6ef363a4bf8be2f6db517", size = 467781, upload-time = "2025-11-19T15:18:35.84Z" }, + { url = "https://files.pythonhosted.org/packages/e8/00/374c0c068e30cd31f1e1b46b4b5738168ec79e7689ca82ee93ddfea05109/safetensors-0.7.0-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:94fd4858284736bb67a897a41608b5b0c2496c9bdb3bf2af1fa3409127f20d57", size = 447058, upload-time = "2025-11-19T15:18:34.416Z" }, + { url = "https://files.pythonhosted.org/packages/f1/06/578ffed52c2296f93d7fd2d844cabfa92be51a587c38c8afbb8ae449ca89/safetensors-0.7.0-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e07d91d0c92a31200f25351f4acb2bc6aff7f48094e13ebb1d0fb995b54b6542", size = 491748, upload-time = "2025-11-19T15:18:09.79Z" }, + { url = "https://files.pythonhosted.org/packages/ae/33/1debbbb70e4791dde185edb9413d1fe01619255abb64b300157d7f15dddd/safetensors-0.7.0-cp38-abi3-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:8469155f4cb518bafb4acf4865e8bb9d6804110d2d9bdcaa78564b9fd841e104", size = 503881, upload-time = "2025-11-19T15:18:16.145Z" }, + { url = "https://files.pythonhosted.org/packages/8e/1c/40c2ca924d60792c3be509833df711b553c60effbd91da6f5284a83f7122/safetensors-0.7.0-cp38-abi3-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:54bef08bf00a2bff599982f6b08e8770e09cc012d7bba00783fc7ea38f1fb37d", size = 623463, upload-time = "2025-11-19T15:18:21.11Z" }, + { url = "https://files.pythonhosted.org/packages/9b/3a/13784a9364bd43b0d61eef4bea2845039bc2030458b16594a1bd787ae26e/safetensors-0.7.0-cp38-abi3-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:42cb091236206bb2016d245c377ed383aa7f78691748f3bb6ee1bfa51ae2ce6a", size = 532855, upload-time = "2025-11-19T15:18:25.719Z" }, + { url = "https://files.pythonhosted.org/packages/a0/60/429e9b1cb3fc651937727befe258ea24122d9663e4d5709a48c9cbfceecb/safetensors-0.7.0-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dac7252938f0696ddea46f5e855dd3138444e82236e3be475f54929f0c510d48", size = 507152, upload-time = "2025-11-19T15:18:33.023Z" }, + { url = "https://files.pythonhosted.org/packages/3c/a8/4b45e4e059270d17af60359713ffd83f97900d45a6afa73aaa0d737d48b6/safetensors-0.7.0-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:1d060c70284127fa805085d8f10fbd0962792aed71879d00864acda69dbab981", size = 541856, upload-time = "2025-11-19T15:18:31.075Z" }, + { url = "https://files.pythonhosted.org/packages/06/87/d26d8407c44175d8ae164a95b5a62707fcc445f3c0c56108e37d98070a3d/safetensors-0.7.0-cp38-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:cdab83a366799fa730f90a4ebb563e494f28e9e92c4819e556152ad55e43591b", size = 674060, upload-time = "2025-11-19T15:18:37.211Z" }, + { url = "https://files.pythonhosted.org/packages/11/f5/57644a2ff08dc6325816ba7217e5095f17269dada2554b658442c66aed51/safetensors-0.7.0-cp38-abi3-musllinux_1_2_armv7l.whl", hash = "sha256:672132907fcad9f2aedcb705b2d7b3b93354a2aec1b2f706c4db852abe338f85", size = 771715, upload-time = "2025-11-19T15:18:38.689Z" }, + { url = "https://files.pythonhosted.org/packages/86/31/17883e13a814bd278ae6e266b13282a01049b0c81341da7fd0e3e71a80a3/safetensors-0.7.0-cp38-abi3-musllinux_1_2_i686.whl", hash = "sha256:5d72abdb8a4d56d4020713724ba81dac065fedb7f3667151c4a637f1d3fb26c0", size = 714377, upload-time = "2025-11-19T15:18:40.162Z" }, + { url = "https://files.pythonhosted.org/packages/4a/d8/0c8a7dc9b41dcac53c4cbf9df2b9c83e0e0097203de8b37a712b345c0be5/safetensors-0.7.0-cp38-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:b0f6d66c1c538d5a94a73aa9ddca8ccc4227e6c9ff555322ea40bdd142391dd4", size = 677368, upload-time = "2025-11-19T15:18:41.627Z" }, + { url = "https://files.pythonhosted.org/packages/05/e5/cb4b713c8a93469e3c5be7c3f8d77d307e65fe89673e731f5c2bfd0a9237/safetensors-0.7.0-cp38-abi3-win32.whl", hash = "sha256:c74af94bf3ac15ac4d0f2a7c7b4663a15f8c2ab15ed0fc7531ca61d0835eccba", size = 326423, upload-time = "2025-11-19T15:18:45.74Z" }, + { url = "https://files.pythonhosted.org/packages/5d/e6/ec8471c8072382cb91233ba7267fd931219753bb43814cbc71757bfd4dab/safetensors-0.7.0-cp38-abi3-win_amd64.whl", hash = "sha256:d1239932053f56f3456f32eb9625590cc7582e905021f94636202a864d470755", size = 341380, upload-time = "2025-11-19T15:18:44.427Z" }, +] + +[[package]] +name = "scikit-learn" +version = "1.8.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "joblib" }, + { name = "numpy" }, + { name = "scipy" }, + { name = "threadpoolctl" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/0e/d4/40988bf3b8e34feec1d0e6a051446b1f66225f8529b9309becaeef62b6c4/scikit_learn-1.8.0.tar.gz", hash = "sha256:9bccbb3b40e3de10351f8f5068e105d0f4083b1a65fa07b6634fbc401a6287fd", size = 7335585, upload-time = "2025-12-10T07:08:53.618Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/03/aa/e22e0768512ce9255eba34775be2e85c2048da73da1193e841707f8f039c/scikit_learn-1.8.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:0d6ae97234d5d7079dc0040990a6f7aeb97cb7fa7e8945f1999a429b23569e0a", size = 8513770, upload-time = "2025-12-10T07:08:03.251Z" }, + { url = "https://files.pythonhosted.org/packages/58/37/31b83b2594105f61a381fc74ca19e8780ee923be2d496fcd8d2e1147bd99/scikit_learn-1.8.0-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:edec98c5e7c128328124a029bceb09eda2d526997780fef8d65e9a69eead963e", size = 8044458, upload-time = "2025-12-10T07:08:05.336Z" }, + { url = "https://files.pythonhosted.org/packages/2d/5a/3f1caed8765f33eabb723596666da4ebbf43d11e96550fb18bdec42b467b/scikit_learn-1.8.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:74b66d8689d52ed04c271e1329f0c61635bcaf5b926db9b12d58914cdc01fe57", size = 8610341, upload-time = "2025-12-10T07:08:07.732Z" }, + { url = "https://files.pythonhosted.org/packages/38/cf/06896db3f71c75902a8e9943b444a56e727418f6b4b4a90c98c934f51ed4/scikit_learn-1.8.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8fdf95767f989b0cfedb85f7ed8ca215d4be728031f56ff5a519ee1e3276dc2e", size = 8900022, upload-time = "2025-12-10T07:08:09.862Z" }, + { url = "https://files.pythonhosted.org/packages/1c/f9/9b7563caf3ec8873e17a31401858efab6b39a882daf6c1bfa88879c0aa11/scikit_learn-1.8.0-cp313-cp313-win_amd64.whl", hash = "sha256:2de443b9373b3b615aec1bb57f9baa6bb3a9bd093f1269ba95c17d870422b271", size = 7989409, upload-time = "2025-12-10T07:08:12.028Z" }, + { url = "https://files.pythonhosted.org/packages/49/bd/1f4001503650e72c4f6009ac0c4413cb17d2d601cef6f71c0453da2732fc/scikit_learn-1.8.0-cp313-cp313-win_arm64.whl", hash = "sha256:eddde82a035681427cbedded4e6eff5e57fa59216c2e3e90b10b19ab1d0a65c3", size = 7619760, upload-time = "2025-12-10T07:08:13.688Z" }, + { url = "https://files.pythonhosted.org/packages/d2/7d/a630359fc9dcc95496588c8d8e3245cc8fd81980251079bc09c70d41d951/scikit_learn-1.8.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:7cc267b6108f0a1499a734167282c00c4ebf61328566b55ef262d48e9849c735", size = 8826045, upload-time = "2025-12-10T07:08:15.215Z" }, + { url = "https://files.pythonhosted.org/packages/cc/56/a0c86f6930cfcd1c7054a2bc417e26960bb88d32444fe7f71d5c2cfae891/scikit_learn-1.8.0-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:fe1c011a640a9f0791146011dfd3c7d9669785f9fed2b2a5f9e207536cf5c2fd", size = 8420324, upload-time = "2025-12-10T07:08:17.561Z" }, + { url = "https://files.pythonhosted.org/packages/46/1e/05962ea1cebc1cf3876667ecb14c283ef755bf409993c5946ade3b77e303/scikit_learn-1.8.0-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:72358cce49465d140cc4e7792015bb1f0296a9742d5622c67e31399b75468b9e", size = 8680651, upload-time = "2025-12-10T07:08:19.952Z" }, + { url = "https://files.pythonhosted.org/packages/fe/56/a85473cd75f200c9759e3a5f0bcab2d116c92a8a02ee08ccd73b870f8bb4/scikit_learn-1.8.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:80832434a6cc114f5219211eec13dcbc16c2bac0e31ef64c6d346cde3cf054cb", size = 8925045, upload-time = "2025-12-10T07:08:22.11Z" }, + { url = "https://files.pythonhosted.org/packages/cc/b7/64d8cfa896c64435ae57f4917a548d7ac7a44762ff9802f75a79b77cb633/scikit_learn-1.8.0-cp313-cp313t-win_amd64.whl", hash = "sha256:ee787491dbfe082d9c3013f01f5991658b0f38aa8177e4cd4bf434c58f551702", size = 8507994, upload-time = "2025-12-10T07:08:23.943Z" }, + { url = "https://files.pythonhosted.org/packages/5e/37/e192ea709551799379958b4c4771ec507347027bb7c942662c7fbeba31cb/scikit_learn-1.8.0-cp313-cp313t-win_arm64.whl", hash = "sha256:bf97c10a3f5a7543f9b88cbf488d33d175e9146115a451ae34568597ba33dcde", size = 7869518, upload-time = "2025-12-10T07:08:25.71Z" }, + { url = "https://files.pythonhosted.org/packages/24/05/1af2c186174cc92dcab2233f327336058c077d38f6fe2aceb08e6ab4d509/scikit_learn-1.8.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:c22a2da7a198c28dd1a6e1136f19c830beab7fdca5b3e5c8bba8394f8a5c45b3", size = 8528667, upload-time = "2025-12-10T07:08:27.541Z" }, + { url = "https://files.pythonhosted.org/packages/a8/25/01c0af38fe969473fb292bba9dc2b8f9b451f3112ff242c647fee3d0dfe7/scikit_learn-1.8.0-cp314-cp314-macosx_12_0_arm64.whl", hash = "sha256:6b595b07a03069a2b1740dc08c2299993850ea81cce4fe19b2421e0c970de6b7", size = 8066524, upload-time = "2025-12-10T07:08:29.822Z" }, + { url = "https://files.pythonhosted.org/packages/be/ce/a0623350aa0b68647333940ee46fe45086c6060ec604874e38e9ab7d8e6c/scikit_learn-1.8.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:29ffc74089f3d5e87dfca4c2c8450f88bdc61b0fc6ed5d267f3988f19a1309f6", size = 8657133, upload-time = "2025-12-10T07:08:31.865Z" }, + { url = "https://files.pythonhosted.org/packages/b8/cb/861b41341d6f1245e6ca80b1c1a8c4dfce43255b03df034429089ca2a2c5/scikit_learn-1.8.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fb65db5d7531bccf3a4f6bec3462223bea71384e2cda41da0f10b7c292b9e7c4", size = 8923223, upload-time = "2025-12-10T07:08:34.166Z" }, + { url = "https://files.pythonhosted.org/packages/76/18/a8def8f91b18cd1ba6e05dbe02540168cb24d47e8dcf69e8d00b7da42a08/scikit_learn-1.8.0-cp314-cp314-win_amd64.whl", hash = "sha256:56079a99c20d230e873ea40753102102734c5953366972a71d5cb39a32bc40c6", size = 8096518, upload-time = "2025-12-10T07:08:36.339Z" }, + { url = "https://files.pythonhosted.org/packages/d1/77/482076a678458307f0deb44e29891d6022617b2a64c840c725495bee343f/scikit_learn-1.8.0-cp314-cp314-win_arm64.whl", hash = "sha256:3bad7565bc9cf37ce19a7c0d107742b320c1285df7aab1a6e2d28780df167242", size = 7754546, upload-time = "2025-12-10T07:08:38.128Z" }, + { url = "https://files.pythonhosted.org/packages/2d/d1/ef294ca754826daa043b2a104e59960abfab4cf653891037d19dd5b6f3cf/scikit_learn-1.8.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:4511be56637e46c25721e83d1a9cea9614e7badc7040c4d573d75fbe257d6fd7", size = 8848305, upload-time = "2025-12-10T07:08:41.013Z" }, + { url = "https://files.pythonhosted.org/packages/5b/e2/b1f8b05138ee813b8e1a4149f2f0d289547e60851fd1bb268886915adbda/scikit_learn-1.8.0-cp314-cp314t-macosx_12_0_arm64.whl", hash = "sha256:a69525355a641bf8ef136a7fa447672fb54fe8d60cab5538d9eb7c6438543fb9", size = 8432257, upload-time = "2025-12-10T07:08:42.873Z" }, + { url = "https://files.pythonhosted.org/packages/26/11/c32b2138a85dcb0c99f6afd13a70a951bfdff8a6ab42d8160522542fb647/scikit_learn-1.8.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c2656924ec73e5939c76ac4c8b026fc203b83d8900362eb2599d8aee80e4880f", size = 8678673, upload-time = "2025-12-10T07:08:45.362Z" }, + { url = "https://files.pythonhosted.org/packages/c7/57/51f2384575bdec454f4fe4e7a919d696c9ebce914590abf3e52d47607ab8/scikit_learn-1.8.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:15fc3b5d19cc2be65404786857f2e13c70c83dd4782676dd6814e3b89dc8f5b9", size = 8922467, upload-time = "2025-12-10T07:08:47.408Z" }, + { url = "https://files.pythonhosted.org/packages/35/4d/748c9e2872637a57981a04adc038dacaa16ba8ca887b23e34953f0b3f742/scikit_learn-1.8.0-cp314-cp314t-win_amd64.whl", hash = "sha256:00d6f1d66fbcf4eba6e356e1420d33cc06c70a45bb1363cd6f6a8e4ebbbdece2", size = 8774395, upload-time = "2025-12-10T07:08:49.337Z" }, + { url = "https://files.pythonhosted.org/packages/60/22/d7b2ebe4704a5e50790ba089d5c2ae308ab6bb852719e6c3bd4f04c3a363/scikit_learn-1.8.0-cp314-cp314t-win_arm64.whl", hash = "sha256:f28dd15c6bb0b66ba09728cf09fd8736c304be29409bd8445a080c1280619e8c", size = 8002647, upload-time = "2025-12-10T07:08:51.601Z" }, +] + +[[package]] +name = "scipy" +version = "1.17.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "numpy" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/7a/97/5a3609c4f8d58b039179648e62dd220f89864f56f7357f5d4f45c29eb2cc/scipy-1.17.1.tar.gz", hash = "sha256:95d8e012d8cb8816c226aef832200b1d45109ed4464303e997c5b13122b297c0", size = 30573822, upload-time = "2026-02-23T00:26:24.851Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/76/27/07ee1b57b65e92645f219b37148a7e7928b82e2b5dbeccecb4dff7c64f0b/scipy-1.17.1-cp313-cp313-macosx_10_14_x86_64.whl", hash = "sha256:5e3c5c011904115f88a39308379c17f91546f77c1667cea98739fe0fccea804c", size = 31590199, upload-time = "2026-02-23T00:19:17.192Z" }, + { url = "https://files.pythonhosted.org/packages/ec/ae/db19f8ab842e9b724bf5dbb7db29302a91f1e55bc4d04b1025d6d605a2c5/scipy-1.17.1-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:6fac755ca3d2c3edcb22f479fceaa241704111414831ddd3bc6056e18516892f", size = 28154001, upload-time = "2026-02-23T00:19:22.241Z" }, + { url = "https://files.pythonhosted.org/packages/5b/58/3ce96251560107b381cbd6e8413c483bbb1228a6b919fa8652b0d4090e7f/scipy-1.17.1-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:7ff200bf9d24f2e4d5dc6ee8c3ac64d739d3a89e2326ba68aaf6c4a2b838fd7d", size = 20325719, upload-time = "2026-02-23T00:19:26.329Z" }, + { url = "https://files.pythonhosted.org/packages/b2/83/15087d945e0e4d48ce2377498abf5ad171ae013232ae31d06f336e64c999/scipy-1.17.1-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:4b400bdc6f79fa02a4d86640310dde87a21fba0c979efff5248908c6f15fad1b", size = 22683595, upload-time = "2026-02-23T00:19:30.304Z" }, + { url = "https://files.pythonhosted.org/packages/b4/e0/e58fbde4a1a594c8be8114eb4aac1a55bcd6587047efc18a61eb1f5c0d30/scipy-1.17.1-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2b64ca7d4aee0102a97f3ba22124052b4bd2152522355073580bf4845e2550b6", size = 32896429, upload-time = "2026-02-23T00:19:35.536Z" }, + { url = "https://files.pythonhosted.org/packages/f5/5f/f17563f28ff03c7b6799c50d01d5d856a1d55f2676f537ca8d28c7f627cd/scipy-1.17.1-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:581b2264fc0aa555f3f435a5944da7504ea3a065d7029ad60e7c3d1ae09c5464", size = 35203952, upload-time = "2026-02-23T00:19:42.259Z" }, + { url = "https://files.pythonhosted.org/packages/8d/a5/9afd17de24f657fdfe4df9a3f1ea049b39aef7c06000c13db1530d81ccca/scipy-1.17.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:beeda3d4ae615106d7094f7e7cef6218392e4465cc95d25f900bebabfded0950", size = 34979063, upload-time = "2026-02-23T00:19:47.547Z" }, + { url = "https://files.pythonhosted.org/packages/8b/13/88b1d2384b424bf7c924f2038c1c409f8d88bb2a8d49d097861dd64a57b2/scipy-1.17.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:6609bc224e9568f65064cfa72edc0f24ee6655b47575954ec6339534b2798369", size = 37598449, upload-time = "2026-02-23T00:19:53.238Z" }, + { url = "https://files.pythonhosted.org/packages/35/e5/d6d0e51fc888f692a35134336866341c08655d92614f492c6860dc45bb2c/scipy-1.17.1-cp313-cp313-win_amd64.whl", hash = "sha256:37425bc9175607b0268f493d79a292c39f9d001a357bebb6b88fdfaff13f6448", size = 36510943, upload-time = "2026-02-23T00:20:50.89Z" }, + { url = "https://files.pythonhosted.org/packages/2a/fd/3be73c564e2a01e690e19cc618811540ba5354c67c8680dce3281123fb79/scipy-1.17.1-cp313-cp313-win_arm64.whl", hash = "sha256:5cf36e801231b6a2059bf354720274b7558746f3b1a4efb43fcf557ccd484a87", size = 24545621, upload-time = "2026-02-23T00:20:55.871Z" }, + { url = "https://files.pythonhosted.org/packages/6f/6b/17787db8b8114933a66f9dcc479a8272e4b4da75fe03b0c282f7b0ade8cd/scipy-1.17.1-cp313-cp313t-macosx_10_14_x86_64.whl", hash = "sha256:d59c30000a16d8edc7e64152e30220bfbd724c9bbb08368c054e24c651314f0a", size = 31936708, upload-time = "2026-02-23T00:19:58.694Z" }, + { url = "https://files.pythonhosted.org/packages/38/2e/524405c2b6392765ab1e2b722a41d5da33dc5c7b7278184a8ad29b6cb206/scipy-1.17.1-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:010f4333c96c9bb1a4516269e33cb5917b08ef2166d5556ca2fd9f082a9e6ea0", size = 28570135, upload-time = "2026-02-23T00:20:03.934Z" }, + { url = "https://files.pythonhosted.org/packages/fd/c3/5bd7199f4ea8556c0c8e39f04ccb014ac37d1468e6cfa6a95c6b3562b76e/scipy-1.17.1-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:2ceb2d3e01c5f1d83c4189737a42d9cb2fc38a6eeed225e7515eef71ad301dce", size = 20741977, upload-time = "2026-02-23T00:20:07.935Z" }, + { url = "https://files.pythonhosted.org/packages/d9/b8/8ccd9b766ad14c78386599708eb745f6b44f08400a5fd0ade7cf89b6fc93/scipy-1.17.1-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:844e165636711ef41f80b4103ed234181646b98a53c8f05da12ca5ca289134f6", size = 23029601, upload-time = "2026-02-23T00:20:12.161Z" }, + { url = "https://files.pythonhosted.org/packages/6d/a0/3cb6f4d2fb3e17428ad2880333cac878909ad1a89f678527b5328b93c1d4/scipy-1.17.1-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:158dd96d2207e21c966063e1635b1063cd7787b627b6f07305315dd73d9c679e", size = 33019667, upload-time = "2026-02-23T00:20:17.208Z" }, + { url = "https://files.pythonhosted.org/packages/f3/c3/2d834a5ac7bf3a0c806ad1508efc02dda3c8c61472a56132d7894c312dea/scipy-1.17.1-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:74cbb80d93260fe2ffa334efa24cb8f2f0f622a9b9febf8b483c0b865bfb3475", size = 35264159, upload-time = "2026-02-23T00:20:23.087Z" }, + { url = "https://files.pythonhosted.org/packages/4d/77/d3ed4becfdbd217c52062fafe35a72388d1bd82c2d0ba5ca19d6fcc93e11/scipy-1.17.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:dbc12c9f3d185f5c737d801da555fb74b3dcfa1a50b66a1a93e09190f41fab50", size = 35102771, upload-time = "2026-02-23T00:20:28.636Z" }, + { url = "https://files.pythonhosted.org/packages/bd/12/d19da97efde68ca1ee5538bb261d5d2c062f0c055575128f11a2730e3ac1/scipy-1.17.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:94055a11dfebe37c656e70317e1996dc197e1a15bbcc351bcdd4610e128fe1ca", size = 37665910, upload-time = "2026-02-23T00:20:34.743Z" }, + { url = "https://files.pythonhosted.org/packages/06/1c/1172a88d507a4baaf72c5a09bb6c018fe2ae0ab622e5830b703a46cc9e44/scipy-1.17.1-cp313-cp313t-win_amd64.whl", hash = "sha256:e30bdeaa5deed6bc27b4cc490823cd0347d7dae09119b8803ae576ea0ce52e4c", size = 36562980, upload-time = "2026-02-23T00:20:40.575Z" }, + { url = "https://files.pythonhosted.org/packages/70/b0/eb757336e5a76dfa7911f63252e3b7d1de00935d7705cf772db5b45ec238/scipy-1.17.1-cp313-cp313t-win_arm64.whl", hash = "sha256:a720477885a9d2411f94a93d16f9d89bad0f28ca23c3f8daa521e2dcc3f44d49", size = 24856543, upload-time = "2026-02-23T00:20:45.313Z" }, + { url = "https://files.pythonhosted.org/packages/cf/83/333afb452af6f0fd70414dc04f898647ee1423979ce02efa75c3b0f2c28e/scipy-1.17.1-cp314-cp314-macosx_10_14_x86_64.whl", hash = "sha256:a48a72c77a310327f6a3a920092fa2b8fd03d7deaa60f093038f22d98e096717", size = 31584510, upload-time = "2026-02-23T00:21:01.015Z" }, + { url = "https://files.pythonhosted.org/packages/ed/a6/d05a85fd51daeb2e4ea71d102f15b34fedca8e931af02594193ae4fd25f7/scipy-1.17.1-cp314-cp314-macosx_12_0_arm64.whl", hash = "sha256:45abad819184f07240d8a696117a7aacd39787af9e0b719d00285549ed19a1e9", size = 28170131, upload-time = "2026-02-23T00:21:05.888Z" }, + { url = "https://files.pythonhosted.org/packages/db/7b/8624a203326675d7746a254083a187398090a179335b2e4a20e2ddc46e83/scipy-1.17.1-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:3fd1fcdab3ea951b610dc4cef356d416d5802991e7e32b5254828d342f7b7e0b", size = 20342032, upload-time = "2026-02-23T00:21:09.904Z" }, + { url = "https://files.pythonhosted.org/packages/c9/35/2c342897c00775d688d8ff3987aced3426858fd89d5a0e26e020b660b301/scipy-1.17.1-cp314-cp314-macosx_14_0_x86_64.whl", hash = "sha256:7bdf2da170b67fdf10bca777614b1c7d96ae3ca5794fd9587dce41eb2966e866", size = 22678766, upload-time = "2026-02-23T00:21:14.313Z" }, + { url = "https://files.pythonhosted.org/packages/ef/f2/7cdb8eb308a1a6ae1e19f945913c82c23c0c442a462a46480ce487fdc0ac/scipy-1.17.1-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:adb2642e060a6549c343603a3851ba76ef0b74cc8c079a9a58121c7ec9fe2350", size = 32957007, upload-time = "2026-02-23T00:21:19.663Z" }, + { url = "https://files.pythonhosted.org/packages/0b/2e/7eea398450457ecb54e18e9d10110993fa65561c4f3add5e8eccd2b9cd41/scipy-1.17.1-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:eee2cfda04c00a857206a4330f0c5e3e56535494e30ca445eb19ec624ae75118", size = 35221333, upload-time = "2026-02-23T00:21:25.278Z" }, + { url = "https://files.pythonhosted.org/packages/d9/77/5b8509d03b77f093a0d52e606d3c4f79e8b06d1d38c441dacb1e26cacf46/scipy-1.17.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:d2650c1fb97e184d12d8ba010493ee7b322864f7d3d00d3f9bb97d9c21de4068", size = 35042066, upload-time = "2026-02-23T00:21:31.358Z" }, + { url = "https://files.pythonhosted.org/packages/f9/df/18f80fb99df40b4070328d5ae5c596f2f00fffb50167e31439e932f29e7d/scipy-1.17.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:08b900519463543aa604a06bec02461558a6e1cef8fdbb8098f77a48a83c8118", size = 37612763, upload-time = "2026-02-23T00:21:37.247Z" }, + { url = "https://files.pythonhosted.org/packages/4b/39/f0e8ea762a764a9dc52aa7dabcfad51a354819de1f0d4652b6a1122424d6/scipy-1.17.1-cp314-cp314-win_amd64.whl", hash = "sha256:3877ac408e14da24a6196de0ddcace62092bfc12a83823e92e49e40747e52c19", size = 37290984, upload-time = "2026-02-23T00:22:35.023Z" }, + { url = "https://files.pythonhosted.org/packages/7c/56/fe201e3b0f93d1a8bcf75d3379affd228a63d7e2d80ab45467a74b494947/scipy-1.17.1-cp314-cp314-win_arm64.whl", hash = "sha256:f8885db0bc2bffa59d5c1b72fad7a6a92d3e80e7257f967dd81abb553a90d293", size = 25192877, upload-time = "2026-02-23T00:22:39.798Z" }, + { url = "https://files.pythonhosted.org/packages/96/ad/f8c414e121f82e02d76f310f16db9899c4fcde36710329502a6b2a3c0392/scipy-1.17.1-cp314-cp314t-macosx_10_14_x86_64.whl", hash = "sha256:1cc682cea2ae55524432f3cdff9e9a3be743d52a7443d0cba9017c23c87ae2f6", size = 31949750, upload-time = "2026-02-23T00:21:42.289Z" }, + { url = "https://files.pythonhosted.org/packages/7c/b0/c741e8865d61b67c81e255f4f0a832846c064e426636cd7de84e74d209be/scipy-1.17.1-cp314-cp314t-macosx_12_0_arm64.whl", hash = "sha256:2040ad4d1795a0ae89bfc7e8429677f365d45aa9fd5e4587cf1ea737f927b4a1", size = 28585858, upload-time = "2026-02-23T00:21:47.706Z" }, + { url = "https://files.pythonhosted.org/packages/ed/1b/3985219c6177866628fa7c2595bfd23f193ceebbe472c98a08824b9466ff/scipy-1.17.1-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:131f5aaea57602008f9822e2115029b55d4b5f7c070287699fe45c661d051e39", size = 20757723, upload-time = "2026-02-23T00:21:52.039Z" }, + { url = "https://files.pythonhosted.org/packages/c0/19/2a04aa25050d656d6f7b9e7b685cc83d6957fb101665bfd9369ca6534563/scipy-1.17.1-cp314-cp314t-macosx_14_0_x86_64.whl", hash = "sha256:9cdc1a2fcfd5c52cfb3045feb399f7b3ce822abdde3a193a6b9a60b3cb5854ca", size = 23043098, upload-time = "2026-02-23T00:21:56.185Z" }, + { url = "https://files.pythonhosted.org/packages/86/f1/3383beb9b5d0dbddd030335bf8a8b32d4317185efe495374f134d8be6cce/scipy-1.17.1-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6e3dcd57ab780c741fde8dc68619de988b966db759a3c3152e8e9142c26295ad", size = 33030397, upload-time = "2026-02-23T00:22:01.404Z" }, + { url = "https://files.pythonhosted.org/packages/41/68/8f21e8a65a5a03f25a79165ec9d2b28c00e66dc80546cf5eb803aeeff35b/scipy-1.17.1-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a9956e4d4f4a301ebf6cde39850333a6b6110799d470dbbb1e25326ac447f52a", size = 35281163, upload-time = "2026-02-23T00:22:07.024Z" }, + { url = "https://files.pythonhosted.org/packages/84/8d/c8a5e19479554007a5632ed7529e665c315ae7492b4f946b0deb39870e39/scipy-1.17.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:a4328d245944d09fd639771de275701ccadf5f781ba0ff092ad141e017eccda4", size = 35116291, upload-time = "2026-02-23T00:22:12.585Z" }, + { url = "https://files.pythonhosted.org/packages/52/52/e57eceff0e342a1f50e274264ed47497b59e6a4e3118808ee58ddda7b74a/scipy-1.17.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:a77cbd07b940d326d39a1d1b37817e2ee4d79cb30e7338f3d0cddffae70fcaa2", size = 37682317, upload-time = "2026-02-23T00:22:18.513Z" }, + { url = "https://files.pythonhosted.org/packages/11/2f/b29eafe4a3fbc3d6de9662b36e028d5f039e72d345e05c250e121a230dd4/scipy-1.17.1-cp314-cp314t-win_amd64.whl", hash = "sha256:eb092099205ef62cd1782b006658db09e2fed75bffcae7cc0d44052d8aa0f484", size = 37345327, upload-time = "2026-02-23T00:22:24.442Z" }, + { url = "https://files.pythonhosted.org/packages/07/39/338d9219c4e87f3e708f18857ecd24d22a0c3094752393319553096b98af/scipy-1.17.1-cp314-cp314t-win_arm64.whl", hash = "sha256:200e1050faffacc162be6a486a984a0497866ec54149a01270adc8a59b7c7d21", size = 25489165, upload-time = "2026-02-23T00:22:29.563Z" }, +] + +[[package]] +name = "send2trash" +version = "2.1.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/c5/f0/184b4b5f8d00f2a92cf96eec8967a3d550b52cf94362dad1100df9e48d57/send2trash-2.1.0.tar.gz", hash = "sha256:1c72b39f09457db3c05ce1d19158c2cbef4c32b8bedd02c155e49282b7ea7459", size = 17255, upload-time = "2026-01-14T06:27:36.056Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/1c/78/504fdd027da3b84ff1aecd9f6957e65f35134534ccc6da8628eb71e76d3f/send2trash-2.1.0-py3-none-any.whl", hash = "sha256:0da2f112e6d6bb22de6aa6daa7e144831a4febf2a87261451c4ad849fe9a873c", size = 17610, upload-time = "2026-01-14T06:27:35.218Z" }, +] + +[[package]] +name = "sentencepiece" +version = "0.2.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/15/15/2e7a025fc62d764b151ae6d0f2a92f8081755ebe8d4a64099accc6f77ba6/sentencepiece-0.2.1.tar.gz", hash = "sha256:8138cec27c2f2282f4a34d9a016e3374cd40e5c6e9cb335063db66a0a3b71fad", size = 3228515, upload-time = "2025-08-12T07:00:51.718Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ba/4a/85fbe1706d4d04a7e826b53f327c4b80f849cf1c7b7c5e31a20a97d8f28b/sentencepiece-0.2.1-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:dcd8161eee7b41aae57ded06272905dbd680a0a04b91edd0f64790c796b2f706", size = 1943150, upload-time = "2025-08-12T06:59:53.588Z" }, + { url = "https://files.pythonhosted.org/packages/c2/83/4cfb393e287509fc2155480b9d184706ef8d9fa8cbf5505d02a5792bf220/sentencepiece-0.2.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:c6c8f42949f419ff8c7e9960dbadcfbc982d7b5efc2f6748210d3dd53a7de062", size = 1325651, upload-time = "2025-08-12T06:59:55.073Z" }, + { url = "https://files.pythonhosted.org/packages/8d/de/5a007fb53b1ab0aafc69d11a5a3dd72a289d5a3e78dcf2c3a3d9b14ffe93/sentencepiece-0.2.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:097f3394e99456e9e4efba1737c3749d7e23563dd1588ce71a3d007f25475fff", size = 1253641, upload-time = "2025-08-12T06:59:56.562Z" }, + { url = "https://files.pythonhosted.org/packages/2c/d2/f552be5928105588f4f4d66ee37dd4c61460d8097e62d0e2e0eec41bc61d/sentencepiece-0.2.1-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d7b670879c370d350557edabadbad1f6561a9e6968126e6debca4029e5547820", size = 1316271, upload-time = "2025-08-12T06:59:58.109Z" }, + { url = "https://files.pythonhosted.org/packages/96/df/0cfe748ace5485be740fed9476dee7877f109da32ed0d280312c94ec259f/sentencepiece-0.2.1-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c7f0fd2f2693309e6628aeeb2e2faf6edd221134dfccac3308ca0de01f8dab47", size = 1387882, upload-time = "2025-08-12T07:00:00.701Z" }, + { url = "https://files.pythonhosted.org/packages/ac/dd/f7774d42a881ced8e1739f393ab1e82ece39fc9abd4779e28050c2e975b5/sentencepiece-0.2.1-cp313-cp313-win32.whl", hash = "sha256:92b3816aa2339355fda2c8c4e021a5de92180b00aaccaf5e2808972e77a4b22f", size = 999541, upload-time = "2025-08-12T07:00:02.709Z" }, + { url = "https://files.pythonhosted.org/packages/dd/e9/932b9eae6fd7019548321eee1ab8d5e3b3d1294df9d9a0c9ac517c7b636d/sentencepiece-0.2.1-cp313-cp313-win_amd64.whl", hash = "sha256:10ed3dab2044c47f7a2e7b4969b0c430420cdd45735d78c8f853191fa0e3148b", size = 1054669, upload-time = "2025-08-12T07:00:04.915Z" }, + { url = "https://files.pythonhosted.org/packages/c9/3a/76488a00ea7d6931689cda28726a1447d66bf1a4837943489314593d5596/sentencepiece-0.2.1-cp313-cp313-win_arm64.whl", hash = "sha256:ac650534e2251083c5f75dde4ff28896ce7c8904133dc8fef42780f4d5588fcd", size = 1033922, upload-time = "2025-08-12T07:00:06.496Z" }, + { url = "https://files.pythonhosted.org/packages/4a/b6/08fe2ce819e02ccb0296f4843e3f195764ce9829cbda61b7513f29b95718/sentencepiece-0.2.1-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:8dd4b477a7b069648d19363aad0cab9bad2f4e83b2d179be668efa672500dc94", size = 1946052, upload-time = "2025-08-12T07:00:08.136Z" }, + { url = "https://files.pythonhosted.org/packages/ab/d9/1ea0e740591ff4c6fc2b6eb1d7510d02f3fb885093f19b2f3abd1363b402/sentencepiece-0.2.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:0c0f672da370cc490e4c59d89e12289778310a0e71d176c541e4834759e1ae07", size = 1327408, upload-time = "2025-08-12T07:00:09.572Z" }, + { url = "https://files.pythonhosted.org/packages/99/7e/1fb26e8a21613f6200e1ab88824d5d203714162cf2883248b517deb500b7/sentencepiece-0.2.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:ad8493bea8432dae8d6830365352350f3b4144415a1d09c4c8cb8d30cf3b6c3c", size = 1254857, upload-time = "2025-08-12T07:00:11.021Z" }, + { url = "https://files.pythonhosted.org/packages/bc/85/c72fd1f3c7a6010544d6ae07f8ddb38b5e2a7e33bd4318f87266c0bbafbf/sentencepiece-0.2.1-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b81a24733726e3678d2db63619acc5a8dccd074f7aa7a54ecd5ca33ca6d2d596", size = 1315722, upload-time = "2025-08-12T07:00:12.989Z" }, + { url = "https://files.pythonhosted.org/packages/4a/e8/661e5bd82a8aa641fd6c1020bd0e890ef73230a2b7215ddf9c8cd8e941c2/sentencepiece-0.2.1-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0a81799d0a68d618e89063fb423c3001a034c893069135ffe51fee439ae474d6", size = 1387452, upload-time = "2025-08-12T07:00:15.088Z" }, + { url = "https://files.pythonhosted.org/packages/99/5e/ae66c361023a470afcbc1fbb8da722c72ea678a2fcd9a18f1a12598c7501/sentencepiece-0.2.1-cp313-cp313t-win32.whl", hash = "sha256:89a3ea015517c42c0341d0d962f3e6aaf2cf10d71b1932d475c44ba48d00aa2b", size = 1002501, upload-time = "2025-08-12T07:00:16.966Z" }, + { url = "https://files.pythonhosted.org/packages/c1/03/d332828c4ff764e16c1b56c2c8f9a33488bbe796b53fb6b9c4205ddbf167/sentencepiece-0.2.1-cp313-cp313t-win_amd64.whl", hash = "sha256:33f068c9382dc2e7c228eedfd8163b52baa86bb92f50d0488bf2b7da7032e484", size = 1057555, upload-time = "2025-08-12T07:00:18.573Z" }, + { url = "https://files.pythonhosted.org/packages/88/14/5aee0bf0864df9bd82bd59e7711362908e4935e3f9cdc1f57246b5d5c9b9/sentencepiece-0.2.1-cp313-cp313t-win_arm64.whl", hash = "sha256:b3616ad246f360e52c85781e47682d31abfb6554c779e42b65333d4b5f44ecc0", size = 1036042, upload-time = "2025-08-12T07:00:20.209Z" }, + { url = "https://files.pythonhosted.org/packages/24/9c/89eb8b2052f720a612478baf11c8227dcf1dc28cd4ea4c0c19506b5af2a2/sentencepiece-0.2.1-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:5d0350b686c320068702116276cfb26c066dc7e65cfef173980b11bb4d606719", size = 1943147, upload-time = "2025-08-12T07:00:21.809Z" }, + { url = "https://files.pythonhosted.org/packages/82/0b/a1432bc87f97c2ace36386ca23e8bd3b91fb40581b5e6148d24b24186419/sentencepiece-0.2.1-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:c7f54a31cde6fa5cb030370566f68152a742f433f8d2be458463d06c208aef33", size = 1325624, upload-time = "2025-08-12T07:00:23.289Z" }, + { url = "https://files.pythonhosted.org/packages/ea/99/bbe054ebb5a5039457c590e0a4156ed073fb0fe9ce4f7523404dd5b37463/sentencepiece-0.2.1-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:c83b85ab2d6576607f31df77ff86f28182be4a8de6d175d2c33ca609925f5da1", size = 1253670, upload-time = "2025-08-12T07:00:24.69Z" }, + { url = "https://files.pythonhosted.org/packages/19/ad/d5c7075f701bd97971d7c2ac2904f227566f51ef0838dfbdfdccb58cd212/sentencepiece-0.2.1-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1855f57db07b51fb51ed6c9c452f570624d2b169b36f0f79ef71a6e6c618cd8b", size = 1316247, upload-time = "2025-08-12T07:00:26.435Z" }, + { url = "https://files.pythonhosted.org/packages/fb/03/35fbe5f3d9a7435eebd0b473e09584bd3cc354ce118b960445b060d33781/sentencepiece-0.2.1-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:01e6912125cb45d3792f530a4d38f8e21bf884d6b4d4ade1b2de5cf7a8d2a52b", size = 1387894, upload-time = "2025-08-12T07:00:28.339Z" }, + { url = "https://files.pythonhosted.org/packages/dc/aa/956ef729aafb6c8f9c443104c9636489093bb5c61d6b90fc27aa1a865574/sentencepiece-0.2.1-cp314-cp314-win32.whl", hash = "sha256:c415c9de1447e0a74ae3fdb2e52f967cb544113a3a5ce3a194df185cbc1f962f", size = 1096698, upload-time = "2025-08-12T07:00:29.764Z" }, + { url = "https://files.pythonhosted.org/packages/b8/cb/fe400d8836952cc535c81a0ce47dc6875160e5fedb71d2d9ff0e9894c2a6/sentencepiece-0.2.1-cp314-cp314-win_amd64.whl", hash = "sha256:881b2e44b14fc19feade3cbed314be37de639fc415375cefaa5bc81a4be137fd", size = 1155115, upload-time = "2025-08-12T07:00:32.865Z" }, + { url = "https://files.pythonhosted.org/packages/32/89/047921cf70f36c7b6b6390876b2399b3633ab73b8d0cb857e5a964238941/sentencepiece-0.2.1-cp314-cp314-win_arm64.whl", hash = "sha256:2005242a16d2dc3ac5fe18aa7667549134d37854823df4c4db244752453b78a8", size = 1133890, upload-time = "2025-08-12T07:00:34.763Z" }, + { url = "https://files.pythonhosted.org/packages/a1/11/5b414b9fae6255b5fb1e22e2ed3dc3a72d3a694e5703910e640ac78346bb/sentencepiece-0.2.1-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:a19adcec27c524cb7069a1c741060add95f942d1cbf7ad0d104dffa0a7d28a2b", size = 1946081, upload-time = "2025-08-12T07:00:36.97Z" }, + { url = "https://files.pythonhosted.org/packages/77/eb/7a5682bb25824db8545f8e5662e7f3e32d72a508fdce086029d89695106b/sentencepiece-0.2.1-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:e37e4b4c4a11662b5db521def4e44d4d30ae69a1743241412a93ae40fdcab4bb", size = 1327406, upload-time = "2025-08-12T07:00:38.669Z" }, + { url = "https://files.pythonhosted.org/packages/03/b0/811dae8fb9f2784e138785d481469788f2e0d0c109c5737372454415f55f/sentencepiece-0.2.1-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:477c81505db072b3ab627e7eab972ea1025331bd3a92bacbf798df2b75ea86ec", size = 1254846, upload-time = "2025-08-12T07:00:40.611Z" }, + { url = "https://files.pythonhosted.org/packages/ef/23/195b2e7ec85ebb6a547969f60b723c7aca5a75800ece6cc3f41da872d14e/sentencepiece-0.2.1-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:010f025a544ef770bb395091d57cb94deb9652d8972e0d09f71d85d5a0816c8c", size = 1315721, upload-time = "2025-08-12T07:00:42.914Z" }, + { url = "https://files.pythonhosted.org/packages/7e/aa/553dbe4178b5f23eb28e59393dddd64186178b56b81d9b8d5c3ff1c28395/sentencepiece-0.2.1-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:733e59ff1794d26db706cd41fc2d7ca5f6c64a820709cb801dc0ea31780d64ab", size = 1387458, upload-time = "2025-08-12T07:00:44.56Z" }, + { url = "https://files.pythonhosted.org/packages/66/7c/08ff0012507297a4dd74a5420fdc0eb9e3e80f4e88cab1538d7f28db303d/sentencepiece-0.2.1-cp314-cp314t-win32.whl", hash = "sha256:d3233770f78e637dc8b1fda2cd7c3b99ec77e7505041934188a4e7fe751de3b0", size = 1099765, upload-time = "2025-08-12T07:00:46.058Z" }, + { url = "https://files.pythonhosted.org/packages/91/d5/2a69e1ce15881beb9ddfc7e3f998322f5cedcd5e4d244cb74dade9441663/sentencepiece-0.2.1-cp314-cp314t-win_amd64.whl", hash = "sha256:5e4366c97b68218fd30ea72d70c525e6e78a6c0a88650f57ac4c43c63b234a9d", size = 1157807, upload-time = "2025-08-12T07:00:47.673Z" }, + { url = "https://files.pythonhosted.org/packages/f3/16/54f611fcfc2d1c46cbe3ec4169780b2cfa7cf63708ef2b71611136db7513/sentencepiece-0.2.1-cp314-cp314t-win_arm64.whl", hash = "sha256:105e36e75cbac1292642045458e8da677b2342dcd33df503e640f0b457cb6751", size = 1136264, upload-time = "2025-08-12T07:00:49.485Z" }, +] + +[[package]] +name = "seqeval" +version = "1.2.2" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "numpy" }, + { name = "scikit-learn" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/9d/2d/233c79d5b4e5ab1dbf111242299153f3caddddbb691219f363ad55ce783d/seqeval-1.2.2.tar.gz", hash = "sha256:f28e97c3ab96d6fcd32b648f6438ff2e09cfba87f05939da9b3970713ec56e6f", size = 43605, upload-time = "2020-10-24T00:24:54.926Z" } + +[[package]] +name = "setuptools" +version = "81.0.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/0d/1c/73e719955c59b8e424d015ab450f51c0af856ae46ea2da83eba51cc88de1/setuptools-81.0.0.tar.gz", hash = "sha256:487b53915f52501f0a79ccfd0c02c165ffe06631443a886740b91af4b7a5845a", size = 1198299, upload-time = "2026-02-06T21:10:39.601Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e1/e3/c164c88b2e5ce7b24d667b9bd83589cf4f3520d97cad01534cd3c4f55fdb/setuptools-81.0.0-py3-none-any.whl", hash = "sha256:fdd925d5c5d9f62e4b74b30d6dd7828ce236fd6ed998a08d81de62ce5a6310d6", size = 1062021, upload-time = "2026-02-06T21:10:37.175Z" }, +] + +[[package]] +name = "shellingham" +version = "1.5.4" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/58/15/8b3609fd3830ef7b27b655beb4b4e9c62313a4e8da8c676e142cc210d58e/shellingham-1.5.4.tar.gz", hash = "sha256:8dbca0739d487e5bd35ab3ca4b36e11c4078f3a234bfce294b0a0291363404de", size = 10310, upload-time = "2023-10-24T04:13:40.426Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e0/f9/0595336914c5619e5f28a1fb793285925a8cd4b432c9da0a987836c7f822/shellingham-1.5.4-py2.py3-none-any.whl", hash = "sha256:7ecfff8f2fd72616f7481040475a65b2bf8af90a56c89140852d1120324e8686", size = 9755, upload-time = "2023-10-24T04:13:38.866Z" }, +] + +[[package]] +name = "six" +version = "1.17.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/94/e7/b2c673351809dca68a0e064b6af791aa332cf192da575fd474ed7d6f16a2/six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81", size = 34031, upload-time = "2024-12-04T17:35:28.174Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274", size = 11050, upload-time = "2024-12-04T17:35:26.475Z" }, +] + +[[package]] +name = "soupsieve" +version = "2.8.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/7b/ae/2d9c981590ed9999a0d91755b47fc74f74de286b0f5cee14c9269041e6c4/soupsieve-2.8.3.tar.gz", hash = "sha256:3267f1eeea4251fb42728b6dfb746edc9acaffc4a45b27e19450b676586e8349", size = 118627, upload-time = "2026-01-20T04:27:02.457Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/46/2c/1462b1d0a634697ae9e55b3cecdcb64788e8b7d63f54d923fcd0bb140aed/soupsieve-2.8.3-py3-none-any.whl", hash = "sha256:ed64f2ba4eebeab06cc4962affce381647455978ffc1e36bb79a545b91f45a95", size = 37016, upload-time = "2026-01-20T04:27:01.012Z" }, +] + +[[package]] +name = "sqlite-anyio" +version = "0.2.8" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "anyio" }, + { name = "typing-extensions" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/20/a3/7830c95b37f1268dbb47e559d1f1ae027f3a4c36b1f7fc1b2dc5de1c5073/sqlite_anyio-0.2.8.tar.gz", hash = "sha256:d68b51a18c01a7dfa9cedbc319871ce77ab3ed0822518fb32810bb465b52d761", size = 3271, upload-time = "2026-03-02T10:37:43.466Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/7e/aa/182981b92659df83c3eb7d6f8fb0874984d72ad688fa4054cb96bc044bb0/sqlite_anyio-0.2.8-py3-none-any.whl", hash = "sha256:bbdfefb144aed2633d2618ee1508edd3abe67a00389379360949da4671640d86", size = 4041, upload-time = "2026-03-02T10:37:42.246Z" }, +] + +[[package]] +name = "sse-starlette" +version = "3.4.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "anyio" }, + { name = "starlette" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/e1/9a/f35932a8c0eb6b2287b66fa65a0321df8c84e4e355a659c1841a37c39fdb/sse_starlette-3.4.1.tar.gz", hash = "sha256:f780bebcf6c8997fe514e3bd8e8c648d8284976b391c8bed0bcb1f611632b555", size = 35127, upload-time = "2026-04-26T13:32:32.292Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ff/07/45c21ed03d708c477367305726b89919b020a3a2a01f72aaf5ad941caf35/sse_starlette-3.4.1-py3-none-any.whl", hash = "sha256:6b43cf21f1d574d582a6e1b0cfbde1c94dc86a32a701a7168c99c4475c6bd1d0", size = 16487, upload-time = "2026-04-26T13:32:30.819Z" }, +] + +[[package]] +name = "stack-data" +version = "0.6.3" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "asttokens" }, + { name = "executing" }, + { name = "pure-eval" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/28/e3/55dcc2cfbc3ca9c29519eb6884dd1415ecb53b0e934862d3559ddcb7e20b/stack_data-0.6.3.tar.gz", hash = "sha256:836a778de4fec4dcd1dcd89ed8abff8a221f58308462e1c4aa2a3cf30148f0b9", size = 44707, upload-time = "2023-09-30T13:58:05.479Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f1/7b/ce1eafaf1a76852e2ec9b22edecf1daa58175c090266e9f6c64afcd81d91/stack_data-0.6.3-py3-none-any.whl", hash = "sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695", size = 24521, upload-time = "2023-09-30T13:58:03.53Z" }, +] + +[[package]] +name = "starlette" +version = "1.0.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "anyio" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/81/69/17425771797c36cded50b7fe44e850315d039f28b15901ab44839e70b593/starlette-1.0.0.tar.gz", hash = "sha256:6a4beaf1f81bb472fd19ea9b918b50dc3a77a6f2e190a12954b25e6ed5eea149", size = 2655289, upload-time = "2026-03-22T18:29:46.779Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/0b/c9/584bc9651441b4ba60cc4d557d8a547b5aff901af35bda3a4ee30c819b82/starlette-1.0.0-py3-none-any.whl", hash = "sha256:d3ec55e0bb321692d275455ddfd3df75fff145d009685eb40dc91fc66b03d38b", size = 72651, upload-time = "2026-03-22T18:29:45.111Z" }, +] + +[[package]] +name = "sympy" +version = "1.14.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "mpmath" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/83/d3/803453b36afefb7c2bb238361cd4ae6125a569b4db67cd9e79846ba2d68c/sympy-1.14.0.tar.gz", hash = "sha256:d3d3fe8df1e5a0b42f0e7bdf50541697dbe7d23746e894990c030e2b05e72517", size = 7793921, upload-time = "2025-04-27T18:05:01.611Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/a2/09/77d55d46fd61b4a135c444fc97158ef34a095e5681d0a6c10b75bf356191/sympy-1.14.0-py3-none-any.whl", hash = "sha256:e091cc3e99d2141a0ba2847328f5479b05d94a6635cb96148ccb3f34671bd8f5", size = 6299353, upload-time = "2025-04-27T18:04:59.103Z" }, +] + +[[package]] +name = "terminado" +version = "0.18.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "ptyprocess", marker = "os_name != 'nt'" }, + { name = "pywinpty", marker = "os_name == 'nt'" }, + { name = "tornado" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/8a/11/965c6fd8e5cc254f1fe142d547387da17a8ebfd75a3455f637c663fb38a0/terminado-0.18.1.tar.gz", hash = "sha256:de09f2c4b85de4765f7714688fff57d3e75bad1f909b589fde880460c753fd2e", size = 32701, upload-time = "2024-03-12T14:34:39.026Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/6a/9e/2064975477fdc887e47ad42157e214526dcad8f317a948dee17e1659a62f/terminado-0.18.1-py3-none-any.whl", hash = "sha256:a4468e1b37bb318f8a86514f65814e1afc977cf29b3992a4500d9dd305dcceb0", size = 14154, upload-time = "2024-03-12T14:34:36.569Z" }, +] + +[[package]] +name = "threadpoolctl" +version = "3.6.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/b7/4d/08c89e34946fce2aec4fbb45c9016efd5f4d7f24af8e5d93296e935631d8/threadpoolctl-3.6.0.tar.gz", hash = "sha256:8ab8b4aa3491d812b623328249fab5302a68d2d71745c8a4c719a2fcaba9f44e", size = 21274, upload-time = "2025-03-13T13:49:23.031Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/32/d5/f9a850d79b0851d1d4ef6456097579a9005b31fea68726a4ae5f2d82ddd9/threadpoolctl-3.6.0-py3-none-any.whl", hash = "sha256:43a0b8fd5a2928500110039e43a5eed8480b918967083ea48dc3ab9f13c4a7fb", size = 18638, upload-time = "2025-03-13T13:49:21.846Z" }, +] + +[[package]] +name = "tinycss2" +version = "1.4.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "webencodings" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/7a/fd/7a5ee21fd08ff70d3d33a5781c255cbe779659bd03278feb98b19ee550f4/tinycss2-1.4.0.tar.gz", hash = "sha256:10c0972f6fc0fbee87c3edb76549357415e94548c1ae10ebccdea16fb404a9b7", size = 87085, upload-time = "2024-10-24T14:58:29.895Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e6/34/ebdc18bae6aa14fbee1a08b63c015c72b64868ff7dae68808ab500c492e2/tinycss2-1.4.0-py3-none-any.whl", hash = "sha256:3a49cf47b7675da0b15d0c6e1df8df4ebd96e9394bb905a5775adb0d884c5289", size = 26610, upload-time = "2024-10-24T14:58:28.029Z" }, +] + +[[package]] +name = "tokenizers" +version = "0.22.2" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "huggingface-hub" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/73/6f/f80cfef4a312e1fb34baf7d85c72d4411afde10978d4657f8cdd811d3ccc/tokenizers-0.22.2.tar.gz", hash = "sha256:473b83b915e547aa366d1eee11806deaf419e17be16310ac0a14077f1e28f917", size = 372115, upload-time = "2026-01-05T10:45:15.988Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/92/97/5dbfabf04c7e348e655e907ed27913e03db0923abb5dfdd120d7b25630e1/tokenizers-0.22.2-cp39-abi3-macosx_10_12_x86_64.whl", hash = "sha256:544dd704ae7238755d790de45ba8da072e9af3eea688f698b137915ae959281c", size = 3100275, upload-time = "2026-01-05T10:41:02.158Z" }, + { url = "https://files.pythonhosted.org/packages/2e/47/174dca0502ef88b28f1c9e06b73ce33500eedfac7a7692108aec220464e7/tokenizers-0.22.2-cp39-abi3-macosx_11_0_arm64.whl", hash = "sha256:1e418a55456beedca4621dbab65a318981467a2b188e982a23e117f115ce5001", size = 2981472, upload-time = "2026-01-05T10:41:00.276Z" }, + { url = "https://files.pythonhosted.org/packages/d6/84/7990e799f1309a8b87af6b948f31edaa12a3ed22d11b352eaf4f4b2e5753/tokenizers-0.22.2-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2249487018adec45d6e3554c71d46eb39fa8ea67156c640f7513eb26f318cec7", size = 3290736, upload-time = "2026-01-05T10:40:32.165Z" }, + { url = "https://files.pythonhosted.org/packages/78/59/09d0d9ba94dcd5f4f1368d4858d24546b4bdc0231c2354aa31d6199f0399/tokenizers-0.22.2-cp39-abi3-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:25b85325d0815e86e0bac263506dd114578953b7b53d7de09a6485e4a160a7dd", size = 3168835, upload-time = "2026-01-05T10:40:38.847Z" }, + { url = "https://files.pythonhosted.org/packages/47/50/b3ebb4243e7160bda8d34b731e54dd8ab8b133e50775872e7a434e524c28/tokenizers-0.22.2-cp39-abi3-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:bfb88f22a209ff7b40a576d5324bf8286b519d7358663db21d6246fb17eea2d5", size = 3521673, upload-time = "2026-01-05T10:40:56.614Z" }, + { url = "https://files.pythonhosted.org/packages/e0/fa/89f4cb9e08df770b57adb96f8cbb7e22695a4cb6c2bd5f0c4f0ebcf33b66/tokenizers-0.22.2-cp39-abi3-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1c774b1276f71e1ef716e5486f21e76333464f47bece56bbd554485982a9e03e", size = 3724818, upload-time = "2026-01-05T10:40:44.507Z" }, + { url = "https://files.pythonhosted.org/packages/64/04/ca2363f0bfbe3b3d36e95bf67e56a4c88c8e3362b658e616d1ac185d47f2/tokenizers-0.22.2-cp39-abi3-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:df6c4265b289083bf710dff49bc51ef252f9d5be33a45ee2bed151114a56207b", size = 3379195, upload-time = "2026-01-05T10:40:51.139Z" }, + { url = "https://files.pythonhosted.org/packages/2e/76/932be4b50ef6ccedf9d3c6639b056a967a86258c6d9200643f01269211ca/tokenizers-0.22.2-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:369cc9fc8cc10cb24143873a0d95438bb8ee257bb80c71989e3ee290e8d72c67", size = 3274982, upload-time = "2026-01-05T10:40:58.331Z" }, + { url = "https://files.pythonhosted.org/packages/1d/28/5f9f5a4cc211b69e89420980e483831bcc29dade307955cc9dc858a40f01/tokenizers-0.22.2-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:29c30b83d8dcd061078b05ae0cb94d3c710555fbb44861139f9f83dcca3dc3e4", size = 9478245, upload-time = "2026-01-05T10:41:04.053Z" }, + { url = "https://files.pythonhosted.org/packages/6c/fb/66e2da4704d6aadebf8cb39f1d6d1957df667ab24cff2326b77cda0dcb85/tokenizers-0.22.2-cp39-abi3-musllinux_1_2_armv7l.whl", hash = "sha256:37ae80a28c1d3265bb1f22464c856bd23c02a05bb211e56d0c5301a435be6c1a", size = 9560069, upload-time = "2026-01-05T10:45:10.673Z" }, + { url = "https://files.pythonhosted.org/packages/16/04/fed398b05caa87ce9b1a1bb5166645e38196081b225059a6edaff6440fac/tokenizers-0.22.2-cp39-abi3-musllinux_1_2_i686.whl", hash = "sha256:791135ee325f2336f498590eb2f11dc5c295232f288e75c99a36c5dbce63088a", size = 9899263, upload-time = "2026-01-05T10:45:12.559Z" }, + { url = "https://files.pythonhosted.org/packages/05/a1/d62dfe7376beaaf1394917e0f8e93ee5f67fea8fcf4107501db35996586b/tokenizers-0.22.2-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:38337540fbbddff8e999d59970f3c6f35a82de10053206a7562f1ea02d046fa5", size = 10033429, upload-time = "2026-01-05T10:45:14.333Z" }, + { url = "https://files.pythonhosted.org/packages/fd/18/a545c4ea42af3df6effd7d13d250ba77a0a86fb20393143bbb9a92e434d4/tokenizers-0.22.2-cp39-abi3-win32.whl", hash = "sha256:a6bf3f88c554a2b653af81f3204491c818ae2ac6fbc09e76ef4773351292bc92", size = 2502363, upload-time = "2026-01-05T10:45:20.593Z" }, + { url = "https://files.pythonhosted.org/packages/65/71/0670843133a43d43070abeb1949abfdef12a86d490bea9cd9e18e37c5ff7/tokenizers-0.22.2-cp39-abi3-win_amd64.whl", hash = "sha256:c9ea31edff2968b44a88f97d784c2f16dc0729b8b143ed004699ebca91f05c48", size = 2747786, upload-time = "2026-01-05T10:45:18.411Z" }, + { url = "https://files.pythonhosted.org/packages/72/f4/0de46cfa12cdcbcd464cc59fde36912af405696f687e53a091fb432f694c/tokenizers-0.22.2-cp39-abi3-win_arm64.whl", hash = "sha256:9ce725d22864a1e965217204946f830c37876eee3b2ba6fc6255e8e903d5fcbc", size = 2612133, upload-time = "2026-01-05T10:45:17.232Z" }, +] + +[[package]] +name = "torch" +version = "2.11.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "cuda-bindings", marker = "sys_platform == 'linux'" }, + { name = "cuda-toolkit", extra = ["cublas", "cudart", "cufft", "cufile", "cupti", "curand", "cusolver", "cusparse", "nvjitlink", "nvrtc", "nvtx"], marker = "sys_platform == 'linux'" }, + { name = "filelock" }, + { name = "fsspec" }, + { name = "jinja2" }, + { name = "networkx" }, + { name = "nvidia-cudnn-cu13", marker = "sys_platform == 'linux'" }, + { name = "nvidia-cusparselt-cu13", marker = "sys_platform == 'linux'" }, + { name = "nvidia-nccl-cu13", marker = "sys_platform == 'linux'" }, + { name = "nvidia-nvshmem-cu13", marker = "sys_platform == 'linux'" }, + { name = "setuptools" }, + { name = "sympy" }, + { name = "triton", marker = "sys_platform == 'linux'" }, + { name = "typing-extensions" }, +] +wheels = [ + { url = "https://files.pythonhosted.org/packages/87/89/5ea6722763acee56b045435fb84258db7375c48165ec8be7880ab2b281c5/torch-2.11.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:1e6debd97ccd3205bbb37eb806a9d8219e1139d15419982c09e23ef7d4369d18", size = 80606801, upload-time = "2026-03-23T18:10:18.649Z" }, + { url = "https://files.pythonhosted.org/packages/32/d1/8ed2173589cbfe744ed54e5a73efc107c0085ba5777ee93a5f4c1ab90553/torch-2.11.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:63a68fa59de8f87acc7e85a5478bb2dddbb3392b7593ec3e78827c793c4b73fd", size = 419732382, upload-time = "2026-03-23T18:08:30.835Z" }, + { url = "https://files.pythonhosted.org/packages/3d/e1/b73f7c575a4b8f87a5928f50a1e35416b5e27295d8be9397d5293e7e8d4c/torch-2.11.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:cc89b9b173d9adfab59fd227f0ab5e5516d9a52b658ae41d64e59d2e55a418db", size = 530711509, upload-time = "2026-03-23T18:08:47.213Z" }, + { url = "https://files.pythonhosted.org/packages/66/82/3e3fcdd388fbe54e29fd3f991f36846ff4ac90b0d0181e9c8f7236565f82/torch-2.11.0-cp313-cp313-win_amd64.whl", hash = "sha256:4dda3b3f52d121063a731ddb835f010dc137b920d7fec2778e52f60d8e4bf0cd", size = 114555842, upload-time = "2026-03-23T18:09:52.111Z" }, + { url = "https://files.pythonhosted.org/packages/db/38/8ac78069621b8c2b4979c2f96dc8409ef5e9c4189f6aac629189a78677ca/torch-2.11.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:8b394322f49af4362d4f80e424bcaca7efcd049619af03a4cf4501520bdf0fb4", size = 80959574, upload-time = "2026-03-23T18:10:14.214Z" }, + { url = "https://files.pythonhosted.org/packages/6d/6c/56bfb37073e7136e6dd86bfc6af7339946dd684e0ecf2155ac0eee687ae1/torch-2.11.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:2658f34ce7e2dabf4ec73b45e2ca68aedad7a5be87ea756ad656eaf32bf1e1ea", size = 419732324, upload-time = "2026-03-23T18:09:36.604Z" }, + { url = "https://files.pythonhosted.org/packages/07/f4/1b666b6d61d3394cca306ea543ed03a64aad0a201b6cd159f1d41010aeb1/torch-2.11.0-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:98bb213c3084cfe176302949bdc360074b18a9da7ab59ef2edc9d9f742504778", size = 530596026, upload-time = "2026-03-23T18:09:20.842Z" }, + { url = "https://files.pythonhosted.org/packages/48/6b/30d1459fa7e4b67e9e3fe1685ca1d8bb4ce7c62ef436c3a615963c6c866c/torch-2.11.0-cp313-cp313t-win_amd64.whl", hash = "sha256:a97b94bbf62992949b4730c6cd2cc9aee7b335921ee8dc207d930f2ed09ae2db", size = 114793702, upload-time = "2026-03-23T18:09:47.304Z" }, + { url = "https://files.pythonhosted.org/packages/26/0d/8603382f61abd0db35841148ddc1ffd607bf3100b11c6e1dab6d2fc44e72/torch-2.11.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:01018087326984a33b64e04c8cb5c2795f9120e0d775ada1f6638840227b04d7", size = 80573442, upload-time = "2026-03-23T18:09:10.117Z" }, + { url = "https://files.pythonhosted.org/packages/c7/86/7cd7c66cb9cec6be330fff36db5bd0eef386d80c031b581ec81be1d4b26c/torch-2.11.0-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:2bb3cc54bd0dea126b0060bb1ec9de0f9c7f7342d93d436646516b0330cd5be7", size = 419749385, upload-time = "2026-03-23T18:07:33.77Z" }, + { url = "https://files.pythonhosted.org/packages/47/e8/b98ca2d39b2e0e4730c0ee52537e488e7008025bc77ca89552ff91021f7c/torch-2.11.0-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:4dc8b3809469b6c30b411bb8c4cad3828efd26236153d9beb6a3ec500f211a60", size = 530716756, upload-time = "2026-03-23T18:07:50.02Z" }, + { url = "https://files.pythonhosted.org/packages/78/88/d4a4cda8362f8a30d1ed428564878c3cafb0d87971fbd3947d4c84552095/torch-2.11.0-cp314-cp314-win_amd64.whl", hash = "sha256:2b4e811728bd0cc58fb2b0948fe939a1ee2bf1422f6025be2fca4c7bd9d79718", size = 114552300, upload-time = "2026-03-23T18:09:05.617Z" }, + { url = "https://files.pythonhosted.org/packages/bf/46/4419098ed6d801750f26567b478fc185c3432e11e2cad712bc6b4c2ab0d0/torch-2.11.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:8245477871c3700d4370352ffec94b103cfcb737229445cf9946cddb7b2ca7cd", size = 80959460, upload-time = "2026-03-23T18:09:00.818Z" }, + { url = "https://files.pythonhosted.org/packages/fd/66/54a56a4a6ceaffb567231994a9745821d3af922a854ed33b0b3a278e0a99/torch-2.11.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:ab9a8482f475f9ba20e12db84b0e55e2f58784bdca43a854a6ccd3fd4b9f75e6", size = 419735835, upload-time = "2026-03-23T18:07:18.974Z" }, + { url = "https://files.pythonhosted.org/packages/b1/e7/0b6665f533aa9e337662dc190425abc0af1fe3234088f4454c52393ded61/torch-2.11.0-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:563ed3d25542d7e7bbc5b235ccfacfeb97fb470c7fee257eae599adb8005c8a2", size = 530613405, upload-time = "2026-03-23T18:08:07.014Z" }, + { url = "https://files.pythonhosted.org/packages/cf/bf/c8d12a2c86dbfd7f40fb2f56fbf5a505ccf2d9ce131eb559dfc7c51e1a04/torch-2.11.0-cp314-cp314t-win_amd64.whl", hash = "sha256:b2a43985ff5ef6ddd923bbcf99943e5f58059805787c5c9a2622bf05ca2965b0", size = 114792991, upload-time = "2026-03-23T18:08:19.216Z" }, +] + +[[package]] +name = "tornado" +version = "6.5.5" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/f8/f1/3173dfa4a18db4a9b03e5d55325559dab51ee653763bb8745a75af491286/tornado-6.5.5.tar.gz", hash = "sha256:192b8f3ea91bd7f1f50c06955416ed76c6b72f96779b962f07f911b91e8d30e9", size = 516006, upload-time = "2026-03-10T21:31:02.067Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/59/8c/77f5097695f4dd8255ecbd08b2a1ed8ba8b953d337804dd7080f199e12bf/tornado-6.5.5-cp39-abi3-macosx_10_9_universal2.whl", hash = "sha256:487dc9cc380e29f58c7ab88f9e27cdeef04b2140862e5076a66fb6bb68bb1bfa", size = 445983, upload-time = "2026-03-10T21:30:44.28Z" }, + { url = "https://files.pythonhosted.org/packages/ab/5e/7625b76cd10f98f1516c36ce0346de62061156352353ef2da44e5c21523c/tornado-6.5.5-cp39-abi3-macosx_10_9_x86_64.whl", hash = "sha256:65a7f1d46d4bb41df1ac99f5fcb685fb25c7e61613742d5108b010975a9a6521", size = 444246, upload-time = "2026-03-10T21:30:46.571Z" }, + { url = "https://files.pythonhosted.org/packages/b2/04/7b5705d5b3c0fab088f434f9c83edac1573830ca49ccf29fb83bf7178eec/tornado-6.5.5-cp39-abi3-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:e74c92e8e65086b338fd56333fb9a68b9f6f2fe7ad532645a290a464bcf46be5", size = 447229, upload-time = "2026-03-10T21:30:48.273Z" }, + { url = "https://files.pythonhosted.org/packages/34/01/74e034a30ef59afb4097ef8659515e96a39d910b712a89af76f5e4e1f93c/tornado-6.5.5-cp39-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:435319e9e340276428bbdb4e7fa732c2d399386d1de5686cb331ec8eee754f07", size = 448192, upload-time = "2026-03-10T21:30:51.22Z" }, + { url = "https://files.pythonhosted.org/packages/be/00/fe9e02c5a96429fce1a1d15a517f5d8444f9c412e0bb9eadfbe3b0fc55bf/tornado-6.5.5-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:3f54aa540bdbfee7b9eb268ead60e7d199de5021facd276819c193c0fb28ea4e", size = 448039, upload-time = "2026-03-10T21:30:53.52Z" }, + { url = "https://files.pythonhosted.org/packages/82/9e/656ee4cec0398b1d18d0f1eb6372c41c6b889722641d84948351ae19556d/tornado-6.5.5-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:36abed1754faeb80fbd6e64db2758091e1320f6bba74a4cf8c09cd18ccce8aca", size = 447445, upload-time = "2026-03-10T21:30:55.541Z" }, + { url = "https://files.pythonhosted.org/packages/5a/76/4921c00511f88af86a33de770d64141170f1cfd9c00311aea689949e274e/tornado-6.5.5-cp39-abi3-win32.whl", hash = "sha256:dd3eafaaeec1c7f2f8fdcd5f964e8907ad788fe8a5a32c4426fbbdda621223b7", size = 448582, upload-time = "2026-03-10T21:30:57.142Z" }, + { url = "https://files.pythonhosted.org/packages/2c/23/f6c6112a04d28eed765e374435fb1a9198f73e1ec4b4024184f21faeb1ad/tornado-6.5.5-cp39-abi3-win_amd64.whl", hash = "sha256:6443a794ba961a9f619b1ae926a2e900ac20c34483eea67be4ed8f1e58d3ef7b", size = 448990, upload-time = "2026-03-10T21:30:58.857Z" }, + { url = "https://files.pythonhosted.org/packages/b7/c8/876602cbc96469911f0939f703453c1157b0c826ecb05bdd32e023397d4e/tornado-6.5.5-cp39-abi3-win_arm64.whl", hash = "sha256:2c9a876e094109333f888539ddb2de4361743e5d21eece20688e3e351e4990a6", size = 448016, upload-time = "2026-03-10T21:31:00.43Z" }, +] + +[[package]] +name = "tqdm" +version = "4.67.3" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "colorama", marker = "sys_platform == 'win32'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/09/a9/6ba95a270c6f1fbcd8dac228323f2777d886cb206987444e4bce66338dd4/tqdm-4.67.3.tar.gz", hash = "sha256:7d825f03f89244ef73f1d4ce193cb1774a8179fd96f31d7e1dcde62092b960bb", size = 169598, upload-time = "2026-02-03T17:35:53.048Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/16/e1/3079a9ff9b8e11b846c6ac5c8b5bfb7ff225eee721825310c91b3b50304f/tqdm-4.67.3-py3-none-any.whl", hash = "sha256:ee1e4c0e59148062281c49d80b25b67771a127c85fc9676d3be5f243206826bf", size = 78374, upload-time = "2026-02-03T17:35:50.982Z" }, +] + +[[package]] +name = "traitlets" +version = "5.14.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/eb/79/72064e6a701c2183016abbbfedaba506d81e30e232a68c9f0d6f6fcd1574/traitlets-5.14.3.tar.gz", hash = "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7", size = 161621, upload-time = "2024-04-19T11:11:49.746Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/00/c0/8f5d070730d7836adc9c9b6408dec68c6ced86b304a9b26a14df072a6e8c/traitlets-5.14.3-py3-none-any.whl", hash = "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f", size = 85359, upload-time = "2024-04-19T11:11:46.763Z" }, +] + +[[package]] +name = "transformers" +version = "5.1.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "huggingface-hub" }, + { name = "numpy" }, + { name = "packaging" }, + { name = "pyyaml" }, + { name = "regex" }, + { name = "safetensors" }, + { name = "tokenizers" }, + { name = "tqdm" }, + { name = "typer-slim" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/c9/1d/a7d91500a6c02ec76058bc9e65fcdec1bdb8882854dec8e4adf12d0aa8b0/transformers-5.1.0.tar.gz", hash = "sha256:c60d6180e5845ea1b4eed38d7d1b06fcc4cc341c6b7fa5c1dc767d7e25fe0139", size = 8531810, upload-time = "2026-02-05T15:41:42.932Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/b7/66/57042d4b0f1ede8046d7ae6409bf3640df996e9cbc3fe20467aa29badc54/transformers-5.1.0-py3-none-any.whl", hash = "sha256:de534b50c9b2ce6217fc56421075a1734241fb40704fdc90f50f6a08fc533d59", size = 10276537, upload-time = "2026-02-05T15:41:40.358Z" }, +] + +[[package]] +name = "triton" +version = "3.6.0" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/3c/12/34d71b350e89a204c2c7777a9bba0dcf2f19a5bfdd70b57c4dbc5ffd7154/triton-3.6.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:448e02fe6dc898e9e5aa89cf0ee5c371e99df5aa5e8ad976a80b93334f3494fd", size = 176133521, upload-time = "2026-01-20T16:16:13.321Z" }, + { url = "https://files.pythonhosted.org/packages/f9/0b/37d991d8c130ce81a8728ae3c25b6e60935838e9be1b58791f5997b24a54/triton-3.6.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:10c7f76c6e72d2ef08df639e3d0d30729112f47a56b0c81672edc05ee5116ac9", size = 188289450, upload-time = "2026-01-20T16:00:49.136Z" }, + { url = "https://files.pythonhosted.org/packages/ce/4e/41b0c8033b503fd3cfcd12392cdd256945026a91ff02452bef40ec34bee7/triton-3.6.0-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1722e172d34e32abc3eb7711d0025bb69d7959ebea84e3b7f7a341cd7ed694d6", size = 176276087, upload-time = "2026-01-20T16:16:18.989Z" }, + { url = "https://files.pythonhosted.org/packages/35/f8/9c66bfc55361ec6d0e4040a0337fb5924ceb23de4648b8a81ae9d33b2b38/triton-3.6.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d002e07d7180fd65e622134fbd980c9a3d4211fb85224b56a0a0efbd422ab72f", size = 188400296, upload-time = "2026-01-20T16:00:56.042Z" }, + { url = "https://files.pythonhosted.org/packages/49/55/5ecf0dcaa0f2fbbd4420f7ef227ee3cb172e91e5fede9d0ecaddc43363b4/triton-3.6.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ef5523241e7d1abca00f1d240949eebdd7c673b005edbbce0aca95b8191f1d43", size = 176138577, upload-time = "2026-01-20T16:16:25.426Z" }, + { url = "https://files.pythonhosted.org/packages/df/3d/9e7eee57b37c80cec63322c0231bb6da3cfe535a91d7a4d64896fcb89357/triton-3.6.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a17a5d5985f0ac494ed8a8e54568f092f7057ef60e1b0fa09d3fd1512064e803", size = 188273063, upload-time = "2026-01-20T16:01:07.278Z" }, + { url = "https://files.pythonhosted.org/packages/48/db/56ee649cab5eaff4757541325aca81f52d02d4a7cd3506776cad2451e060/triton-3.6.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0b3a97e8ed304dfa9bd23bb41ca04cdf6b2e617d5e782a8653d616037a5d537d", size = 176274804, upload-time = "2026-01-20T16:16:31.528Z" }, + { url = "https://files.pythonhosted.org/packages/f6/56/6113c23ff46c00aae423333eb58b3e60bdfe9179d542781955a5e1514cb3/triton-3.6.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:46bd1c1af4b6704e554cad2eeb3b0a6513a980d470ccfa63189737340c7746a7", size = 188397994, upload-time = "2026-01-20T16:01:14.236Z" }, +] + +[[package]] +name = "typer" +version = "0.25.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "annotated-doc" }, + { name = "click" }, + { name = "rich" }, + { name = "shellingham" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/e4/51/9aed62104cea109b820bbd6c14245af756112017d309da813ef107d42e7e/typer-0.25.1.tar.gz", hash = "sha256:9616eb8853a09ffeabab1698952f33c6f29ffdbceb4eaeecf571880e8d7664cc", size = 122276, upload-time = "2026-04-30T19:32:16.964Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/3f/f9/2b3ff4e56e5fa7debfaf9eb135d0da96f3e9a1d5b27222223c7296336e5f/typer-0.25.1-py3-none-any.whl", hash = "sha256:75caa44ed46a03fb2dab8808753ffacdbfea88495e74c85a28c5eefcf5f39c89", size = 58409, upload-time = "2026-04-30T19:32:18.271Z" }, +] + +[[package]] +name = "typer-slim" +version = "0.24.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "typer" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/a7/a7/e6aecc4b4eb59598829a3b5076a93aff291b4fdaa2ded25efc4e1f4d219c/typer_slim-0.24.0.tar.gz", hash = "sha256:f0ed36127183f52ae6ced2ecb2521789995992c521a46083bfcdbb652d22ad34", size = 4776, upload-time = "2026-02-16T22:08:51.2Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/a7/24/5480c20380dfd18cf33d14784096dca45a24eae6102e91d49a718d3b6855/typer_slim-0.24.0-py3-none-any.whl", hash = "sha256:d5d7ee1ee2834d5020c7c616ed5e0d0f29b9a4b1dd283bdebae198ec09778d0e", size = 3394, upload-time = "2026-02-16T22:08:49.92Z" }, +] + +[[package]] +name = "typing-extensions" +version = "4.15.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/72/94/1a15dd82efb362ac84269196e94cf00f187f7ed21c242792a923cdb1c61f/typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466", size = 109391, upload-time = "2025-08-25T13:49:26.313Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/18/67/36e9267722cc04a6b9f15c7f3441c2363321a3ea07da7ae0c0707beb2a9c/typing_extensions-4.15.0-py3-none-any.whl", hash = "sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548", size = 44614, upload-time = "2025-08-25T13:49:24.86Z" }, +] + +[[package]] +name = "typing-inspection" +version = "0.4.2" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "typing-extensions" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/55/e3/70399cb7dd41c10ac53367ae42139cf4b1ca5f36bb3dc6c9d33acdb43655/typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464", size = 75949, upload-time = "2025-10-01T02:14:41.687Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/dc/9b/47798a6c91d8bdb567fe2698fe81e0c6b7cb7ef4d13da4114b41d239f65d/typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7", size = 14611, upload-time = "2025-10-01T02:14:40.154Z" }, +] + +[[package]] +name = "tzdata" +version = "2026.2" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/ba/19/1b9b0e29f30c6d35cb345486df41110984ea67ae69dddbc0e8a100999493/tzdata-2026.2.tar.gz", hash = "sha256:9173fde7d80d9018e02a662e168e5a2d04f87c41ea174b139fbef642eda62d10", size = 198254, upload-time = "2026-04-24T15:22:08.651Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ce/e4/dccd7f47c4b64213ac01ef921a1337ee6e30e8c6466046018326977efd95/tzdata-2026.2-py2.py3-none-any.whl", hash = "sha256:bbe9af844f658da81a5f95019480da3a89415801f6cc966806612cc7169bffe7", size = 349321, upload-time = "2026-04-24T15:22:05.876Z" }, +] + +[[package]] +name = "uri-template" +version = "1.3.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/31/c7/0336f2bd0bcbada6ccef7aaa25e443c118a704f828a0620c6fa0207c1b64/uri-template-1.3.0.tar.gz", hash = "sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7", size = 21678, upload-time = "2023-06-21T01:49:05.374Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e7/00/3fca040d7cf8a32776d3d81a00c8ee7457e00f80c649f1e4a863c8321ae9/uri_template-1.3.0-py3-none-any.whl", hash = "sha256:a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363", size = 11140, upload-time = "2023-06-21T01:49:03.467Z" }, +] + +[[package]] +name = "urllib3" +version = "2.6.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/c7/24/5f1b3bdffd70275f6661c76461e25f024d5a38a46f04aaca912426a2b1d3/urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed", size = 435556, upload-time = "2026-01-07T16:24:43.925Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/39/08/aaaad47bc4e9dc8c725e68f9d04865dbcb2052843ff09c97b08904852d84/urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4", size = 131584, upload-time = "2026-01-07T16:24:42.685Z" }, +] + +[[package]] +name = "uvicorn" +version = "0.46.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "click" }, + { name = "h11" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/1f/93/041fca8274050e40e6791f267d82e0e2e27dd165627bd640d3e0e378d877/uvicorn-0.46.0.tar.gz", hash = "sha256:fb9da0926999cc6cb22dc7cd71a94a632f078e6ae47ff683c5c420750fb7413d", size = 88758, upload-time = "2026-04-23T07:16:00.151Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/31/a3/5b1562db76a5a488274b2332a97199b32d0442aca0ed193697fd47786316/uvicorn-0.46.0-py3-none-any.whl", hash = "sha256:bbebbcbed972d162afca128605223022bedd345b7bc7855ce66deb31487a9048", size = 70926, upload-time = "2026-04-23T07:15:58.355Z" }, +] + +[[package]] +name = "wcwidth" +version = "0.7.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/2c/ee/afaf0f85a9a18fe47a67f1e4422ed6cf1fe642f0ae0a2f81166231303c52/wcwidth-0.7.0.tar.gz", hash = "sha256:90e3a7ea092341c44b99562e75d09e4d5160fe7a3974c6fb842a101a95e7eed0", size = 182132, upload-time = "2026-05-02T16:04:12.653Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/41/52/e465037f5375f43533d1a80b6923955201596a99142ed524d77b571a1418/wcwidth-0.7.0-py3-none-any.whl", hash = "sha256:5d69154c429a82910e241c738cd0e2976fac8a2dd47a1a805f4afed1c0f136f2", size = 110825, upload-time = "2026-05-02T16:04:11.033Z" }, +] + +[[package]] +name = "webcolors" +version = "25.10.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/1d/7a/eb316761ec35664ea5174709a68bbd3389de60d4a1ebab8808bfc264ed67/webcolors-25.10.0.tar.gz", hash = "sha256:62abae86504f66d0f6364c2a8520de4a0c47b80c03fc3a5f1815fedbef7c19bf", size = 53491, upload-time = "2025-10-31T07:51:03.977Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e2/cc/e097523dd85c9cf5d354f78310927f1656c422bd7b2613b2db3e3f9a0f2c/webcolors-25.10.0-py3-none-any.whl", hash = "sha256:032c727334856fc0b968f63daa252a1ac93d33db2f5267756623c210e57a4f1d", size = 14905, upload-time = "2025-10-31T07:51:01.778Z" }, +] + +[[package]] +name = "webencodings" +version = "0.5.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/0b/02/ae6ceac1baeda530866a85075641cec12989bd8d31af6d5ab4a3e8c92f47/webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923", size = 9721, upload-time = "2017-04-05T20:21:34.189Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f4/24/2a3e3df732393fed8b3ebf2ec078f05546de641fe1b667ee316ec1dcf3b7/webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78", size = 11774, upload-time = "2017-04-05T20:21:32.581Z" }, +] + +[[package]] +name = "websocket-client" +version = "1.9.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/2c/41/aa4bf9664e4cda14c3b39865b12251e8e7d239f4cd0e3cc1b6c2ccde25c1/websocket_client-1.9.0.tar.gz", hash = "sha256:9e813624b6eb619999a97dc7958469217c3176312b3a16a4bd1bc7e08a46ec98", size = 70576, upload-time = "2025-10-07T21:16:36.495Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/34/db/b10e48aa8fff7407e67470363eac595018441cf32d5e1001567a7aeba5d2/websocket_client-1.9.0-py3-none-any.whl", hash = "sha256:af248a825037ef591efbf6ed20cc5faa03d3b47b9e5a2230a529eeee1c1fc3ef", size = 82616, upload-time = "2025-10-07T21:16:34.951Z" }, +] + +[[package]] +name = "websockets" +version = "16.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/04/24/4b2031d72e840ce4c1ccb255f693b15c334757fc50023e4db9537080b8c4/websockets-16.0.tar.gz", hash = "sha256:5f6261a5e56e8d5c42a4497b364ea24d94d9563e8fbd44e78ac40879c60179b5", size = 179346, upload-time = "2026-01-10T09:23:47.181Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/cc/9c/baa8456050d1c1b08dd0ec7346026668cbc6f145ab4e314d707bb845bf0d/websockets-16.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:878b336ac47938b474c8f982ac2f7266a540adc3fa4ad74ae96fea9823a02cc9", size = 177364, upload-time = "2026-01-10T09:22:59.333Z" }, + { url = "https://files.pythonhosted.org/packages/7e/0c/8811fc53e9bcff68fe7de2bcbe75116a8d959ac699a3200f4847a8925210/websockets-16.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:52a0fec0e6c8d9a784c2c78276a48a2bdf099e4ccc2a4cad53b27718dbfd0230", size = 175039, upload-time = "2026-01-10T09:23:01.171Z" }, + { url = "https://files.pythonhosted.org/packages/aa/82/39a5f910cb99ec0b59e482971238c845af9220d3ab9fa76dd9162cda9d62/websockets-16.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:e6578ed5b6981005df1860a56e3617f14a6c307e6a71b4fff8c48fdc50f3ed2c", size = 175323, upload-time = "2026-01-10T09:23:02.341Z" }, + { url = "https://files.pythonhosted.org/packages/bd/28/0a25ee5342eb5d5f297d992a77e56892ecb65e7854c7898fb7d35e9b33bd/websockets-16.0-cp313-cp313-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:95724e638f0f9c350bb1c2b0a7ad0e83d9cc0c9259f3ea94e40d7b02a2179ae5", size = 184975, upload-time = "2026-01-10T09:23:03.756Z" }, + { url = "https://files.pythonhosted.org/packages/f9/66/27ea52741752f5107c2e41fda05e8395a682a1e11c4e592a809a90c6a506/websockets-16.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c0204dc62a89dc9d50d682412c10b3542d748260d743500a85c13cd1ee4bde82", size = 186203, upload-time = "2026-01-10T09:23:05.01Z" }, + { url = "https://files.pythonhosted.org/packages/37/e5/8e32857371406a757816a2b471939d51c463509be73fa538216ea52b792a/websockets-16.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:52ac480f44d32970d66763115edea932f1c5b1312de36df06d6b219f6741eed8", size = 185653, upload-time = "2026-01-10T09:23:06.301Z" }, + { url = "https://files.pythonhosted.org/packages/9b/67/f926bac29882894669368dc73f4da900fcdf47955d0a0185d60103df5737/websockets-16.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:6e5a82b677f8f6f59e8dfc34ec06ca6b5b48bc4fcda346acd093694cc2c24d8f", size = 184920, upload-time = "2026-01-10T09:23:07.492Z" }, + { url = "https://files.pythonhosted.org/packages/3c/a1/3d6ccdcd125b0a42a311bcd15a7f705d688f73b2a22d8cf1c0875d35d34a/websockets-16.0-cp313-cp313-win32.whl", hash = "sha256:abf050a199613f64c886ea10f38b47770a65154dc37181bfaff70c160f45315a", size = 178255, upload-time = "2026-01-10T09:23:09.245Z" }, + { url = "https://files.pythonhosted.org/packages/6b/ae/90366304d7c2ce80f9b826096a9e9048b4bb760e44d3b873bb272cba696b/websockets-16.0-cp313-cp313-win_amd64.whl", hash = "sha256:3425ac5cf448801335d6fdc7ae1eb22072055417a96cc6b31b3861f455fbc156", size = 178689, upload-time = "2026-01-10T09:23:10.483Z" }, + { url = "https://files.pythonhosted.org/packages/f3/1d/e88022630271f5bd349ed82417136281931e558d628dd52c4d8621b4a0b2/websockets-16.0-cp314-cp314-macosx_10_15_universal2.whl", hash = "sha256:8cc451a50f2aee53042ac52d2d053d08bf89bcb31ae799cb4487587661c038a0", size = 177406, upload-time = "2026-01-10T09:23:12.178Z" }, + { url = "https://files.pythonhosted.org/packages/f2/78/e63be1bf0724eeb4616efb1ae1c9044f7c3953b7957799abb5915bffd38e/websockets-16.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:daa3b6ff70a9241cf6c7fc9e949d41232d9d7d26fd3522b1ad2b4d62487e9904", size = 175085, upload-time = "2026-01-10T09:23:13.511Z" }, + { url = "https://files.pythonhosted.org/packages/bb/f4/d3c9220d818ee955ae390cf319a7c7a467beceb24f05ee7aaaa2414345ba/websockets-16.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:fd3cb4adb94a2a6e2b7c0d8d05cb94e6f1c81a0cf9dc2694fb65c7e8d94c42e4", size = 175328, upload-time = "2026-01-10T09:23:14.727Z" }, + { url = "https://files.pythonhosted.org/packages/63/bc/d3e208028de777087e6fb2b122051a6ff7bbcca0d6df9d9c2bf1dd869ae9/websockets-16.0-cp314-cp314-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:781caf5e8eee67f663126490c2f96f40906594cb86b408a703630f95550a8c3e", size = 185044, upload-time = "2026-01-10T09:23:15.939Z" }, + { url = "https://files.pythonhosted.org/packages/ad/6e/9a0927ac24bd33a0a9af834d89e0abc7cfd8e13bed17a86407a66773cc0e/websockets-16.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:caab51a72c51973ca21fa8a18bd8165e1a0183f1ac7066a182ff27107b71e1a4", size = 186279, upload-time = "2026-01-10T09:23:17.148Z" }, + { url = "https://files.pythonhosted.org/packages/b9/ca/bf1c68440d7a868180e11be653c85959502efd3a709323230314fda6e0b3/websockets-16.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:19c4dc84098e523fd63711e563077d39e90ec6702aff4b5d9e344a60cb3c0cb1", size = 185711, upload-time = "2026-01-10T09:23:18.372Z" }, + { url = "https://files.pythonhosted.org/packages/c4/f8/fdc34643a989561f217bb477cbc47a3a07212cbda91c0e4389c43c296ebf/websockets-16.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:a5e18a238a2b2249c9a9235466b90e96ae4795672598a58772dd806edc7ac6d3", size = 184982, upload-time = "2026-01-10T09:23:19.652Z" }, + { url = "https://files.pythonhosted.org/packages/dd/d1/574fa27e233764dbac9c52730d63fcf2823b16f0856b3329fc6268d6ae4f/websockets-16.0-cp314-cp314-win32.whl", hash = "sha256:a069d734c4a043182729edd3e9f247c3b2a4035415a9172fd0f1b71658a320a8", size = 177915, upload-time = "2026-01-10T09:23:21.458Z" }, + { url = "https://files.pythonhosted.org/packages/8a/f1/ae6b937bf3126b5134ce1f482365fde31a357c784ac51852978768b5eff4/websockets-16.0-cp314-cp314-win_amd64.whl", hash = "sha256:c0ee0e63f23914732c6d7e0cce24915c48f3f1512ec1d079ed01fc629dab269d", size = 178381, upload-time = "2026-01-10T09:23:22.715Z" }, + { url = "https://files.pythonhosted.org/packages/06/9b/f791d1db48403e1f0a27577a6beb37afae94254a8c6f08be4a23e4930bc0/websockets-16.0-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:a35539cacc3febb22b8f4d4a99cc79b104226a756aa7400adc722e83b0d03244", size = 177737, upload-time = "2026-01-10T09:23:24.523Z" }, + { url = "https://files.pythonhosted.org/packages/bd/40/53ad02341fa33b3ce489023f635367a4ac98b73570102ad2cdd770dacc9a/websockets-16.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:b784ca5de850f4ce93ec85d3269d24d4c82f22b7212023c974c401d4980ebc5e", size = 175268, upload-time = "2026-01-10T09:23:25.781Z" }, + { url = "https://files.pythonhosted.org/packages/74/9b/6158d4e459b984f949dcbbb0c5d270154c7618e11c01029b9bbd1bb4c4f9/websockets-16.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:569d01a4e7fba956c5ae4fc988f0d4e187900f5497ce46339c996dbf24f17641", size = 175486, upload-time = "2026-01-10T09:23:27.033Z" }, + { url = "https://files.pythonhosted.org/packages/e5/2d/7583b30208b639c8090206f95073646c2c9ffd66f44df967981a64f849ad/websockets-16.0-cp314-cp314t-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:50f23cdd8343b984957e4077839841146f67a3d31ab0d00e6b824e74c5b2f6e8", size = 185331, upload-time = "2026-01-10T09:23:28.259Z" }, + { url = "https://files.pythonhosted.org/packages/45/b0/cce3784eb519b7b5ad680d14b9673a31ab8dcb7aad8b64d81709d2430aa8/websockets-16.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:152284a83a00c59b759697b7f9e9cddf4e3c7861dd0d964b472b70f78f89e80e", size = 186501, upload-time = "2026-01-10T09:23:29.449Z" }, + { url = "https://files.pythonhosted.org/packages/19/60/b8ebe4c7e89fb5f6cdf080623c9d92789a53636950f7abacfc33fe2b3135/websockets-16.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:bc59589ab64b0022385f429b94697348a6a234e8ce22544e3681b2e9331b5944", size = 186062, upload-time = "2026-01-10T09:23:31.368Z" }, + { url = "https://files.pythonhosted.org/packages/88/a8/a080593f89b0138b6cba1b28f8df5673b5506f72879322288b031337c0b8/websockets-16.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:32da954ffa2814258030e5a57bc73a3635463238e797c7375dc8091327434206", size = 185356, upload-time = "2026-01-10T09:23:32.627Z" }, + { url = "https://files.pythonhosted.org/packages/c2/b6/b9afed2afadddaf5ebb2afa801abf4b0868f42f8539bfe4b071b5266c9fe/websockets-16.0-cp314-cp314t-win32.whl", hash = "sha256:5a4b4cc550cb665dd8a47f868c8d04c8230f857363ad3c9caf7a0c3bf8c61ca6", size = 178085, upload-time = "2026-01-10T09:23:33.816Z" }, + { url = "https://files.pythonhosted.org/packages/9f/3e/28135a24e384493fa804216b79a6a6759a38cc4ff59118787b9fb693df93/websockets-16.0-cp314-cp314t-win_amd64.whl", hash = "sha256:b14dc141ed6d2dde437cddb216004bcac6a1df0935d79656387bd41632ba0bbd", size = 178531, upload-time = "2026-01-10T09:23:35.016Z" }, + { url = "https://files.pythonhosted.org/packages/6f/28/258ebab549c2bf3e64d2b0217b973467394a9cea8c42f70418ca2c5d0d2e/websockets-16.0-py3-none-any.whl", hash = "sha256:1637db62fad1dc833276dded54215f2c7fa46912301a24bd94d45d46a011ceec", size = 171598, upload-time = "2026-01-10T09:23:45.395Z" }, +] + +[[package]] +name = "widgetsnbextension" +version = "4.0.15" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/bd/f4/c67440c7fb409a71b7404b7aefcd7569a9c0d6bd071299bf4198ae7a5d95/widgetsnbextension-4.0.15.tar.gz", hash = "sha256:de8610639996f1567952d763a5a41af8af37f2575a41f9852a38f947eb82a3b9", size = 1097402, upload-time = "2025-11-01T21:15:55.178Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/3f/0e/fa3b193432cfc60c93b42f3be03365f5f909d2b3ea410295cf36df739e31/widgetsnbextension-4.0.15-py3-none-any.whl", hash = "sha256:8156704e4346a571d9ce73b84bee86a29906c9abfd7223b7228a28899ccf3366", size = 2196503, upload-time = "2025-11-01T21:15:53.565Z" }, +] + +[[package]] +name = "win32-setctime" +version = "1.2.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/b3/8f/705086c9d734d3b663af0e9bb3d4de6578d08f46b1b101c2442fd9aecaa2/win32_setctime-1.2.0.tar.gz", hash = "sha256:ae1fdf948f5640aae05c511ade119313fb6a30d7eabe25fef9764dca5873c4c0", size = 4867, upload-time = "2024-12-07T15:28:28.314Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e1/07/c6fe3ad3e685340704d314d765b7912993bcb8dc198f0e7a89382d37974b/win32_setctime-1.2.0-py3-none-any.whl", hash = "sha256:95d644c4e708aba81dc3704a116d8cbc974d70b3bdb8be1d150e36be6e9d1390", size = 4083, upload-time = "2024-12-07T15:28:26.465Z" }, +] + +[[package]] +name = "xxhash" +version = "3.7.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/24/2f/e183a1b407002f5af81822bee18b61cdb94b8670208ef34734d8d2b8ebe9/xxhash-3.7.0.tar.gz", hash = "sha256:6cc4eefbb542a5d6ffd6d70ea9c502957c925e800f998c5630ecc809d6702bae", size = 82022, upload-time = "2026-04-25T11:10:32.553Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/c1/ca/d5174b4c36d10f64d4ca7050563138c5a599efb01a765858ddefc9c1202a/xxhash-3.7.0-cp313-cp313-android_21_arm64_v8a.whl", hash = "sha256:4b6d6b33f141158692bd4eafbb96edbc5aa0dabdb593a962db01a91983d4f8fa", size = 36813, upload-time = "2026-04-25T11:06:51.73Z" }, + { url = "https://files.pythonhosted.org/packages/41/d0/abc6c9d347ba1f1e1e1d98125d0881a0452c7f9a76a9dd03a7b5d2197f23/xxhash-3.7.0-cp313-cp313-android_21_x86_64.whl", hash = "sha256:845d347df254d6c619f616afa921331bada8614b8d373d58725c663ba97c3605", size = 35121, upload-time = "2026-04-25T11:06:53.048Z" }, + { url = "https://files.pythonhosted.org/packages/bf/11/4cc834eb3d79f2f2b3a6ef7324195208bcdfbdcf7534d2b17267aa5f3a8f/xxhash-3.7.0-cp313-cp313-ios_13_0_arm64_iphoneos.whl", hash = "sha256:fddbbb69a6fff4f421e7a0d1fa28f894b20112e9e3fab306af451e2dfd0e459b", size = 29624, upload-time = "2026-04-25T11:06:54.311Z" }, + { url = "https://files.pythonhosted.org/packages/23/83/e97d3e7b635fe73a1dfb1e91f805324dd6d930bb42041cbf18f183bc0b6d/xxhash-3.7.0-cp313-cp313-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:54876a4e45101cec2bf8f31a973cda073a23e2e108538dad224ba07f85f22487", size = 30638, upload-time = "2026-04-25T11:06:55.864Z" }, + { url = "https://files.pythonhosted.org/packages/f4/40/d84951d80c35db1f4c40a29a64a8520eea5d56e764c603906b4fe763580f/xxhash-3.7.0-cp313-cp313-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:0c72fe9c7e3d6dfd7f1e21e224a877917fa09c465694ba4e06464b9511b65544", size = 33323, upload-time = "2026-04-25T11:06:57.336Z" }, + { url = "https://files.pythonhosted.org/packages/89/cc/c7dc6558d97e9ab023f663d69ab28b340ed9bf4d2d94f2c259cf896bb354/xxhash-3.7.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:a6d73a830b17ef49bc04e00182bd839164c1b3c59c127cd7c54fcb10c7ed8ee8", size = 33362, upload-time = "2026-04-25T11:06:58.656Z" }, + { url = "https://files.pythonhosted.org/packages/2a/6e/46b84017b1301d54091430353d4ad5901654a3e0871649877a416f7f1644/xxhash-3.7.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:91c3b07cf3362086d8f126c6aecd8e5e9396ad8b2f2219ea7e49a8250c318acd", size = 30874, upload-time = "2026-04-25T11:06:59.834Z" }, + { url = "https://files.pythonhosted.org/packages/df/5e/8f9158e3ab906ad3fec51e09b5ea0093e769f12207bfa42a368ca204e7ab/xxhash-3.7.0-cp313-cp313-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:50e879ebbac351c81565ca108db766d7832f5b8b6a5b14b8c0151f7190028e3d", size = 194185, upload-time = "2026-04-25T11:07:01.658Z" }, + { url = "https://files.pythonhosted.org/packages/f3/29/a804ded9f5d3d3758292678d23e7528b08fda7b7e750688d08b052322475/xxhash-3.7.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:921c14e93817842dd0dd9f372890a0f0c72e534650b6ab13c5be5cd0db11d47e", size = 213033, upload-time = "2026-04-25T11:07:03.606Z" }, + { url = "https://files.pythonhosted.org/packages/8b/91/1ce5a7d2fdc975267320e2c78fc1cecfe7ab735ccbcf6993ec5dd541cb2c/xxhash-3.7.0-cp313-cp313-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:e64a7c9d7dfca3e0fafcbc5e455519090706a3e36e95d655cec3e04e79f95aaa", size = 236140, upload-time = "2026-04-25T11:07:05.396Z" }, + { url = "https://files.pythonhosted.org/packages/34/04/fd595a4fd8617b05fa27bd9b684ecb4985bfed27917848eea85d54036d06/xxhash-3.7.0-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:2220af08163baf5fa36c2b8af079dc2cbe6e66ae061385267f9472362dfd53c6", size = 212291, upload-time = "2026-04-25T11:07:06.966Z" }, + { url = "https://files.pythonhosted.org/packages/03/fb/f1a379cbc372ae5b9f4ab36154c48a849ca6ebe3ac477067a57865bf3bc6/xxhash-3.7.0-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:f14bb8b22a4a91325813e3d553b8963c10cf8c756cff65ee50c194431296c655", size = 445532, upload-time = "2026-04-25T11:07:08.525Z" }, + { url = "https://files.pythonhosted.org/packages/65/59/172424b79f8cfd4b6d8a122b2193e6b8ad4b11f7159bb3b6f9b3191329bb/xxhash-3.7.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:496736f86a9bedaf64b0dc70e3539d0766df01c71ea22032698e88f3f04a1ce9", size = 193990, upload-time = "2026-04-25T11:07:10.315Z" }, + { url = "https://files.pythonhosted.org/packages/b9/19/aeac22161d953f139f07ba5586cb4a17c5b7b6dff985122803bb12933500/xxhash-3.7.0-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:0ff71596bd79816975b3de7130ab1ff4541410285a3c084584eeb1c8239996fd", size = 284876, upload-time = "2026-04-25T11:07:12.15Z" }, + { url = "https://files.pythonhosted.org/packages/77/d5/4fd0b59e7a02242953da05ff679fbb961b0a4368eac97a217e11dae110c1/xxhash-3.7.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:1ad86695c19b1d46fe106925db3c7a37f16be37669dcf58dcc70a9dd6e324676", size = 210495, upload-time = "2026-04-25T11:07:13.952Z" }, + { url = "https://files.pythonhosted.org/packages/aa/fb/976a3165c728c7faf74aa1b5ab3cf6a85e6d731612894741840524c7d28c/xxhash-3.7.0-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:970f9f8c50961d639cbd0d988c96f80ddf66006de93641719282c4fe7a87c5e6", size = 241331, upload-time = "2026-04-25T11:07:15.557Z" }, + { url = "https://files.pythonhosted.org/packages/4a/2c/6763d5901d53ac9e6ba296e5717ae599025c9d268396e8faa8b4b0a8e0ac/xxhash-3.7.0-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:5886ad85e9e347911783760a1d16cb6b393e8f9e3b52c982568226cb56927bdc", size = 198037, upload-time = "2026-04-25T11:07:17.563Z" }, + { url = "https://files.pythonhosted.org/packages/61/2b/876e722d533833f5f9a83473e6ba993e48745701096944e77bbecf29b2c3/xxhash-3.7.0-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:6e934bbae1e0ec74e27d5f0d7f37ef547ce5ff9f0a7e63fb39e559fc99526734", size = 210744, upload-time = "2026-04-25T11:07:19.055Z" }, + { url = "https://files.pythonhosted.org/packages/21/e6/d7e7baef7ce24166b4668d3c48557bb35a23b92ecadcac7e7718d099ab69/xxhash-3.7.0-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:3b6b3d28228af044ebcded71c4a3dd86e1dbd7e2f4645bf40f7b5da65bb5fb5a", size = 275406, upload-time = "2026-04-25T11:07:20.908Z" }, + { url = "https://files.pythonhosted.org/packages/92/fe/198b3763b2e01ca908f2154969a2352ec99bda892b574a11a9a151c5ede4/xxhash-3.7.0-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:6be4d70d9ab76c9f324ead9c01af6ff52c324745ea0c3731682a0cf99720f1fe", size = 414125, upload-time = "2026-04-25T11:07:23.037Z" }, + { url = "https://files.pythonhosted.org/packages/3a/6d/019a11affd5a5499137cacca53808659964785439855b5aa40dfd3412916/xxhash-3.7.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:151d7520838d4465461a0b7f4ae488b3b00de16183dd3214c1a6b14bf89d7fb6", size = 191555, upload-time = "2026-04-25T11:07:24.991Z" }, + { url = "https://files.pythonhosted.org/packages/76/21/b96d58568df2d01533244c3e0e5cbdd0c8b2b25c4bec4d72f19259a292d7/xxhash-3.7.0-cp313-cp313-win32.whl", hash = "sha256:d798c1e291bffb8e37b5bbe0dda77fc767cd19e89cadaf66e6ed5d0ff88c9fe6", size = 30668, upload-time = "2026-04-25T11:07:26.665Z" }, + { url = "https://files.pythonhosted.org/packages/99/57/d849a8d3afa1f8f4bc6a831cd89f49f9706fbbad94d2975d6140a171988c/xxhash-3.7.0-cp313-cp313-win_amd64.whl", hash = "sha256:875811ba23c543b1a1c3143c926e43996eb27ebb8f52d3500744aa608c275aed", size = 31524, upload-time = "2026-04-25T11:07:27.92Z" }, + { url = "https://files.pythonhosted.org/packages/81/52/bacc753e92dee78b058af8dcef0a50815f5f860986c664a92d75f965b6a5/xxhash-3.7.0-cp313-cp313-win_arm64.whl", hash = "sha256:54a675cb300dda83d71daae2a599389d22db8021a0f8db0dd659e14626eb3ecc", size = 27768, upload-time = "2026-04-25T11:07:29.113Z" }, + { url = "https://files.pythonhosted.org/packages/1c/47/ddbd683b7fc7e592c1a8d9d65f73ce9ab513f082b3967eee2baf549b8fc6/xxhash-3.7.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:a3b19a42111c4057c1547a4a1396a53961dca576a0f6b82bfa88a2d1561764b2", size = 33576, upload-time = "2026-04-25T11:07:30.469Z" }, + { url = "https://files.pythonhosted.org/packages/07/f2/36d3310161db7f72efb4562aadde0ed429f1d0531782dd6345b12d2da527/xxhash-3.7.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:8f4608a06e4d61b7a3425665a46d00e0579122e1a2fae97a0c52953a3aad9aa3", size = 31123, upload-time = "2026-04-25T11:07:31.989Z" }, + { url = "https://files.pythonhosted.org/packages/0d/3f/75937a5c69556ed213021e43cbedd84c8e0279d0d74e7d41a255d84ba4b1/xxhash-3.7.0-cp313-cp313t-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:ad37c7792479e49cf96c1ab25517d7003fe0d93687a772ba19a097d235bbe41e", size = 196491, upload-time = "2026-04-25T11:07:33.358Z" }, + { url = "https://files.pythonhosted.org/packages/22/29/f10d7ff8c7a733d4403a43b9de18c8fabc005f98cec054644f04418659ee/xxhash-3.7.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:dc026e3b89d98e30a8288c95cb696e77d150b3f0fb7a51f73dcd49ee6b5577fa", size = 215793, upload-time = "2026-04-25T11:07:34.919Z" }, + { url = "https://files.pythonhosted.org/packages/8b/fd/778f60aa295f58907938f030a8b514611f391405614a525cccd2ffc00eb5/xxhash-3.7.0-cp313-cp313t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:c9b31ab1f28b078a6a1ac1a54eb35e7d5390deddd56870d0be3a0a733d1c321c", size = 237993, upload-time = "2026-04-25T11:07:36.638Z" }, + { url = "https://files.pythonhosted.org/packages/70/f5/736db5de387b4a540e37a05b84b40dc58a1ce974bfd2b4e5754ce29b68c3/xxhash-3.7.0-cp313-cp313t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:3bb5fd680c038fd5229e44e9c493782f90df9bef632fd0499d442374688ff70b", size = 214887, upload-time = "2026-04-25T11:07:38.564Z" }, + { url = "https://files.pythonhosted.org/packages/4d/aa/09a095f22fdb9a27fbb716841fbff52119721f9ca4261952d07a912f7839/xxhash-3.7.0-cp313-cp313t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:030c0fd688fce3569fbb49a2feefd4110cbb0b650186fb4610759ecfac677548", size = 448407, upload-time = "2026-04-25T11:07:40.552Z" }, + { url = "https://files.pythonhosted.org/packages/74/8a/b745efeeca9e34a91c26fdc97ad8514c43d5a81ac78565cba80a1353870a/xxhash-3.7.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5b1bde10324f4c31812ae0d0502e92d916ae8917cad7209353f122b8b8f610c3", size = 196119, upload-time = "2026-04-25T11:07:42.101Z" }, + { url = "https://files.pythonhosted.org/packages/8a/5c/0cfceb024af90c191f665c7933b1f318ee234f4797858383bebd1881d52f/xxhash-3.7.0-cp313-cp313t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:503722d52a615f2604f5e7611de7d43878df010dc0053094ef91cb9a9ac3d987", size = 286751, upload-time = "2026-04-25T11:07:43.568Z" }, + { url = "https://files.pythonhosted.org/packages/0b/0a/0793e405dc3cf8f4ebe2c1acec1e4e4608cd9e7e50ea691dabbc2a95ccbb/xxhash-3.7.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:c72500a3b6d6c30ebfc135035bcace9eb5884f2dc220804efcaaba43e9f611dd", size = 212961, upload-time = "2026-04-25T11:07:45.388Z" }, + { url = "https://files.pythonhosted.org/packages/0c/7e/721118ffc63bfff94aa565bcf2555a820f9f4bdb0f001e0d609bdfad70de/xxhash-3.7.0-cp313-cp313t-musllinux_1_2_armv7l.whl", hash = "sha256:43475925a766d01ca8cd9a857fd87f3d50406983c8506a4c07c4df12adcc867f", size = 243703, upload-time = "2026-04-25T11:07:47.053Z" }, + { url = "https://files.pythonhosted.org/packages/6e/18/16f6267160488b8276fd3d449d425712512add292ba545c1b6946bfdb7dd/xxhash-3.7.0-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:8d09dfd2ab135b985daf868b594315ebe11ad86cd9fea46e6c69f19b28f7d25a", size = 200894, upload-time = "2026-04-25T11:07:48.657Z" }, + { url = "https://files.pythonhosted.org/packages/2d/94/80ba841287fd97e3e9cac1d228788c8ef623746f570404961eec748ecb5c/xxhash-3.7.0-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:c50269d0055ac1faecfd559886d2cbe4b730de236585aba0e873f9d9dadbe585", size = 213357, upload-time = "2026-04-25T11:07:50.257Z" }, + { url = "https://files.pythonhosted.org/packages/a1/7e/106d4067130c59f1e18a55ffadcd876d8c68534883a1e02685b29d3d8153/xxhash-3.7.0-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:1910df4756a5ab58cfad8744fc2d0f23926e3efcc346ee76e87b974abab922f4", size = 277600, upload-time = "2026-04-25T11:07:51.745Z" }, + { url = "https://files.pythonhosted.org/packages/c5/86/a081dd30da71d720b2612a792bfd55e45fa9a07ac76a0507f60487473c25/xxhash-3.7.0-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:d006faf3b491957efcb433489be3c149efe4787b7063d5cddb8ddaefdc60e0c1", size = 416980, upload-time = "2026-04-25T11:07:53.504Z" }, + { url = "https://files.pythonhosted.org/packages/35/29/1a95221a029a3c1293773869e1ab47b07cbbdd82444a42809e8c60156626/xxhash-3.7.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:abb65b4e947e958f7b3b0d71db3ce447d1bc5f37f5eab871ce7223bda8768a04", size = 193840, upload-time = "2026-04-25T11:07:55.103Z" }, + { url = "https://files.pythonhosted.org/packages/c5/e0/db909dd0823285de2286f67e10ee4d81e96ad35d7d8e964ecb07fccd8af9/xxhash-3.7.0-cp313-cp313t-win32.whl", hash = "sha256:178959906cb1716a1ce08e0d69c82886c70a15a6f2790fc084fdd146ca30cd49", size = 30966, upload-time = "2026-04-25T11:07:56.524Z" }, + { url = "https://files.pythonhosted.org/packages/7b/ff/d705b15b22f21ee106adce239cb65d35067a158c630b240270f09b17c2e6/xxhash-3.7.0-cp313-cp313t-win_amd64.whl", hash = "sha256:2524a1e20d4c231d13b50f7cf39e44265b055669a64a7a4b9a2a44faa03f19b6", size = 31784, upload-time = "2026-04-25T11:07:57.758Z" }, + { url = "https://files.pythonhosted.org/packages/a2/1f/b2cf83c3638fd0588e0b17f22e5a9400bdfb1a3e3755324ac0aee2250b88/xxhash-3.7.0-cp313-cp313t-win_arm64.whl", hash = "sha256:37d994d0ffe81ef087bb330d392caa809bb5853c77e22ea3f71db024a0543dba", size = 27932, upload-time = "2026-04-25T11:07:59.109Z" }, + { url = "https://files.pythonhosted.org/packages/0e/cc/431db584f6fbb9312e40a173af027644e5580d39df1f73603cbb9dca4d6b/xxhash-3.7.0-cp314-cp314-android_24_arm64_v8a.whl", hash = "sha256:8c5fcfd806c335bfa2adf1cd0b3110a44fc7b6995c3a648c27489bae85801465", size = 36644, upload-time = "2026-04-25T11:08:00.658Z" }, + { url = "https://files.pythonhosted.org/packages/bc/01/255ec513e0a705d1f9a61413e78dfce4e3235203f0ed525a24c2b4b56345/xxhash-3.7.0-cp314-cp314-android_24_x86_64.whl", hash = "sha256:506a0b488f190f0a06769575e30caf71615c898ed93ab18b0dbcb6dec5c3713c", size = 35003, upload-time = "2026-04-25T11:08:02.338Z" }, + { url = "https://files.pythonhosted.org/packages/68/70/c55fc33c93445b44d8fc5a17b41ed99e3cebe92bcf8396809e63fc9a1165/xxhash-3.7.0-cp314-cp314-ios_13_0_arm64_iphoneos.whl", hash = "sha256:ec68dbba21532c0173a9872298e65c89749f7c9d21538c3a78b5bb6105871568", size = 29655, upload-time = "2026-04-25T11:08:03.701Z" }, + { url = "https://files.pythonhosted.org/packages/c2/72/ff8de73df000d74467d12a59ce6d6e2b2a368b978d41ab7b1fba5ed442be/xxhash-3.7.0-cp314-cp314-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:fa77e7ec1450d415d20129961814787c9abd9a07f98872f070b1fe96c5084611", size = 30664, upload-time = "2026-04-25T11:08:05.011Z" }, + { url = "https://files.pythonhosted.org/packages/b6/91/08416d9bd9bc3bf39d831abe8a5631ac2db5141dfd6fe81c3fe59a1f9264/xxhash-3.7.0-cp314-cp314-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:fe32736295ea38e43e7d9424053c8c47c9f64fecfc7c895fb3da9b30b131c9ee", size = 33317, upload-time = "2026-04-25T11:08:06.413Z" }, + { url = "https://files.pythonhosted.org/packages/0e/3b/86b1caa4dee10a99f4bf9521e623359341c5e50d05158fa10c275b2bd079/xxhash-3.7.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:ab9dd2c83c4bbd63e422181a76f13502d049d3ddcac9a1bdc29196263d692bb8", size = 33457, upload-time = "2026-04-25T11:08:08.099Z" }, + { url = "https://files.pythonhosted.org/packages/ed/38/98ea14ad1517e1461292a65906951458d520689782bfbae111050145bdba/xxhash-3.7.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:3afec3a336a2286601a437cb07562ab0227685e6fbb9ec17e8c18457ff348ecf", size = 30894, upload-time = "2026-04-25T11:08:09.429Z" }, + { url = "https://files.pythonhosted.org/packages/61/a2/074654d0b893606541199993c7db70067d9fc63b748e0d60020a52a1bd36/xxhash-3.7.0-cp314-cp314-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:565df64437a9390f84465dcca33e7377114c7ede8d05cd2cf20081f831ea788e", size = 194409, upload-time = "2026-04-25T11:08:10.91Z" }, + { url = "https://files.pythonhosted.org/packages/e2/26/6d2a1afc468189f77ca28c32e1c83e1b9da1178231e05641dbc1b350e332/xxhash-3.7.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:12eca820a5d558633d423bf8bb78ce72a55394823f64089247f788a7e0ae691e", size = 213135, upload-time = "2026-04-25T11:08:12.575Z" }, + { url = "https://files.pythonhosted.org/packages/8e/0e/d8aecf95e09c42547453137be74d2f7b8b14e08f5177fa2fab6144a19061/xxhash-3.7.0-cp314-cp314-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:f262b8f7599516567e070abf607b9af649052b2c4bd6f9be02b0cb41b7024805", size = 236379, upload-time = "2026-04-25T11:08:14.206Z" }, + { url = "https://files.pythonhosted.org/packages/f2/74/8140e8210536b3dd0cc816c4faaeb5ba6e63e8125ab25af4bcddd6a037b3/xxhash-3.7.0-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:f1598916cb197681e03e601901e4ab96a9a963de398c59d0964f8a6f44a2b361", size = 212447, upload-time = "2026-04-25T11:08:15.79Z" }, + { url = "https://files.pythonhosted.org/packages/a0/d2/462001d2903b4bee5a5689598a0a55e5e7cd1ac7f4247a5545cff10d3ebb/xxhash-3.7.0-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:322b2f0622230f526aeb1738149948a7ae357a9e2ceb1383c6fd1fdaecdafa16", size = 445660, upload-time = "2026-04-25T11:08:17.441Z" }, + { url = "https://files.pythonhosted.org/packages/23/09/2bd1ed7f8689b20e51727952cac8329d50c694dc32b2eba06ba5bc742b37/xxhash-3.7.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:24cc22070880cc57b830a65cde4e65fa884c6d9b28ae4803b5ee05911e7bafba", size = 194076, upload-time = "2026-04-25T11:08:19.134Z" }, + { url = "https://files.pythonhosted.org/packages/c9/6e/692302cd0a5f4ac4e6289f37fa888dc2e1e07750b68fe3e4bfe939b8cea3/xxhash-3.7.0-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:cb5a888a968b2434abf9ecda357b5d43f10d7b5a6da6fdbbe036208473aff0e2", size = 284990, upload-time = "2026-04-25T11:08:20.618Z" }, + { url = "https://files.pythonhosted.org/packages/05/d9/e54b159b3d9df7999d2a7c676ce7b323d1b5588a64f8f51ed8172567bd87/xxhash-3.7.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:a999771ff97bec27d18341be4f3a36b163bb1ac41ec17bef6d2dabd84acd33c7", size = 210590, upload-time = "2026-04-25T11:08:22.24Z" }, + { url = "https://files.pythonhosted.org/packages/50/93/0e0df1a3a196ced4ca71de76d65ead25d8e87bbfb87b64306ea47a40c00d/xxhash-3.7.0-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:ed4a6efe2dee1655adb73e7ad40c6aa955a6892422b1e3b95de6a34de56e3cbb", size = 241442, upload-time = "2026-04-25T11:08:23.844Z" }, + { url = "https://files.pythonhosted.org/packages/9a/a9/d917a7a814e90b218f8a0d37967105eea91bf752c3303683c99a1f7bfc1f/xxhash-3.7.0-cp314-cp314-musllinux_1_2_i686.whl", hash = "sha256:9fd17f14ac0faa12126c2f9ca774a8cf342957265ec3c8669c144e5e6cdb478c", size = 198356, upload-time = "2026-04-25T11:08:25.99Z" }, + { url = "https://files.pythonhosted.org/packages/89/5e/f2ba1877c39469abbefc72991d6ebdcbd4c0880db01ae8cb1f553b0c537d/xxhash-3.7.0-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:05fd1254268c59b5cb2a029dfc204275e9fc52de2913f1e53aa8d01442c96b4d", size = 210898, upload-time = "2026-04-25T11:08:27.608Z" }, + { url = "https://files.pythonhosted.org/packages/90/c6/be56b58e73de531f39a10de1355bb77ceb663900dc4bf2d6d3002a9c3f9e/xxhash-3.7.0-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:a2eae53197c6276d5b317f75a1be226bbf440c20b58bf525f36b5d0e1f657ca6", size = 275519, upload-time = "2026-04-25T11:08:29.301Z" }, + { url = "https://files.pythonhosted.org/packages/92/e2/17ddc85d5765b9c709f192009ed8f5a1fc876f4eb35bba7c307b5b1169f9/xxhash-3.7.0-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:bfe6f92e3522dcbe8c4281efd74fa7542a336cb00b0e3272c4ec0edabeaeaf67", size = 414191, upload-time = "2026-04-25T11:08:31.16Z" }, + { url = "https://files.pythonhosted.org/packages/9c/42/85f5b79f4bf1ec7ba052491164adfd4f4e9519f5dc7246de4fbd64a1bd56/xxhash-3.7.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:7ab9a49c410d8c6c786ab99e79c529938d894c01433130353dd0fe999111077a", size = 191604, upload-time = "2026-04-25T11:08:32.862Z" }, + { url = "https://files.pythonhosted.org/packages/b8/d0/6127b623aa4cca18d8b7743592b048d689fd6c6e37ff26a22cddf6cd9d7f/xxhash-3.7.0-cp314-cp314-win32.whl", hash = "sha256:040ea63668f9185b92bc74942df09c7e65703deed71431333678fc6e739a9955", size = 31271, upload-time = "2026-04-25T11:08:34.651Z" }, + { url = "https://files.pythonhosted.org/packages/64/4f/44fc4788568004c43921701cbc127f48218a1eede2c9aea231115323564d/xxhash-3.7.0-cp314-cp314-win_amd64.whl", hash = "sha256:2a61e2a3fb23c892496d587b470dee7fa1b58b248a187719c65ea8e94ec13257", size = 32284, upload-time = "2026-04-25T11:08:35.987Z" }, + { url = "https://files.pythonhosted.org/packages/6d/77/18bb895eb60a49453d16e17d67990e5caff557c78eafc90ad4e2eabf4570/xxhash-3.7.0-cp314-cp314-win_arm64.whl", hash = "sha256:c7741c7524961d8c0cb4d4c21b28957ff731a3fd5b5cd8b856dc80a40e9e5acc", size = 28701, upload-time = "2026-04-25T11:08:37.767Z" }, + { url = "https://files.pythonhosted.org/packages/45/a0/46f72244570c550fbbb7db1ef554183dd5ebe9136385f30e032b781ae8f6/xxhash-3.7.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:fc84bf7aa7592f31ec63a3e7b11d624f468a3f19f5238cec7282a42e838ab1d7", size = 33646, upload-time = "2026-04-25T11:08:39.109Z" }, + { url = "https://files.pythonhosted.org/packages/4a/3a/453846a7eceea11e75def361eed01ec6a0205b9822c19927ed364ccae7cc/xxhash-3.7.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:9f1563fdc8abfc389748e6932c7e4e99c89a53e4ec37d4563c24fc06f5e5644b", size = 31125, upload-time = "2026-04-25T11:08:40.467Z" }, + { url = "https://files.pythonhosted.org/packages/bd/3e/49434aba738885d512f9e486db1bdd19db28dfa40372b56da26ef7a4e738/xxhash-3.7.0-cp314-cp314t-manylinux1_i686.manylinux_2_28_i686.manylinux_2_5_i686.whl", hash = "sha256:2d415f18becf6f153046ab6adc97da77e3643a0ee205dae61c4012604113a020", size = 196633, upload-time = "2026-04-25T11:08:41.943Z" }, + { url = "https://files.pythonhosted.org/packages/a4/e9/006cb6127baeb9f8abe6d15e62faa01349f09b34e2bfd65175b2422d026b/xxhash-3.7.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:bb16aa13ed175bc9be5c2491ba031b85a9b51c4ed90e0b3d4ebe63cf3fb54f8e", size = 215899, upload-time = "2026-04-25T11:08:43.645Z" }, + { url = "https://files.pythonhosted.org/packages/27/e4/cc57d72e66df0ae29b914335f1c6dcf61e8f3746ddf0ae3c471aa4f15e00/xxhash-3.7.0-cp314-cp314t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:f9fd595f1e5941b3d7863e4774e4b30caa6731fc34b9277da032295aa5656ee5", size = 238116, upload-time = "2026-04-25T11:08:45.698Z" }, + { url = "https://files.pythonhosted.org/packages/af/78/3531d4a3fd8a0038cc6be1f265a69c1b3587f557a10b677dd736de2202c1/xxhash-3.7.0-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:1295325c5a98d552333fa53dc2b026b0ef0ec9c8e73ca3a952990b4c7d65d459", size = 215012, upload-time = "2026-04-25T11:08:47.355Z" }, + { url = "https://files.pythonhosted.org/packages/b4/f6/259fb1eaaec921f59b17203b0daee69829761226d3b980d5191d7723dd83/xxhash-3.7.0-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:3573a651d146912da9daa9e29e5fbc45994420daaa9ef1e2fa5823e1dc485513", size = 448534, upload-time = "2026-04-25T11:08:49.149Z" }, + { url = "https://files.pythonhosted.org/packages/7b/16/a66d0eaf6a7e68532c07714361ddc904c663ec940f3b028c1ae4a21a7b9d/xxhash-3.7.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5ec1e080a3d02d94ea9335bfab0e3374b877e25411422c18f51a943fa4b46381", size = 196217, upload-time = "2026-04-25T11:08:50.805Z" }, + { url = "https://files.pythonhosted.org/packages/8d/ef/d2efc7fc51756dc52509109d1a25cefc859d74bc4b19a167b12dbd8c2786/xxhash-3.7.0-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:84415265192072d8638a3afc3c1bc5995e310570cd9acb54dc46d3939e364fe0", size = 286906, upload-time = "2026-04-25T11:08:52.418Z" }, + { url = "https://files.pythonhosted.org/packages/fc/67/25decd1d4a4018582ec4db2a868a2b7e40640f4adb20dfeb19ac923aa825/xxhash-3.7.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:8d4dea659b57443989ef32f4295104fd6912c73d0bf26d1d148bb88a9f159b02", size = 213057, upload-time = "2026-04-25T11:08:54.105Z" }, + { url = "https://files.pythonhosted.org/packages/0d/5d/17651eb29d06786cdc40c60ae3d27d645aa5d61d2eca6237a7ba0b94789b/xxhash-3.7.0-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:05ece0fe4d9c9c2728912d1981ae1566cfc83a011571b24732cbf76e1fb70dca", size = 243886, upload-time = "2026-04-25T11:08:56.109Z" }, + { url = "https://files.pythonhosted.org/packages/8a/d4/174d9cf7502243d586e6a9ae842b1ae23026620995114f85f1380e588bc9/xxhash-3.7.0-cp314-cp314t-musllinux_1_2_i686.whl", hash = "sha256:fd880353cf1ffaf321bc18dd663e111976dbd0d3bbd8a66d58d2b470dfa7f396", size = 201015, upload-time = "2026-04-25T11:08:57.777Z" }, + { url = "https://files.pythonhosted.org/packages/91/8c/2254e2d06c3ac5e6fe22eaf3da791b87ea823ae9f2c17b4af66755c5752d/xxhash-3.7.0-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:4e15cc9e2817f6481160f930c62842b3ff419e20e13072bcbab12230943092bc", size = 213457, upload-time = "2026-04-25T11:08:59.826Z" }, + { url = "https://files.pythonhosted.org/packages/79/a2/e3daa762545921173e3360f3b4ff7fc63c2d27359f7230ec1a7a74e117f6/xxhash-3.7.0-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:90b9d1a8bd37d768ffc92a1f651ec69afc532a96fa1ac2ea7abbed5d630b3237", size = 277738, upload-time = "2026-04-25T11:09:01.423Z" }, + { url = "https://files.pythonhosted.org/packages/e1/4c/e186da2c46b87f5204640e008d42730bf3c1ee9f0efb71ae1ebcdfeac681/xxhash-3.7.0-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:157c49475b34ecea8809e51123d9769a534e139d1247942f7a4bc67710bb2533", size = 417127, upload-time = "2026-04-25T11:09:03.592Z" }, + { url = "https://files.pythonhosted.org/packages/17/28/3798e15007a3712d0da3d3fe70f8e11916569858b5cc371053bc26270832/xxhash-3.7.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:5a6ddec83325685e729ca119d1f5c518ec39294212ecd770e60693cdc5f7eb79", size = 193962, upload-time = "2026-04-25T11:09:06.228Z" }, + { url = "https://files.pythonhosted.org/packages/ad/95/a26baa93b5241fd7630998816a4ec47a5a0bad193b3f8fc8f3593e1a4a67/xxhash-3.7.0-cp314-cp314t-win32.whl", hash = "sha256:a04a6cab47e2166435aaf5b9e5ee41d1532cc8300efdef87f2a4d0acb7db19ed", size = 31643, upload-time = "2026-04-25T11:09:08.153Z" }, + { url = "https://files.pythonhosted.org/packages/44/36/5454f13c447e395f9b06a3e91274c59f503d31fad84e1836efe3bdb71f6a/xxhash-3.7.0-cp314-cp314t-win_amd64.whl", hash = "sha256:8653dd7c2eda020545bb2c71c7f7039b53fe7434d0fc1a0a9deb79ab3f1a4fc1", size = 32522, upload-time = "2026-04-25T11:09:09.534Z" }, + { url = "https://files.pythonhosted.org/packages/74/35/698e7e3ff38e22992ea24870a511d8762474fb6783627a2910ff22a185c2/xxhash-3.7.0-cp314-cp314t-win_arm64.whl", hash = "sha256:468f0fc114faaa4b36699f8e328bbc3bb11dc418ba94ac52c26dd736d4b6c637", size = 28807, upload-time = "2026-04-25T11:09:11.234Z" }, +] + +[[package]] +name = "yarl" +version = "1.23.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "idna" }, + { name = "multidict" }, + { name = "propcache" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/23/6e/beb1beec874a72f23815c1434518bfc4ed2175065173fb138c3705f658d4/yarl-1.23.0.tar.gz", hash = "sha256:53b1ea6ca88ebd4420379c330aea57e258408dd0df9af0992e5de2078dc9f5d5", size = 194676, upload-time = "2026-03-01T22:07:53.373Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/9a/4b/a0a6e5d0ee8a2f3a373ddef8a4097d74ac901ac363eea1440464ccbe0898/yarl-1.23.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:16c6994ac35c3e74fb0ae93323bf8b9c2a9088d55946109489667c510a7d010e", size = 123796, upload-time = "2026-03-01T22:05:41.412Z" }, + { url = "https://files.pythonhosted.org/packages/67/b6/8925d68af039b835ae876db5838e82e76ec87b9782ecc97e192b809c4831/yarl-1.23.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:4a42e651629dafb64fd5b0286a3580613702b5809ad3f24934ea87595804f2c5", size = 86547, upload-time = "2026-03-01T22:05:42.841Z" }, + { url = "https://files.pythonhosted.org/packages/ae/50/06d511cc4b8e0360d3c94af051a768e84b755c5eb031b12adaaab6dec6e5/yarl-1.23.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:7c6b9461a2a8b47c65eef63bb1c76a4f1c119618ffa99ea79bc5bb1e46c5821b", size = 85854, upload-time = "2026-03-01T22:05:44.85Z" }, + { url = "https://files.pythonhosted.org/packages/c4/f4/4e30b250927ffdab4db70da08b9b8d2194d7c7b400167b8fbeca1e4701ca/yarl-1.23.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2569b67d616eab450d262ca7cb9f9e19d2f718c70a8b88712859359d0ab17035", size = 98351, upload-time = "2026-03-01T22:05:46.836Z" }, + { url = "https://files.pythonhosted.org/packages/86/fc/4118c5671ea948208bdb1492d8b76bdf1453d3e73df051f939f563e7dcc5/yarl-1.23.0-cp313-cp313-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:e9d9a4d06d3481eab79803beb4d9bd6f6a8e781ec078ac70d7ef2dcc29d1bea5", size = 92711, upload-time = "2026-03-01T22:05:48.316Z" }, + { url = "https://files.pythonhosted.org/packages/56/11/1ed91d42bd9e73c13dc9e7eb0dd92298d75e7ac4dd7f046ad0c472e231cd/yarl-1.23.0-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:f514f6474e04179d3d33175ed3f3e31434d3130d42ec153540d5b157deefd735", size = 106014, upload-time = "2026-03-01T22:05:50.028Z" }, + { url = "https://files.pythonhosted.org/packages/ce/c9/74e44e056a23fbc33aca71779ef450ca648a5bc472bdad7a82339918f818/yarl-1.23.0-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:fda207c815b253e34f7e1909840fd14299567b1c0eb4908f8c2ce01a41265401", size = 105557, upload-time = "2026-03-01T22:05:51.416Z" }, + { url = "https://files.pythonhosted.org/packages/66/fe/b1e10b08d287f518994f1e2ff9b6d26f0adeecd8dd7d533b01bab29a3eda/yarl-1.23.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:34b6cf500e61c90f305094911f9acc9c86da1a05a7a3f5be9f68817043f486e4", size = 101559, upload-time = "2026-03-01T22:05:52.872Z" }, + { url = "https://files.pythonhosted.org/packages/72/59/c5b8d94b14e3d3c2a9c20cb100119fd534ab5a14b93673ab4cc4a4141ea5/yarl-1.23.0-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:d7504f2b476d21653e4d143f44a175f7f751cd41233525312696c76aa3dbb23f", size = 100502, upload-time = "2026-03-01T22:05:54.954Z" }, + { url = "https://files.pythonhosted.org/packages/77/4f/96976cb54cbfc5c9fd73ed4c51804f92f209481d1fb190981c0f8a07a1d7/yarl-1.23.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:578110dd426f0d209d1509244e6d4a3f1a3e9077655d98c5f22583d63252a08a", size = 98027, upload-time = "2026-03-01T22:05:56.409Z" }, + { url = "https://files.pythonhosted.org/packages/63/6e/904c4f476471afdbad6b7e5b70362fb5810e35cd7466529a97322b6f5556/yarl-1.23.0-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:609d3614d78d74ebe35f54953c5bbd2ac647a7ddb9c30a5d877580f5e86b22f2", size = 95369, upload-time = "2026-03-01T22:05:58.141Z" }, + { url = "https://files.pythonhosted.org/packages/9d/40/acfcdb3b5f9d68ef499e39e04d25e141fe90661f9d54114556cf83be8353/yarl-1.23.0-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:4966242ec68afc74c122f8459abd597afd7d8a60dc93d695c1334c5fd25f762f", size = 105565, upload-time = "2026-03-01T22:06:00.286Z" }, + { url = "https://files.pythonhosted.org/packages/5e/c6/31e28f3a6ba2869c43d124f37ea5260cac9c9281df803c354b31f4dd1f3c/yarl-1.23.0-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:e0fd068364a6759bc794459f0a735ab151d11304346332489c7972bacbe9e72b", size = 99813, upload-time = "2026-03-01T22:06:01.712Z" }, + { url = "https://files.pythonhosted.org/packages/08/1f/6f65f59e72d54aa467119b63fc0b0b1762eff0232db1f4720cd89e2f4a17/yarl-1.23.0-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:39004f0ad156da43e86aa71f44e033de68a44e5a31fc53507b36dd253970054a", size = 105632, upload-time = "2026-03-01T22:06:03.188Z" }, + { url = "https://files.pythonhosted.org/packages/a3/c4/18b178a69935f9e7a338127d5b77d868fdc0f0e49becd286d51b3a18c61d/yarl-1.23.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:e5723c01a56c5028c807c701aa66722916d2747ad737a046853f6c46f4875543", size = 101895, upload-time = "2026-03-01T22:06:04.651Z" }, + { url = "https://files.pythonhosted.org/packages/8f/54/f5b870b5505663911dba950a8e4776a0dbd51c9c54c0ae88e823e4b874a0/yarl-1.23.0-cp313-cp313-win32.whl", hash = "sha256:1b6b572edd95b4fa8df75de10b04bc81acc87c1c7d16bcdd2035b09d30acc957", size = 82356, upload-time = "2026-03-01T22:06:06.04Z" }, + { url = "https://files.pythonhosted.org/packages/7a/84/266e8da36879c6edcd37b02b547e2d9ecdfea776be49598e75696e3316e1/yarl-1.23.0-cp313-cp313-win_amd64.whl", hash = "sha256:baaf55442359053c7d62f6f8413a62adba3205119bcb6f49594894d8be47e5e3", size = 87515, upload-time = "2026-03-01T22:06:08.107Z" }, + { url = "https://files.pythonhosted.org/packages/00/fd/7e1c66efad35e1649114fa13f17485f62881ad58edeeb7f49f8c5e748bf9/yarl-1.23.0-cp313-cp313-win_arm64.whl", hash = "sha256:fb4948814a2a98e3912505f09c9e7493b1506226afb1f881825368d6fb776ee3", size = 81785, upload-time = "2026-03-01T22:06:10.181Z" }, + { url = "https://files.pythonhosted.org/packages/9c/fc/119dd07004f17ea43bb91e3ece6587759edd7519d6b086d16bfbd3319982/yarl-1.23.0-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:aecfed0b41aa72b7881712c65cf764e39ce2ec352324f5e0837c7048d9e6daaa", size = 130719, upload-time = "2026-03-01T22:06:11.708Z" }, + { url = "https://files.pythonhosted.org/packages/e6/0d/9f2348502fbb3af409e8f47730282cd6bc80dec6630c1e06374d882d6eb2/yarl-1.23.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:a41bcf68efd19073376eb8cf948b8d9be0af26256403e512bb18f3966f1f9120", size = 89690, upload-time = "2026-03-01T22:06:13.429Z" }, + { url = "https://files.pythonhosted.org/packages/50/93/e88f3c80971b42cfc83f50a51b9d165a1dbf154b97005f2994a79f212a07/yarl-1.23.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:cde9a2ecd91668bcb7f077c4966d8ceddb60af01b52e6e3e2680e4cf00ad1a59", size = 89851, upload-time = "2026-03-01T22:06:15.53Z" }, + { url = "https://files.pythonhosted.org/packages/1c/07/61c9dd8ba8f86473263b4036f70fb594c09e99c0d9737a799dfd8bc85651/yarl-1.23.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5023346c4ee7992febc0068e7593de5fa2bf611848c08404b35ebbb76b1b0512", size = 95874, upload-time = "2026-03-01T22:06:17.553Z" }, + { url = "https://files.pythonhosted.org/packages/9e/e9/f9ff8ceefba599eac6abddcfb0b3bee9b9e636e96dbf54342a8577252379/yarl-1.23.0-cp313-cp313t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:d1009abedb49ae95b136a8904a3f71b342f849ffeced2d3747bf29caeda218c4", size = 88710, upload-time = "2026-03-01T22:06:19.004Z" }, + { url = "https://files.pythonhosted.org/packages/eb/78/0231bfcc5d4c8eec220bc2f9ef82cb4566192ea867a7c5b4148f44f6cbcd/yarl-1.23.0-cp313-cp313t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a8d00f29b42f534cc8aa3931cfe773b13b23e561e10d2b26f27a8d309b0e82a1", size = 101033, upload-time = "2026-03-01T22:06:21.203Z" }, + { url = "https://files.pythonhosted.org/packages/cd/9b/30ea5239a61786f18fd25797151a17fbb3be176977187a48d541b5447dd4/yarl-1.23.0-cp313-cp313t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:95451e6ce06c3e104556d73b559f5da6c34a069b6b62946d3ad66afcd51642ea", size = 100817, upload-time = "2026-03-01T22:06:22.738Z" }, + { url = "https://files.pythonhosted.org/packages/62/e2/a4980481071791bc83bce2b7a1a1f7adcabfa366007518b4b845e92eeee3/yarl-1.23.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:531ef597132086b6cf96faa7c6c1dcd0361dd5f1694e5cc30375907b9b7d3ea9", size = 97482, upload-time = "2026-03-01T22:06:24.21Z" }, + { url = "https://files.pythonhosted.org/packages/e5/1e/304a00cf5f6100414c4b5a01fc7ff9ee724b62158a08df2f8170dfc72a2d/yarl-1.23.0-cp313-cp313t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:88f9fb0116fbfcefcab70f85cf4b74a2b6ce5d199c41345296f49d974ddb4123", size = 95949, upload-time = "2026-03-01T22:06:25.697Z" }, + { url = "https://files.pythonhosted.org/packages/68/03/093f4055ed4cae649ac53bca3d180bd37102e9e11d048588e9ab0c0108d0/yarl-1.23.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:e7b0460976dc75cb87ad9cc1f9899a4b97751e7d4e77ab840fc9b6d377b8fd24", size = 95839, upload-time = "2026-03-01T22:06:27.309Z" }, + { url = "https://files.pythonhosted.org/packages/b9/28/4c75ebb108f322aa8f917ae10a8ffa4f07cae10a8a627b64e578617df6a0/yarl-1.23.0-cp313-cp313t-musllinux_1_2_armv7l.whl", hash = "sha256:115136c4a426f9da976187d238e84139ff6b51a20839aa6e3720cd1026d768de", size = 90696, upload-time = "2026-03-01T22:06:29.048Z" }, + { url = "https://files.pythonhosted.org/packages/23/9c/42c2e2dd91c1a570402f51bdf066bfdb1241c2240ba001967bad778e77b7/yarl-1.23.0-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:ead11956716a940c1abc816b7df3fa2b84d06eaed8832ca32f5c5e058c65506b", size = 100865, upload-time = "2026-03-01T22:06:30.525Z" }, + { url = "https://files.pythonhosted.org/packages/74/05/1bcd60a8a0a914d462c305137246b6f9d167628d73568505fce3f1cb2e65/yarl-1.23.0-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:fe8f8f5e70e6dbdfca9882cd9deaac058729bcf323cf7a58660901e55c9c94f6", size = 96234, upload-time = "2026-03-01T22:06:32.692Z" }, + { url = "https://files.pythonhosted.org/packages/90/b2/f52381aac396d6778ce516b7bc149c79e65bfc068b5de2857ab69eeea3b7/yarl-1.23.0-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:a0e317df055958a0c1e79e5d2aa5a5eaa4a6d05a20d4b0c9c3f48918139c9fc6", size = 100295, upload-time = "2026-03-01T22:06:34.268Z" }, + { url = "https://files.pythonhosted.org/packages/e5/e8/638bae5bbf1113a659b2435d8895474598afe38b4a837103764f603aba56/yarl-1.23.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:6f0fd84de0c957b2d280143522c4f91a73aada1923caee763e24a2b3fda9f8a5", size = 97784, upload-time = "2026-03-01T22:06:35.864Z" }, + { url = "https://files.pythonhosted.org/packages/80/25/a3892b46182c586c202629fc2159aa13975d3741d52ebd7347fd501d48d5/yarl-1.23.0-cp313-cp313t-win32.whl", hash = "sha256:93a784271881035ab4406a172edb0faecb6e7d00f4b53dc2f55919d6c9688595", size = 88313, upload-time = "2026-03-01T22:06:37.39Z" }, + { url = "https://files.pythonhosted.org/packages/43/68/8c5b36aa5178900b37387937bc2c2fe0e9505537f713495472dcf6f6fccc/yarl-1.23.0-cp313-cp313t-win_amd64.whl", hash = "sha256:dd00607bffbf30250fe108065f07453ec124dbf223420f57f5e749b04295e090", size = 94932, upload-time = "2026-03-01T22:06:39.579Z" }, + { url = "https://files.pythonhosted.org/packages/c6/cc/d79ba8292f51f81f4dc533a8ccfb9fc6992cabf0998ed3245de7589dc07c/yarl-1.23.0-cp313-cp313t-win_arm64.whl", hash = "sha256:ac09d42f48f80c9ee1635b2fcaa819496a44502737660d3c0f2ade7526d29144", size = 84786, upload-time = "2026-03-01T22:06:41.988Z" }, + { url = "https://files.pythonhosted.org/packages/90/98/b85a038d65d1b92c3903ab89444f48d3cee490a883477b716d7a24b1a78c/yarl-1.23.0-cp314-cp314-macosx_10_15_universal2.whl", hash = "sha256:21d1b7305a71a15b4794b5ff22e8eef96ff4a6d7f9657155e5aa419444b28912", size = 124455, upload-time = "2026-03-01T22:06:43.615Z" }, + { url = "https://files.pythonhosted.org/packages/39/54/bc2b45559f86543d163b6e294417a107bb87557609007c007ad889afec18/yarl-1.23.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:85610b4f27f69984932a7abbe52703688de3724d9f72bceb1cca667deff27474", size = 86752, upload-time = "2026-03-01T22:06:45.425Z" }, + { url = "https://files.pythonhosted.org/packages/24/f9/e8242b68362bffe6fb536c8db5076861466fc780f0f1b479fc4ffbebb128/yarl-1.23.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:23f371bd662cf44a7630d4d113101eafc0cfa7518a2760d20760b26021454719", size = 86291, upload-time = "2026-03-01T22:06:46.974Z" }, + { url = "https://files.pythonhosted.org/packages/ea/d8/d1cb2378c81dd729e98c716582b1ccb08357e8488e4c24714658cc6630e8/yarl-1.23.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c4a80f77dc1acaaa61f0934176fccca7096d9b1ff08c8ba9cddf5ae034a24319", size = 99026, upload-time = "2026-03-01T22:06:48.459Z" }, + { url = "https://files.pythonhosted.org/packages/0a/ff/7196790538f31debe3341283b5b0707e7feb947620fc5e8236ef28d44f72/yarl-1.23.0-cp314-cp314-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:bd654fad46d8d9e823afbb4f87c79160b5a374ed1ff5bde24e542e6ba8f41434", size = 92355, upload-time = "2026-03-01T22:06:50.306Z" }, + { url = "https://files.pythonhosted.org/packages/c1/56/25d58c3eddde825890a5fe6aa1866228377354a3c39262235234ab5f616b/yarl-1.23.0-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:682bae25f0a0dd23a056739f23a134db9f52a63e2afd6bfb37ddc76292bbd723", size = 106417, upload-time = "2026-03-01T22:06:52.1Z" }, + { url = "https://files.pythonhosted.org/packages/51/8a/882c0e7bc8277eb895b31bce0138f51a1ba551fc2e1ec6753ffc1e7c1377/yarl-1.23.0-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a82836cab5f197a0514235aaf7ffccdc886ccdaa2324bc0aafdd4ae898103039", size = 106422, upload-time = "2026-03-01T22:06:54.424Z" }, + { url = "https://files.pythonhosted.org/packages/42/2b/fef67d616931055bf3d6764885990a3ac647d68734a2d6a9e1d13de437a2/yarl-1.23.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1c57676bdedc94cd3bc37724cf6f8cd2779f02f6aba48de45feca073e714fe52", size = 101915, upload-time = "2026-03-01T22:06:55.895Z" }, + { url = "https://files.pythonhosted.org/packages/18/6a/530e16aebce27c5937920f3431c628a29a4b6b430fab3fd1c117b26ff3f6/yarl-1.23.0-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:c7f8dc16c498ff06497c015642333219871effba93e4a2e8604a06264aca5c5c", size = 100690, upload-time = "2026-03-01T22:06:58.21Z" }, + { url = "https://files.pythonhosted.org/packages/88/08/93749219179a45e27b036e03260fda05190b911de8e18225c294ac95bbc9/yarl-1.23.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:5ee586fb17ff8f90c91cf73c6108a434b02d69925f44f5f8e0d7f2f260607eae", size = 98750, upload-time = "2026-03-01T22:06:59.794Z" }, + { url = "https://files.pythonhosted.org/packages/d9/cf/ea424a004969f5d81a362110a6ac1496d79efdc6d50c2c4b2e3ea0fc2519/yarl-1.23.0-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:17235362f580149742739cc3828b80e24029d08cbb9c4bda0242c7b5bc610a8e", size = 94685, upload-time = "2026-03-01T22:07:01.375Z" }, + { url = "https://files.pythonhosted.org/packages/e2/b7/14341481fe568e2b0408bcf1484c652accafe06a0ade9387b5d3fd9df446/yarl-1.23.0-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:0793e2bd0cf14234983bbb371591e6bea9e876ddf6896cdcc93450996b0b5c85", size = 106009, upload-time = "2026-03-01T22:07:03.151Z" }, + { url = "https://files.pythonhosted.org/packages/0a/e6/5c744a9b54f4e8007ad35bce96fbc9218338e84812d36f3390cea616881a/yarl-1.23.0-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:3650dc2480f94f7116c364096bc84b1d602f44224ef7d5c7208425915c0475dd", size = 100033, upload-time = "2026-03-01T22:07:04.701Z" }, + { url = "https://files.pythonhosted.org/packages/0c/23/e3bfc188d0b400f025bc49d99793d02c9abe15752138dcc27e4eaf0c4a9e/yarl-1.23.0-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:f40e782d49630ad384db66d4d8b73ff4f1b8955dc12e26b09a3e3af064b3b9d6", size = 106483, upload-time = "2026-03-01T22:07:06.231Z" }, + { url = "https://files.pythonhosted.org/packages/72/42/f0505f949a90b3f8b7a363d6cbdf398f6e6c58946d85c6d3a3bc70595b26/yarl-1.23.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:94f8575fbdf81749008d980c17796097e645574a3b8c28ee313931068dad14fe", size = 102175, upload-time = "2026-03-01T22:07:08.4Z" }, + { url = "https://files.pythonhosted.org/packages/aa/65/b39290f1d892a9dd671d1c722014ca062a9c35d60885d57e5375db0404b5/yarl-1.23.0-cp314-cp314-win32.whl", hash = "sha256:c8aa34a5c864db1087d911a0b902d60d203ea3607d91f615acd3f3108ac32169", size = 83871, upload-time = "2026-03-01T22:07:09.968Z" }, + { url = "https://files.pythonhosted.org/packages/a9/5b/9b92f54c784c26e2a422e55a8d2607ab15b7ea3349e28359282f84f01d43/yarl-1.23.0-cp314-cp314-win_amd64.whl", hash = "sha256:63e92247f383c85ab00dd0091e8c3fa331a96e865459f5ee80353c70a4a42d70", size = 89093, upload-time = "2026-03-01T22:07:11.501Z" }, + { url = "https://files.pythonhosted.org/packages/e0/7d/8a84dc9381fd4412d5e7ff04926f9865f6372b4c2fd91e10092e65d29eb8/yarl-1.23.0-cp314-cp314-win_arm64.whl", hash = "sha256:70efd20be968c76ece7baa8dafe04c5be06abc57f754d6f36f3741f7aa7a208e", size = 83384, upload-time = "2026-03-01T22:07:13.069Z" }, + { url = "https://files.pythonhosted.org/packages/dd/8d/d2fad34b1c08aa161b74394183daa7d800141aaaee207317e82c790b418d/yarl-1.23.0-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:9a18d6f9359e45722c064c97464ec883eb0e0366d33eda61cb19a244bf222679", size = 131019, upload-time = "2026-03-01T22:07:14.903Z" }, + { url = "https://files.pythonhosted.org/packages/19/ff/33009a39d3ccf4b94d7d7880dfe17fb5816c5a4fe0096d9b56abceea9ac7/yarl-1.23.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:2803ed8b21ca47a43da80a6fd1ed3019d30061f7061daa35ac54f63933409412", size = 89894, upload-time = "2026-03-01T22:07:17.372Z" }, + { url = "https://files.pythonhosted.org/packages/0c/f1/dab7ac5e7306fb79c0190766a3c00b4cb8d09a1f390ded68c85a5934faf5/yarl-1.23.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:394906945aa8b19fc14a61cf69743a868bb8c465efe85eee687109cc540b98f4", size = 89979, upload-time = "2026-03-01T22:07:19.361Z" }, + { url = "https://files.pythonhosted.org/packages/aa/b1/08e95f3caee1fad6e65017b9f26c1d79877b502622d60e517de01e72f95d/yarl-1.23.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:71d006bee8397a4a89f469b8deb22469fe7508132d3c17fa6ed871e79832691c", size = 95943, upload-time = "2026-03-01T22:07:21.266Z" }, + { url = "https://files.pythonhosted.org/packages/c0/cc/6409f9018864a6aa186c61175b977131f373f1988e198e031236916e87e4/yarl-1.23.0-cp314-cp314t-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:62694e275c93d54f7ccedcfef57d42761b2aad5234b6be1f3e3026cae4001cd4", size = 88786, upload-time = "2026-03-01T22:07:23.129Z" }, + { url = "https://files.pythonhosted.org/packages/76/40/cc22d1d7714b717fde2006fad2ced5efe5580606cb059ae42117542122f3/yarl-1.23.0-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a31de1613658308efdb21ada98cbc86a97c181aa050ba22a808120bb5be3ab94", size = 101307, upload-time = "2026-03-01T22:07:24.689Z" }, + { url = "https://files.pythonhosted.org/packages/8f/0d/476c38e85ddb4c6ec6b20b815bdd779aa386a013f3d8b85516feee55c8dc/yarl-1.23.0-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:fb1e8b8d66c278b21d13b0a7ca22c41dd757a7c209c6b12c313e445c31dd3b28", size = 100904, upload-time = "2026-03-01T22:07:26.287Z" }, + { url = "https://files.pythonhosted.org/packages/72/32/0abe4a76d59adf2081dcb0397168553ece4616ada1c54d1c49d8936c74f8/yarl-1.23.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:50f9d8d531dfb767c565f348f33dd5139a6c43f5cbdf3f67da40d54241df93f6", size = 97728, upload-time = "2026-03-01T22:07:27.906Z" }, + { url = "https://files.pythonhosted.org/packages/b7/35/7b30f4810fba112f60f5a43237545867504e15b1c7647a785fbaf588fac2/yarl-1.23.0-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:575aa4405a656e61a540f4a80eaa5260f2a38fff7bfdc4b5f611840d76e9e277", size = 95964, upload-time = "2026-03-01T22:07:30.198Z" }, + { url = "https://files.pythonhosted.org/packages/2d/86/ed7a73ab85ef00e8bb70b0cb5421d8a2a625b81a333941a469a6f4022828/yarl-1.23.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:041b1a4cefacf65840b4e295c6985f334ba83c30607441ae3cf206a0eed1a2e4", size = 95882, upload-time = "2026-03-01T22:07:32.132Z" }, + { url = "https://files.pythonhosted.org/packages/19/90/d56967f61a29d8498efb7afb651e0b2b422a1e9b47b0ab5f4e40a19b699b/yarl-1.23.0-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:d38c1e8231722c4ce40d7593f28d92b5fc72f3e9774fe73d7e800ec32299f63a", size = 90797, upload-time = "2026-03-01T22:07:34.404Z" }, + { url = "https://files.pythonhosted.org/packages/72/00/8b8f76909259f56647adb1011d7ed8b321bcf97e464515c65016a47ecdf0/yarl-1.23.0-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:d53834e23c015ee83a99377db6e5e37d8484f333edb03bd15b4bc312cc7254fb", size = 101023, upload-time = "2026-03-01T22:07:35.953Z" }, + { url = "https://files.pythonhosted.org/packages/ac/e2/cab11b126fb7d440281b7df8e9ddbe4851e70a4dde47a202b6642586b8d9/yarl-1.23.0-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:2e27c8841126e017dd2a054a95771569e6070b9ee1b133366d8b31beb5018a41", size = 96227, upload-time = "2026-03-01T22:07:37.594Z" }, + { url = "https://files.pythonhosted.org/packages/c2/9b/2c893e16bfc50e6b2edf76c1a9eb6cb0c744346197e74c65e99ad8d634d0/yarl-1.23.0-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:76855800ac56f878847a09ce6dba727c93ca2d89c9e9d63002d26b916810b0a2", size = 100302, upload-time = "2026-03-01T22:07:39.334Z" }, + { url = "https://files.pythonhosted.org/packages/28/ec/5498c4e3a6d5f1003beb23405671c2eb9cdbf3067d1c80f15eeafe301010/yarl-1.23.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:e09fd068c2e169a7070d83d3bde728a4d48de0549f975290be3c108c02e499b4", size = 98202, upload-time = "2026-03-01T22:07:41.717Z" }, + { url = "https://files.pythonhosted.org/packages/fe/c3/cd737e2d45e70717907f83e146f6949f20cc23cd4bf7b2688727763aa458/yarl-1.23.0-cp314-cp314t-win32.whl", hash = "sha256:73309162a6a571d4cbd3b6a1dcc703c7311843ae0d1578df6f09be4e98df38d4", size = 90558, upload-time = "2026-03-01T22:07:43.433Z" }, + { url = "https://files.pythonhosted.org/packages/e1/19/3774d162f6732d1cfb0b47b4140a942a35ca82bb19b6db1f80e9e7bdc8f8/yarl-1.23.0-cp314-cp314t-win_amd64.whl", hash = "sha256:4503053d296bc6e4cbd1fad61cf3b6e33b939886c4f249ba7c78b602214fabe2", size = 97610, upload-time = "2026-03-01T22:07:45.773Z" }, + { url = "https://files.pythonhosted.org/packages/51/47/3fa2286c3cb162c71cdb34c4224d5745a1ceceb391b2bd9b19b668a8d724/yarl-1.23.0-cp314-cp314t-win_arm64.whl", hash = "sha256:44bb7bef4ea409384e3f8bc36c063d77ea1b8d4a5b2706956c0d6695f07dcc25", size = 86041, upload-time = "2026-03-01T22:07:49.026Z" }, + { url = "https://files.pythonhosted.org/packages/69/68/c8739671f5699c7dc470580a4f821ef37c32c4cb0b047ce223a7f115757f/yarl-1.23.0-py3-none-any.whl", hash = "sha256:a2df6afe50dea8ae15fa34c9f824a3ee958d785fd5d089063d960bae1daa0a3f", size = 48288, upload-time = "2026-03-01T22:07:51.388Z" }, +] + +[[package]] +name = "zipp" +version = "3.23.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/30/21/093488dfc7cc8964ded15ab726fad40f25fd3d788fd741cc1c5a17d78ee8/zipp-3.23.1.tar.gz", hash = "sha256:32120e378d32cd9714ad503c1d024619063ec28aad2248dc6672ad13edfa5110", size = 25965, upload-time = "2026-04-13T23:21:46.6Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/08/8a/0861bec20485572fbddf3dfba2910e38fe249796cb73ecdeb74e07eeb8d3/zipp-3.23.1-py3-none-any.whl", hash = "sha256:0b3596c50a5c700c9cb40ba8d86d9f2cc4807e9bedb06bcdf7fac85633e444dc", size = 10378, upload-time = "2026-04-13T23:21:45.386Z" }, +]